Memphis MPO Model Update

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3 report Memphis MPO Model Update Model Estimation and Validation Report prepared for Memphis MPO prepared by Cambridge Systematics, Inc. 115 South LaSalle Street, Suite 2200 Chicago, IL date June 16, 2015

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5 Memphis MPO Model Update Table of Contents 1.0 Background Model Structure Changes to Model Framework Model Boundary Expansion Household Segmentation in Travel Demand Model Non-Transportation Modes in the Travel Demand Model Advanced Freight/Truck Model using GPS Data Integration with the Regional Land Use Model Model Framework Trip Generation Module Trip Distribution/Destination Choice Module Mode Choice Module Time-of-Day Module Journey to Work Chain Freight Model External Model Trip Assignment Module Model Estimation Preparation of Socioeconomic Data Census Data to Generate TAZ-level Demographics Data Data Sources Data Preparation Criteria Allocating Data to Model TAZs Consistency Checks Creating Cross-Tabulations to Support Model Application School Enrollment Economic Data from InfoGroup Data Purchase QA/QC Checks Employment Categories Integrating Land Use Forecast Data for Future Year Model Runs Network-Related Data Use of Mississippi 3rd-Bridge Model Cambridge Systematics, Inc. i

6 Table of Contents, continued Key Features of Model Network Use of Network in Model Development Transportation Data Used in Modeling Household Travel Survey Data Transit On-board Survey Data Freight/Truck Survey Data Bicycle/Pedestrian Survey Data Trip Generation Model Internal Person Trip Productions Internal Person Trip Attractions Special Generators Airport Graceland Destination Choice Models Model Framework Model Variables Journey-to-Work Stop Models Stop Generation Stop Destination Choice Mode Choice Models Model Framework Model Estimation and Testing Procedure Observation Exclusions Unavailability of Modes Model Variables Level of service variables Other variables Model Estimation Results Time-of-Day Choice Models Definition of Peak Periods Travel Share by Time Period Convert P-A Trips to O-D Trips Auto Occupancy Factors Truck Model External Truck Models External Truck Trip Generation External Truck Trip Distribution ii Cambridge Systematics, Inc.

7 Memphis MPO Model Update External (Station) Truck Trips and External-External Truck Table Internal Truck Models Internal Truck Trip Generation Internal Truck Trip Distribution Model Validation Data Sources used for Calibration Model Calibration Overview Trip Generation Checks Destination Choice Calibration Model Framework Calibration Process Adjustment of Coefficients Use of K-Factors Calibration Results Mode Choice Calibration Model Framework Calibration Process Calibration Results Highway Assignment Calibration Traffic Count Locations Model vs. Count Location Comparisons TDOT Guideline Comparisons Transit Assignment Calibration Future Year Forecasting Future Year Demand Data Socio-Demographic Data Forecasts External Data Forecasts Future Year Transportation Networks Future Year Model Runs Cambridge Systematics, Inc. iii

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9 Memphis MPO Model Update List of Tables Table 3-1 County Totals by Source Table 3-2 Number of Workers by Source Table 3-3 Crosswalk between NAICS Categories and Model Employment Categories Table 3-4 Use of Different Data in Model Development Table 3-5 Trip Production Model for Journey to Work Chains Table 3-6 Trip Production Model for Home Based School Trips Table 3-7 Trip Production Model for Home Based University Trips Table 3-8 Trip Production Model for Home Based Shop Trips Table 3-9 Trip Production Model for Home Based Pickup/Dropoff Table 3-10 Trip Production Model for Home Based Social-Recreational Table 3-11 Trip Production Model for Home Based Other Trips Table 3-12 Trip Production Model for Non-Home Based Work Trips Table 3-13 Trip Production Model for Non-Home Based Non-Work Trips Table 3-14 Trip Attraction Model Summary Table 3-15 Destination Choice Model for Journey to Work Trips Table 3-16 Destination Choice Model for Home Based University Trips Table 3-17 Destination Choice Model for Home Based School Trips Table 3-18 Destination Choice Model for Home Based Shop Trips Table 3-19 Destination Choice Model for Home Based Pickup/Drop-off Table 3-20 Destination Choice Model for Home Based Social-Recreational Trips Table 3-21 Destination Choice Model for Home Based Other Trips Table 3-22 Destination Choice Model for Non-Home Based Work Trips Table 3-23 Destination Choice Model for Non-Home Based Non-Work Trips Table 3-24 Journey to Work Stops Model Utility Functions Table 3-25 Intermediate Stop Destination Choice Model Estimation Results Cambridge Systematics, Inc. v

10 List of Tables, continued Table 3-26 Distribution by Chosen Mode and Purpose in the Survey Data Set Table 3-27 Mode Choice Model for Journey to Work and Home Based University Trips Table 3-28 Mode Choice Model for Home Based School Trips Table 3-29 Mode Choice Model for Home Based Shop Trips Table 3-30 Mode Choice Model for Home Based Pickup/Dropoff Table 3-31 Mode Choice Model for Home Based Social-Recreational and Home Based Other Trips Table 3-32 Mode Choice Model for Non-Home Based Work Trips Table 3-33 Mode Choice Model for Non-Home Based Non-Work Trips Table 3-34 Time-of-Day Person Trip Factors for Internal Trips Table 3-35 Time-of-Day Directional Trip Factors (Origin = Production) Table 3-36 Commodity Groups for External Truck Models Table 3-37 External Truck Trip Production Rates for Internal Zones Table 3-38 External Trip Attraction Equations for Internal Zones Table 3-39 Internal Truck Production/Attraction Rates Default QRFM vs. ATRI Regression Table 3-40 Internal Truck Production/Attraction Rates Adjusted Rates Trucks per Day per Employee (Household) Table 3-41 Special Generator Locations Table 3-42 Internal Trip Distribution Parameters Table 4-1 Destination Choice Model Sub-Regional Calibration Table Table 4-2 Trips per Household Table 4-3 Percentage Trips by Purpose Compared to TDOT Ranges Table 4-4 Aggregate Trip Rate Benchmark Table 4-5 Percentage Trips by Purpose Compared to Expanded Household Survey by Level Table 4-6 Distance Coefficient Summary Table 4-7 K-factors Table 4-8 Modeled Trips Between Tennessee and Mississippi Compared to Expanded Household Survey Results vi Cambridge Systematics, Inc.

11 Memphis MPO Model Update Table 4-9 Comparison of Average Impedance by Trip Purpose and Table 4-10 Comparison of Percentage Intrazonal by Trip Purpose and Table 4-11 Average Trip Length and Frequencies by Purpose Table 4-12 Percent Intrazonal Trips Table 4-13 Mode Choice Availability by Trip Purpose Table 4-14 Estimated vs. Calibrated Alternative Specific Mode Choice Constants Journey to Work Purpose Table 4-15 Estimated vs. Calibrated Alternative Specific Mode Choice Constants Home-based School Purpose Table 4-16 Estimated vs. Calibrated Alternative Specific Mode Choice Constants Home-based University Purpose Table 4-17 Estimated vs. Calibrated Alternative Specific Mode Choice Constants Home-based Shop Purpose Table 4-18 Estimated vs. Calibrated Alternative Specific Mode Choice Constants Home-based Soc-Rec Purpose Table 4-19 Estimated vs. Calibrated Alternative Specific Mode Choice Constants Home-based Other Purpose Table 4-20 Estimated vs. Calibrated Alternative Specific Mode Choice Constants Home-based Escort Purpose Table 4-21 Estimated vs. Calibrated Alternative Specific Mode Choice Constants Non-Home based Work Purpose Table 4-22 Estimated vs. Calibrated Alternative Specific Mode Choice Constants Non-Home based Non-Work Purpose Table 4-23 Distribution of Traffic Counts by Facility Type Table 4-24 Model vs. Observed Count Data Table 4-25 Model vs. Observed Count Data at Screenlines Table 4-26 Volume-to-Count Ratios and Percent Error Table 4-27 Percent Difference Targets for Daily Volumes Groupings Table 4-28 Percent Error by Volume Group and Roadway Designs Table 4-29 Urban Area VMT by Facility Type Table 4-30 Modeled Versus Observed VMT Table 4-31 Root Mean Square Error (RMSE) By Volume Group Cambridge Systematics, Inc. vii

12 List of Tables, continued Table 4-32 Root Mean Square Error (RMSE) By Functional Class Table 5.1 External Station Growth Rates for Key Stations Table 5-2 Growth in Socio-Demographics by County 2010 vs Table 5-3 Growth in Passenger Trips by Region 2010 vs Table 5-4 Growth in Trip Rates by Category Table 5-5 Change in Average Impedance 2010 vs Table 5-6 Change in Trips by Mode 2010 vs Table 5-7 Growth in VMT and VHT 2010 vs viii Cambridge Systematics, Inc.

13 Memphis MPO Model Update List of Figures Figure 2-1 Memphis MPO Model Boundary Figure 3-1 Model Nesting Structure Figure 3-2 Temporal Distribution of Trips in the 2014 Household Survey Figure 4-1 Detailed Memphis MPO Model Districts Figure 4-2 Journey-to-Work Purpose Destination Choice Calibration Figure 4-3 Home Based School Purpose Destination Choice Calibration Figure 4-4 Home Based University Purpose Destination Choice Calibration Figure 4-5 Home Based Shopping Purpose Destination Choice Calibration Figure 4-6 Home Based Social Recreation Purpose Destination Choice Calibration Figure 4-7 Home Based Escort Purpose Destination Choice Calibration Figure 4-8 Home Based Other Purpose Destination Choice Calibration Figure 4-9 Non Home Based Work Purpose Destination Choice Calibration Figure 4-10 Non Home Based Other Purpose Destination Choice Calibration Figure 4-11 Mode Choice Calibration Sheet for Journey-to-Work Purpose Figure 4-12 Mode Choice Calibration Sheet for Home Based School Purpose Figure 4-13 Mode Choice Calibration Sheet for Home Based University Purpose Figure 4-14 Mode Choice Calibration Sheet for Home Based Shopping Purpose Figure 4-15 Mode Choice Calibration Sheet for Home Based Other Purpose Figure 4-16 Mode Choice Calibration Sheet for Non-Home Based Work Purpose Figure 4-17 Mode Choice Calibration Sheet for Non-Home Based Other Purpose Figure 4-18 Mode Choice Calibration Sheet for Home Based Social- Recreation Purpose Figure 4-19 Mode Choice Calibration Sheet for Home Based Escort Purpose Cambridge Systematics, Inc. ix

14 List of Figures, continued Figure 4-20 Mode Choice Calibration Sheet for All Purposes Figure 4.21 Traffic Count Locations Figure 4-22 Screenlines Figure 5.1 External Station Locations Figure 5-2 Volume to Capacity Ratio during AM period in Year Figure 5-3 Volume to Capacity Ratio during AM period in Year x Cambridge Systematics, Inc.

15 Memphis MPO Model Update 1.0 Background This report documents the development and validation of a new travel demand model for the Memphis region. This work was undertaken for the Memphis Metropolitan Planning Organization (MPO) by a consultant team led by Cambridge Systematics, Inc. (CS). The model development took advantage of a series of new travel surveys that were performed as part of this project and are documented elsewhere. The freight components of the model were developed using new data that were obtained by the Tennessee Department of Transportation (TDOT). The previous travel model for the region was completed in 2007 and was based on data that are now more than 10 years old. Another motivating factor for the model update was the increasing amount of travel between the Memphis urban area and outlying exurban and rural areas. The new model region extends into these areas beyond the boundary of the previous model region. The remainder of this report is organized as follows. The model structure and a summary of model features that have changed from the previous model, including the model boundary expansion, are presented in Chapter 2. Chapter 3 provides a summary of the estimation of parameters for the new model using the newly obtained data. This chapter also provides a summary of the data used in developing and validating the new model and the quality checks performed on the data. Chapter 4 summarizes the validation of the new model. Chapter 5 presents information on the use of the new model for travel forecasting. Cambridge Systematics, Inc. 1-1

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17 Memphis MPO Model Update 2.0 Model Structure The previous Memphis MPO travel demand model was a state-of-the-practice four-step model with a journey to work tour-based component. This model served as a great platform to build upon for the current modeling structure. Therefore, care was taken to keep the model framework to the extent possible. Changes were only made where absolutely necessary. This section of the report outlines the major changes made to the modeling framework and then describes the final components of the model that are generated as a result of the changes to the model framework. 2.1 CHANGES TO MODEL FRAMEWORK There were four key changes to the modeling framework that were implemented based on careful consultation with the MPO staff. These changes include the following: Extend the modeling area for the Memphis MPO model to account for regional growth; Use income as a key segmentation variable in the model to help with environmental justice assessments; Incorporate non-motorized modes of transportation within the mode choice component; Develop a new freight model that uses state of the art GPS-based data to better capture freight data; and Streamline the travel demand model so that it incorporates outputs from the regional land-use model to support future year forecasts. Each of these changes is described in greater detail below: Model Boundary Expansion Expansion of the model boundaries was a key consideration in designing the data collection and model estimation procedures. During the review of the model framework as part of an earlier task, it was determined that recent planning studies shows a need to consider expanding the model to include all the counties in the current Memphis Metropolitan Statistical Area (MSA). Model expansion to West Memphis and Marion, Arkansas, were completed to continue assessing multimodal transportation needs crossing the Mississippi River, including future bridge corridors. Ever expanding commutes into exurban and rural areas of Tipton, Fayette, Marshall, Tate, and Tunica Counties justified the need for including their Cambridge Systematics, Inc. 2-1

18 Memphis MPO Model Update entirety within the model to fully capture daily work trip patterns and truck movements to the highway facilities most likely to be impacted, especially with respect to future segments of I-69. One advantage of including whole, rather than partial, counties in the model is the ability to take advantage of data available at the county level, enabling greater certainty in base and future year socioeconomic data estimates. During preliminary discussions, the study team aimed to do the following: Transfer the remainder of the Marshall County network and transportation analysis zone (TAZ) system were from the Northwest Mississippi travel demand model; Transfer Tunica and T ate Counties from the Northwest Mississippi travel demand model; and Transfer the rest of Fayette and Tipton Counties from the Tennessee statewide model. However, it was observed that zones in the Northwest Mississippi model were far more granular than other zones in the rural parts of the Memphis MPO model. Therefore, the study team used Census tracts and TAZs instead to build the zone structure for the expanded study area. Figure 2-1 Memphis MPO Model Boundary Household Segmentation in Travel Demand Model The previous Memphis MPO model was segmented by auto ownership and included an auto availability model within the four-step modeling framework. 2-2 Cambridge Systematics, Inc.

19 Memphis MPO Model Update During discussions with the MPO, it was determined that having household income as a segmentation variable was more relevant from a social and environmental justice standpoint. Therefore, the modeling team used income as a segmentation variable in the update travel demand model. In particular, four income categories were used: Annual Household : $0 - $20,000 Annual Household : $20,001 - $35,000 Annual Household : $35,001 - $100,000 Annual Household : Over $100,000 This income segmentation was used both in trip generation as well as mode choice modeling. This allows the model and planner to the ability to better capture and validate the travel behavior patterns and decision-making of households from different incomes. Especially important is the ability to capture accurately the transit riding behavior of lower income households in the Memphis region. As a result of using income segmentation in the model, there is no auto ownership segmentation. Consequently, the auto ownership model was dropped from the model stream in the new Memphis MPO model. During model calibration, income-specific segmentation was introduced in the mode and destination choice models. This allows the Memphis MPO to capture incomespecific policy impacts in the model. The calibrated mode choice model shows that lower income respondents have greater propensity to use transit when compared to other income segments. Non-Transportation Modes in the Travel Demand Model The Memphis region has invested in bike and pedestrian development projects over the past several years. Therefore, it was deemed vital that these modes be included in the model stream through the mode choice model so regional planners may capture the quantitative benefits of bike and pedestrian improvements in the region. The household survey data was designed explicitly to capture the use of bike and walk modes for travel in the region. The regional travel demand model network was skimmed to generate bike and walk distances. Bike and walk were included as potential options for most trip purposes except for modes such as escorting. Trip tables that describe bike and walk travel are generated as outputs from the travel demand model. However, the bike and walk trips are not included in traffic assignment. Cambridge Systematics, Inc. 2-3

20 Memphis MPO Model Update Advanced Freight/Truck Model using GPS Data Memphis is a major freight hub and has extensive truck and rail movements. There is extensive truck traffic in the region owing to major freight generators such as FedEx and other regional distributors. Therefore, there was a critical need to develop a truck model that captures behavioral patterns of regional truck movements using the most advanced data possible. The Tennessee Department of Transportation (TDOT) purchased truck GPS data from the American Transportation Research Institute (ATRI) for the entire State as part of the statewide model development project. Processed truck GPS data were made available to the modeling team for use in the Memphis MPO model update process. Similarly, TRANSEARCH data purchased by TDOT were also made available for the Memphis MPO model update. Both datasets were utilized to the fullest extent to develop a truck generation and distribution model which is described in a later section. The entire model script for this advanced freight model was written fresh and then incorporated within the overall model source code. Integration with the Regional Land Use Model One of the key goals of the study was to integrate the travel demand model with the regional land use model that was being conducted as part of a separate study. The study team provided the TAZ file structure that served as the basis for the regional land use model. The land use model developed forecasts for total households, total population, and employment in a few key categories at a TAZ level for multiple future years. The study team then utilized these forecasts to allocate aggregate sociodemographic growth to detailed sub-segments that were necessary for the travel demand model update. A detailed discussion is provided below. The advantage of this approach is that the socio-demographic forecasts used in the land use model were also incorporated in the regional travel demand model; thereby ensuring consistency between the two studies. 2.2 MODEL FRAMEWORK The updated model remains a four-step trip-based model with a journey-to-work chain. Key components are discussed below: 2-4 Cambridge Systematics, Inc.

21 Memphis MPO Model Update Trip Generation Module The new trip generation model retains all nine trip purposes that existed in the previous version of the model. These purposes include: Journey-to-work chain, Home-based chauffeur, Home-based school, Home-based university, Home-based social recreation, Home-based other, Nonhome-based work, and Nonhome-based nonwork trips. Based on an assessment of the household survey data, it was determined that enough survey records existed to develop robust models for each of the nine purposes. The trip generation methodology relies on classifying households into logical socioeconomic categories based on household income, household size, and number of workers (for work purposes). These classifications were used to generate trip production rates. The trip generation methodology also incorporates a trip attraction framework. Trip attraction models are linear regression models with variables such as employment, households, and population as explanatory variables. The resulting linear regression model equations were used as measures of attractiveness in the distribution choice model. The previously existing auto ownership model was dropped since income segmentation was used as a model segmentation variable. Trip Distribution/Destination Choice Module Destination choice models allow for a more exhaustive treatment of attraction zone-specific measures as well as composite congestion measures between the production and attraction zones. The CS team utilized standard destination choice models that use variables such as: Distance to capture degrees of separation between production and attraction zones; Intrazonal indicators to measure the attractiveness of traveling short distances; Density measures that capture the impact of landform on trip attractions; Cambridge Systematics, Inc. 2-5

22 Memphis MPO Model Update Results from the trip attraction models that highlight zonal attractiveness; and Mode choice logsums as a measure of accessibility. Mode Choice Module The CS team estimated partially segmented mode choice models for each trip purpose. The team reviewed and tested several model configurations. To the extent possible, the team utilized nested logit model framework that is used in the previous Memphis MPO model. This framework was developed using variable ranges that have been prescribed by the FTA in previous studies. This model includes the new bike and pedestrian modes as options for several trip purposes. The placeholders for future modes such as light rail and bus rapid transit were dropped. Some MPOs have recently included all transit modes under a single transit umbrella and used level of service differences to capture the sensitivity of models for different types of transit modes. The new Memphis MPO model will be part of this same paradigm. Time-of-Day Module Time-of-day models apportioned daily travel demand into time periods, as in the existing MPO model. Based on the survey data, the share of travel in each of these time periods was revised. Four time periods were retained: AM Peak 6 AM to 9 AM Mid-Day 9 AM to 2 PM PM Peak 2 PM to 6 PM Night 6 PM to 6 AM Journey to Work Chain There are two sub-components in the journey to work chain one that captures the number of stops, and the second that enumerates these stops. After a careful review of the model, it was determined that the second of these models was incorrectly coded in the previous Memphis MPO model. Having to recode this model would necessitate reallocation of a lot of resources (both time and money). It was determined, in consultation with the MPO, that this model would be dropped. 2-6 Cambridge Systematics, Inc.

23 Memphis MPO Model Update Dropping this module does not impact the model results significantly. Further, the model was calibrated to account for the fact that this step was dropped from the overall model chain. Freight Model As discussed earlier, the freight model uses a combination of ATRI data and TRANSEARCH data and is a state-of-the-practice freight model Key explanatory variables for this model are socio-economic data and these data have been forecast for several future years up to As such, the freight model and is well suited to model truck-based travel in the region for a long time to come. External Model The external model is a key component of a travel demand model and it captures traffic that has at least one trip end outside the region. In order to better represent this travel market, an external O-D survey is often conducted and the results from the survey are used to estimate external travel models. The previous Memphis MPO model contains an external model. This model first identifies the external stations for the regional travel demand model. This model then uses traffic counts at these external traffic stations to generate trip generation rates for the external model. In addition, this model has a trip distribution model component that assigns internal trip ends to the regional model TAZs. However, with the change in the model area, the same external stations may not be used to determine the external travel. This was the first key step undertaken by the study team. Next, with no new external travel data collected as part of the current model update, the model team had to use the previous external model for trip distribution purposes. It must be noted that the trip distribution model used in the external market analysis does not have distance as an explanatory variable. Due to this, two zones with identical socio-demographic data that are 5 and 50 miles away from the external station will both attract the same number of external trips. Trip Assignment Module The goal of the assignment step was to provide Memphis MPO staff with working highway and transit assignment models which comply with Federal, state, and local planning goals and guidelines. On the highway side, equilibrium assignment procedures have been evolving in recent years, as has thinking on what constitutes sufficient convergence of Cambridge Systematics, Inc. 2-7

24 Memphis MPO Model Update assignment models (generally, more iterations of equilibrium assignment are required for stable assignments than previously accepted). The model team implemented an optimal equilibrium highway assignment procedure in TransCAD, keeping in mind the sufficiency of convergence, the MPO s requirements for model run time, and the ability to properly validate the assignments. For transit assignment, previously coded procedures were retained with the exception that all transit routes were treated exactly the same with no distinction between local bus and trolley service. 2-8 Cambridge Systematics, Inc.

25 3.0 Model Estimation The primary goal of the Memphis MPO model update project was to utilize the data collected by the household travel survey and the transit on-board survey to update and enhance the regional travel demand model. This section describes the entire model development process including: Development of Socioeconomic data required for modeling; The updated zone system used in modeling; Description of the household and transit on-board survey data used in model estimation; Estimation of each of the major model steps including: Trip Generation that captures the total number of trips being generated; Trip Distribution/Destination Choice which captures the number of trips between different zones; Time-of-Day Choice which breaks down the daily trip table into four time-of-day periods; Mode Choice Models which capture the usage of different modes in the region; Freight Models that describe the movement of trucks in the region; and Special Generators that account for large travel attractors in the region. 3.1 PREPARATION OF SOCIOECONOMIC DATA The preparation of socioeconomic data is one of the key steps in model development. Having high quality, accurate socioeconomic data improves the accuracy of the model in representing regional travel behavior. Socioeconomic data are required by all travel demand forecasting models, with the exception of origin-destination matrix estimation models, many of which still include socioeconomic data for the development of seed matrices. Data are either aggregated or disaggregated to the TAZ level, and are commonly divided into production and attraction components, with the production side focused on households and the attraction side focused on employment and school enrollment. This section outlines all the steps undertaken to populate the socioeconomic data for the model. Census Data to Generate TAZ-level Demographics Data Socioeconomic data is used in many aspects of travel demand modeling. Most significantly, it is used in the model s input variables for forecasting travel. Cambridge Systematics, Inc. 3-1

26 A zone with more children will likely have more school trips. Wealthier households tend to have greater sensitivity to travel time. Additionally, socioeconomic data is necessary for data expansion. A survey sample represents the entire population, but the number of households or persons represented by segments of the sample has to be computed. The expansion scales the sample by relating its socio-economic characteristics to the characteristics of the entire population. This section describes the process used for collecting and applying the zonal data for use in the regional travel demand model. Data Sources In this project, all person and household data comes from the Census. Because 2010 is the base year for the model, a wealth of information from the decennial population survey was used in generating the zonal socioeconomic data. When available, data were drawn from the summary file (SF1) of the Census database. This summary file is population data rather than a survey, so every person and household is counted. The other source used was the sample-based American Community Survey (ACS) from the Census Bureau, which estimates the number of households and population in a variety of segments in each block group or tract. The Census Transportation Planning Products (CTPP) is produced from the ACS in its own TAZs, which are different from model TAZs, and therefore a crosswalk must be developed between two sets of TAZs. A complete count of persons and households is available down to the block level, which is smaller than the model TAZs. Some household and person-level characteristics are only available down to the block group, tract levels, or Census Transportation Planning Products (CTPP) TAZs. CS staff identified data at the most disaggregate level available. Getting data in geographic units smaller than TAZs meant that a sum of values of the units contained in a TAZ could be used to generate zonal data. When information like household households is only available at the block group level, for example, assumptions have to be made about how the households should be divided into each income group for the TAZs within that block group. Data Preparation Criteria Key criteria for developing the zonal data include the following: The model input must be broken down by the geographic unit used for modeling, in this case traffic analysis zone (TAZ). 3-2 Cambridge Systematics, Inc.

27 Socioeconomic data should be from the model s base year. There should be no double counting of people or households. Subsets of the totals should sum to the total (e.g. the sum of people by age groups must equal the number of people). Allocating Data to Model TAZs The first step in this process was to develop a crosswalk between the different geographies that are being included in the model. Tracts and block groups can be related to blocks by their GeoID. A block s GeoID is simply a concatenation of its state number, county number, tract number, block group number and block number. The relation of block to CTPP TAZs and model TAZs is more complicated because it requires a spatial overlay in ArcGIS. In order to avoid situation where a single block is associated with more than one TAZ, the locations of the centroids of the blocks were used to map the block to model TAZ. These steps resulted in a table of blocks that have a single corresponding tract, block group, CTPP TAZ, and model TAZ associated with them. The following assumptions were made to support the allocations: All blocks in a block group would have the same distribution of households of a certain segmentation. For instance, all blocks within a block group would have the same percentage of low income, medium income, high income, and very high income households. Then, for each block the percentage share was multiplied by the number of households or people in the block. An example is shown below: Share of people who are employed in block group i People who are employed in block j, which is contained in block group i Share i,emp = Pop i,emp Popi,total With all the variables in blocks, they can then be summed to TAZs. Using the same example as above: People who are employed in TAZ k for all blocks, j, in TAZ k Pop k,emp = Pop j,emp j Consistency Checks The synthesized data were then compared to national sources to ensure consistency between the original data and the synthesized population. Cambridge Systematics, Inc. 3-3

28 The first check was to aggregate the zonal data and compare them to regional totals for population and number of households. Further, the tracts should have the sum of values in their block groups, and the same for blocks in a block group. Further, the zonal TAZ totals should match the Census totals. The summary file and ACS will have some differences because the latter is an estimate. Significant discrepancies indicate errors in either allocation of data or mismatched data. These checks showed that the socio-demographic data were consistent between the datasets used and between the input and the final zonal values. The regional totals are shown in Table 3.1. The counts of people (over 16) in the population by employment status (from Labor Force Statistics) was used to check the household number of worker totals (Table 3.2). Table 3-1 County Totals by Source County Households ACS Households Census Summary File Households TAZ File Population Summary File Population TAZ File Crittenden 18,717 19,026 19,026 50,902 50,902 DeSoto 55,768 57,748 57, , ,216 Marshall 12,738 13,369 13,369 37,144 37,144 Tate 9,950 10,035 10,035 28,886 28,886 Tunica 4,039 3,927 3,927 10,778 10,778 Fayette 13,498 14,505 14,505 38,413 38,413 Shelby 340, , , , ,644 Tipton 21,235 21,617 21,629 61,081 61,117 Total 476, , ,198 1,316,100 1,316, Cambridge Systematics, Inc.

29 Table 3-2 Number of Workers by Source County Number of Workers from Labor Force Statistics Households TAZ File Difference Crittenden 20,405 19,580 4% DeSoto 75,550 75,304 0% Marshall 14,480 13,314 8% Tate 12,665 11,797 7% Tunica 4,575 4,490 2% Fayette 16,660 15,964 4% Shelby 418, ,116 7% Tipton 26,330 25,699 2% Total 589, ,261 6% Creating Cross-Tabulations to Support Model Application The trip generation model uses cross-tabulations of key socio-demographic variables to quantify trip rates. The study team identified the combinations of variables needed to generate the cross-tabulations and included these in the socio-demographic file and they include: Number of workers and household income Number of persons and number of children Number of persons and number of workers Numbers of persons and household income While some of these cross-tabulations exist at aggregate Census geographies, not all the cross-tabulations are available including household size and children, and household size and detailed income categories. So, the study team used an advanced frataring (iterative proportional fitting) methodology to generate crosstabulations from the single variable control totals that exist for each zone. The household survey data was used as the seed for this frataring. Care was taken to ensure that: The sum of households within a certain cross-tabulation add up to total households by TAZ. Households of a certain dimension in the cross-tabulation must match the control totals for that variable by TAZ. If a certain variable is used across multiple cross-tabulations, care must be taken to ensure that the distribution of that variable across each of the crosstabulations is exactly identical. Cambridge Systematics, Inc. 3-5

30 School Enrollment Both school and University enrollment data were provided by the Memphis MPO. These data were assigned to the appropriate TAZ and cross-referenced against actual school and University location for validation purposes. Economic Data from InfoGroup While the Census is an excellent resource for population and household information, it does not provide any detail about employment (by industry) that is required for both the passenger as well as the freight model. So, it was deemed necessary to purchase employment data from a commercial vendor. Data Purchase InfoGroup had provided employment by establishment for all Tennessee counties through a contract with TDOT. For consistency purposes, the study team purchased Mississippi and Arkansas data from InfoGroup as well. InfoGroup updated the Tennessee employment data using their latest files. As such, all establishment information was purchased for The purchased file included information about each establishment in the modeling region including: 3-digit North American Industry Classification Scheme (NAICS) code; Exact address as well as latitude and longitude of location; and Number of employees; QA/QC Checks As a first step, CS staff compared the geocoded locations against the exact address information. Locations that appeared to be incorrectly geocoded were shared with InfoGroup, who conducted a second round of geocoding on the data to improve the quality of the location of the data. Next, CS staff evaluated the NAICS code associated with the largest establishments and the actual function that they carried out. Some corrections were made, in conjunction with InfoGroup, to the NAICS codes. Third, CS staff also evaluated the employment numbers ascribed to each establishment in the database and compared them against other publicly available data sources. Some changes were made, especially, for the largest establishments. The employment numbers in the database were for 2014, while the model base year is CS staff developed adjustment factors based on total 3-6 Cambridge Systematics, Inc.

31 employment data available from TDOT for 2010 to generate an employment database for the model base year. In total, the base year employment in the region is 638,000 with a majority of employment focused in Shelby County. Employment Categories The establishment level InfoGroup data were synthesized at a TAZ level to produce employment by NAICS category for each TAZ. This process eliminates all unique identifying information about the establishment and is suitable for use in a travel demand model. In total, there are 96 NAICS categories and this type of detailed information is not required for the travel demand model. So, the CS team collapsed the 96 NAICS 3-digit categories into 10 broad aggregate categories (Table 3.3). Each of these 10 industry categories were used in the modeling process (either freight or passenger). The 10 categories are listed below: Agriculture (908 employees); Mining (97 employees); Transportation/construction/utilities (47,959 employees); Construction (27,483 employees); Manufacturing (60,060 employees); Wholesale (33,132 employees); Retail (85,588 employees); Office (212,345 employees); and Services (170,510 employees) Cambridge Systematics, Inc. 3-7

32 Table 3-3 Crosswalk between NAICS Categories and Model Employment Categories NAICS 3-digit Code Industry Code Model Category NAICS_111 Crop Production Agricultural NAICS_112 Animal Production Agricultural NAICS_113 Forestry and Logging Agricultural NAICS_114 Fishing, Hunting and Trapping Agricultural NAICS_115 Support Activities for Agriculture and Forestry Agricultural NAICS_211 Oil and Gas Extraction Mining NAICS_212 Mining (except Oil and Gas) Mining NAICS_213 Support Activities for Mining Mining NAICS_221 Utilities TCU NAICS_236 Construction of Buildings Construction NAICS_237 Heavy and Civil Engineering Construction Construction NAICS_238 Specialty Trade Contractors Construction NAICS_311 Food Manufacturing Manufacturing NAICS_312 Beverage and Tobacco Product Manufacturing Manufacturing NAICS_313 Textile Mills Manufacturing NAICS_314 Textile Product Mills Manufacturing NAICS_315 Apparel Manufacturing Manufacturing NAICS_316 Leather and Allied Product Manufacturing Manufacturing NAICS_321 Wood Product Manufacturing Manufacturing NAICS_322 Paper Manufacturing Manufacturing NAICS_323 Printing and Related Support Activities Manufacturing NAICS_324 Petroleum and Coal Products Manufacturing Manufacturing NAICS_325 Chemical Manufacturing Manufacturing NAICS_326 Plastics and Rubber Products Manufacturing Manufacturing NAICS_327 Nonmetallic Mineral Product Manufacturing Manufacturing NAICS_331 Primary Metal Manufacturing Manufacturing NAICS_332 Fabricated Metal Product Manufacturing Manufacturing NAICS_333 Machinery Manufacturing Manufacturing NAICS_334 Computer and Electronic Product Manufacturing Manufacturing NAICS_335 Electrical Equipment, Appliance, and Component Manufacturing Manufacturing NAICS_336 Transportation Equipment Manufacturing Manufacturing NAICS_337 Furniture and Related Product Manufacturing Manufacturing NAICS_339 Miscellaneous Manufacturing Manufacturing NAICS_423 Merchant Wholesalers, Durable Goods Wholesale 3-8 Cambridge Systematics, Inc.

33 NAICS 3-digit Code Industry Code Model Category NAICS_424 Merchant Wholesalers, Nondurable Goods Wholesale NAICS_425 Wholesale Electronic Markets and Agents and Brokers Wholesale NAICS_441 Motor Vehicle and Parts Dealers Retail NAICS_442 Furniture and Home Furnishings Stores Retail NAICS_443 Electronics and Appliance Stores Retail NAICS_444 Building Material and Garden Equipment and Supplies Dealers Retail NAICS_445 Food and Beverage Stores Retail NAICS_446 Health and Personal Care Stores Retail NAICS_447 Gasoline Stations Retail NAICS_448 Clothing and Clothing Accessories Stores Retail NAICS_451 Sporting Goods, Hobby, Book, and Music Stores Retail NAICS_452 General Merchandise Stores Retail NAICS_453 Miscellaneous Store Retailers Retail NAICS_454 Nonstore Retailers Retail NAICS_481 Air Transportation TCU NAICS_482 Rail Transportation TCU NAICS_483 Water Transportation TCU NAICS_484 Truck Transportation TCU NAICS_485 Transit and Ground Passenger Transportation TCU NAICS_486 Pipeline Transportation TCU NAICS_487 Scenic and Sightseeing Transportation TCU NAICS_488 Support Activities for Transportation TCU NAICS_491 Postal Service TCU NAICS_492 Couriers and Messengers TCU NAICS_493 Warehousing and Storage TCU NAICS_511 Publishing Industries (except Internet) TCU NAICS_512 Motion Picture and Sound Recording Industries TCU NAICS_515 Broadcasting (except Internet) TCU NAICS_517 Telecommunications TCU NAICS_518 Data Processing, Hosting and Related Services Office NAICS_519 Other Information Services Office NAICS_522 Credit Intermediation and Related Activities Office Securities, Commodity Contracts, and Other Financial Investments and NAICS_523 Related Activities Office NAICS_524 Insurance Carriers and Related Activities Office NAICS_525 Funds, Trusts, and Other Financial Vehicles Office NAICS_531 Real Estate Office Cambridge Systematics, Inc. 3-9

34 NAICS 3-digit Code Industry Code Model Category NAICS_532 Rental and Leasing Services Office NAICS_533 Lessors of Nonfinancial Intangible Assets (except Copyrighted Works) Office NAICS_541 Professional, Scientific, and Technical Services Office NAICS_551 Management of Companies and Enterprises Office NAICS_561 Administrative and Support Services Office NAICS_562 Waste Management and Remediation Service Office NAICS_611 Educational Services Office NAICS_621 Ambulatory Health Care Services Services NAICS_622 Hospitals Services NAICS_623 Nursing and Residential Care Facilities Services NAICS_624 Social Assistance Services NAICS_711 Performing Arts, Spectator Sports, and Related Industries Services NAICS_712 Museums, Historical Sites, and Similar Institution Services NAICS_713 Amusement, Gambling, and Recreation Industries Services NAICS_721 Accommodation, including Hotels and Motels Services NAICS_722 Food Services and Drinking Places Services NAICS_811 Repair and Maintenance Services NAICS_812 Personal and Laundry Services Services NAICS_813 Religious, Grantmaking, Civic, Professional, and Similar Organizations Office NAICS_921 Executive, Legislative, and Other General Government Support Office NAICS_922 Justice, Public Order, and Safety Activities Office NAICS_923 Administration of Human Resource Programs Office NAICS_924 Administration of Environmental Quality Programs Office Administration of Housing Programs, Urban Planning, and Community NAICS_925 Development Office NAICS_926 Administration of Economic Programs Office NAICS_928 National Security and International Affairs Office Integrating Land Use Forecast Data for Future Year Model Runs A stand-alone land use model was also developed by Memphis MPO. This model produced TAZ-level forecasts for the following variables: Total number of households; Total population; and Seven employment categories Industrial/Manufacturing Wholesale/Transportation 3-10 Cambridge Systematics, Inc.

35 Office Service Retail Government Other Each of the categories were first disaggregated to the 3-digit NAICS categories and then aggregated back to the ten employment categories included in the travel demand model. These data serve as control totals for future year socio-economic data. However, the data produced by these forecasts do not provide all the details necessary for the travel demand model. So, CS staff developed spreadsheet-based routines that took the control totals developed by the land use model, and utilized demographic and employment distributions from the 2010 TAZ file to develop all socio-demographic cross-tabulations and all employment summaries required for both the freight and passenger models. 3.2 NETWORK-RELATED DATA The region covered by the Memphis MPO model is part of several different models including: Tennessee, Mississippi, and Arkansas (under development) statewide models; Northwest Mississippi Regional Model; West Memphis MPO; and Variations of the previous MPO model including the Third Bridge model that was used to study bridge alternatives over the Mississippi River. Use of Mississippi Third Bridge Model Network A model was developed to support studies of a possible third bridge (in addition to the existing I-40 and I-55 bridges) over the Mississippi River in the Memphis area 1. The Third Bridge model network was chosen as the basis for developing a detailed transportation network for the following reasons: It incorporates the entire region that was included as part of the previous Memphis MPO model. 1 The Southern Gateway: Crossing the River, Connecting America Purpose and Need Statement, Cambridge Systematics, Inc. 3-11

36 This model includes Crittenden County TAZs and, is an excellent resource for data related to Arkansas. Using this model obviates the need to use the West Memphis MPO model thereby limiting the number of models that must be stitched together for the Memphis MPO model update. The TAZ structure in the urban region is granular enough to support model development and enhancements. The statewide models, while having a lot of detail, are pretty large sized even in urban areas. Having such large TAZs in the urban areas reduces the effectiveness of the model to capture key elements such as transit accessibility. Further, both the Mississippi and Arkansas statewide models were under development and therefore, not ready for use in the Memphis MPO model update. Rural TAZs are sized uniformly throughout the region and therefore, provide an excellent starting point for model development. As a contrast, the Northwest Mississippi TAZs were much smaller than TAZs in the rural parts of Tennessee. Having different sized TAZs may lead to inconsistencies in traffic assignment procedures in different parts of the region which is not desirable. Further, the 3-bridge model provides a consistent list of attributes in the model network which is crucial for the development of a functional network structure. Key Features of Model Network The Third Bridge model network was developed based on the network from the previous Memphis MPO model, with additional network information provided from the West Memphis MPO. The documentation of the previous model network 2 provides details on the development of highway network attributes such as facility type, speed, and capacity. Some key features in the Memphis model network are listed below: For areas that were not covered by the Third Bridge model such as parts of Marshall County, new TAZs were designed using the Census TAZ structure. In total, the Memphis MPO model has 1729 internal zones which are aggregated into 26 planning districts for modeling purposes. The districts 2 Kimley-Horn and Associates, Inc., Cambridge Systematics, Inc., and HNTB. Technical Memorandum #1 (a): Network and TAZ Development. Prepared for Memphis MPO, Cambridge Systematics, Inc.

37 have greater granularity in the urban portions of Shelby and DeSoto counties and are rather aggregate in nature in the rural counties. The modeling team retained all attributes included in the model stream that were used in different model routines. Only those variables that were not utilized were dropped from the final list. This allows greater continuity in the modeling framework for Memphis MPO staff. Some roadways that had been built in the recent past were adjusted to account for differences in proposed and actual geographic layout. Such changes were made in discussions with the Memphis MPO staff. It was observed that some attributes such as number of lanes, posted speeds, and functional classes were incorrect in parts of the region, especially Mississippi. Wherever such inconsistencies were found, CS staff made changes to the network during the model development procedures. No changes were made to routines such as: Volume to capacity curves; Model terminal times; Transit and highway skimming procedures; and Feedback loop RMSE criteria. Use of Network in Model Development The model network is used to support the model development: First, trip ends from the household survey data are geocoded to the internal traffic analysis zones (TAZ) in the model. In total, the model has 1,729 internal TAZs. Second, the network is skimmed to generate level of service matrices. These level-of-service matrices are then attached to trip ends to support mode choice and destination choice model estimation. The level of services matrices generated include: Auto travel times, parking costs, terminal times, and auto operating costs; Transit travel times, transit fares, number of transfers, and transit wait times; Bike and walk travel distances; and Measures of separation between zones including travel times, and distances for the destination choice model. Third, the model network is used to run the transit and highway assignment procedures. Cambridge Systematics, Inc. 3-13

38 3.3 TRANSPORTATION DATA USED IN MODELING Three key data sources were used to support the development of the travel demand forecasting model (Table 3.4). Among these, the household travel survey data and the transit on-board survey data were obtained using survey efforts that were conducted specifically for this model update. Freight data, on the other hand, were obtained from TDOT and are part of a multiuse dataset that will be used to update both the Memphis MPO model update and the statewide model. This section outlines all the key datasets used in the model estimation process. Table 3-4 Use of Different Data in Model Development Model Step Household Travel Survey Transit On-board Survey Freight GPS Data Freight TRANSEARCH Data Passenger Trip Generation Passenger Trip Distribution Passenger Mode Choice Model Passenger Time-of-Day Choice Model Freight Trip Generation Model Freight Trip Distribution Model Model Validation Household Travel Survey Data The household survey conducted in the Memphis region was a key part of the Memphis MPO model update study. The household survey provides modelers and planners with a snapshot of travel behavior in the region. The travel and socioeconomic data collected as part of this study were analyzed and used for several purposes including: The development of a regional travel demand forecasting model; Travel patterns by different times of day; Geographic distribution of travel among area residents; Preferences towards different modes for by purpose; and 3-14 Cambridge Systematics, Inc.

39 The objective of the household survey was to obtain an inventory of representative travel behavior in the Memphis MPO modeling area. The sample was distributed to a total of 7 counties including counties in Tennessee and Mississippi. No data were collected from Crittenden County in Arkansas. Shelby County is the largest county in the region and accounts for a majority of households in the study region. To obtain a representative sample of the region s 450,000 households, the sampling plan took into account household size, automobile ownership, number of workers, and geography. An address-based sample was used along with a multi-modal recruit and retrieval effort to improve response rates. Respondents had the option of filling out their diaries and mailing them back, going on the web to complete their diaries, or providing their travel information over the phone. A total of nearly 5,000 household surveys were collected with about 4,300 household surveys deemed complete for purposes of the modeling analysis. Transit On-board Survey Data The onboard survey was conducted in the Memphis metropolitan area between Fall 2013, and Spring 2014 (make-up on one route). At that time Memphis Area Transit Authority (MATA) operated 35 bus routes, and three trolley lines. The main goal of the 2013 onboard survey was to collect surveys from at least ten percent of riders on all transit routes to support detailed disaggregate analysis. Total daily boardings on the entire system are in excess of 28,000. As part of regional planning and modeling efforts, it is critical to understand the travel and usage patterns of riders to support transit planning and to improve the sensitivity of the regional travel demand model to the transit market segment. In total, 3,227 records were entered into the database. Traditional pen and paper surveys were handed out and were supplemented by boarding and alighting counts that were recorded with GPS loggers to provide detailed transit usage patterns and rider information to support modeling. Since these survey data are expected to influence transit policy over the next decade in the Memphis region, a careful overview of existing and required data was carried out. Three key points were critical to finalizing the approach: Cambridge Systematics, Inc. 3-15

40 First, the focus of the study was to capture the most current and reliable data necessary to determine future public transportation needs in the Memphis region. Second, the study collected detailed transit ridership data for different routes by time of day to develop a disaggregate transit trip table that will support model estimation. Third, the study was designed such that data collected from the on-board survey could be leveraged to conduct fare equity analysis. Freight/Truck Survey Data The goal of the freight/truck survey was to develop freight truck trip tables that the Memphis MPO may use to identify how freight trucks utilize major regional gateways and corridors, and how these movements may be modeled and used in evaluating changes in freight performance. The survey was comprised of several data collection efforts, as follows: 1. Obtained processed truck GPS trip records from the American Transportation Research Institute (ATRI) through TDOT; 2. Using a CS-developed analytical tool to extract MPO-level information from the TRANSEARCH database which was also provided by TDOT; and 3. Developing an outline for telephone interviews for industry experts in special generator locations. This approach expands on work successfully conducted by CS staff in other regions of the U.S. including Southern California and Phoenix. Bicycle/Pedestrian Survey Data A three-phase data collection approach was used in collecting data on nonmotorized travel use in the region, as follows: An Internet-based survey of bicyclists and pedestrians from rider groups, building off a recent survey conducted by the MPO, was conducted. The MPO sent out invitations for respondents to participate in this survey; A camera-based counts of bicyclists and pedestrians was conducted at 10 different locations in the region; and Participants from the household travel survey and that had reported using a bicycle or walk in the past one month were asked to participate in a webbased survey to establish a baseline universe for bicycle and pedestrian use in the region. The data from these surveys were primarily used to update the regional bike and pedestrian plan and not used in the travel demand forecasting model Cambridge Systematics, Inc.

41 3.4 TRIP GENERATION MODEL This section covers the development of the following specific submodels related to trip generation: Internal person trip productions Internal person trip attractions Special generators for passenger trips Internal Person Trip Productions Trip production models were developed for the following nine trip purposes: Journey to work Home based school Home based university Home based shopping Home based social-recreational Home based pickup/dropoff Home based other Non-home based work Non-home based non-work Journey to work trips are defined as trips with or without stops between home and work. These journeys are sometimes referred to as half-tours. This approach differs from most conventional four-step models in that the intermediate stops are not treated as separate trips. The trip production models are two-dimensional cross-classification models based on various demographic variables. Households for each zone are crossclassified by income level, number of persons, number of workers, and numbers of children (age 0-17). In the socio-demographic files, necessary crossclassifications of households by pairs of these variables were prepared by iterative proportional fitting. The cross-classification trip production models were estimated from the 2014 household travel survey. Table 3.5 through Table 3.13 show the trip production models for each trip purpose. Some notes on these tables are appropriate: In the previous versions of the Memphis MPO models, vehicle ownership had been used as a segmentation variable. Based on discussions with the MPO, a switch to household income based segmentation was implemented. Cambridge Systematics, Inc. 3-17

42 It was assumed that households without workers made no journey to work or non-home based work trips. The survey data set showed a very small number of these trips, most likely non-workers having a rare work activity though some could represent errors in the data set. There are home based school trips for households without children because some 18-year olds have school activities. The survey data set showed a lack of sensitivity of home based shopping trips and non-home based non-work trips to income level, and so the trip rates are the same across all income levels. For all other purposes, larger households have higher trip rates than smaller households, and trip-making increases with rising income level Cambridge Systematics, Inc.

43 Table 3-5 Trip Production Model for Journey to Work Chains Workers Less Than $20,000 $20,000 - $35,000 Level $35,000 - $100,000 Greater Than $100,000 Average Average Table 3-6 Trip Production Model for Home Based School Trips Persons Number of Children Average 1 0 n/a n/a n/a n/a n/a n/a Average Table 3-7 Trip Production Model for Home Based University Trips Persons Number of Workers Average n/a n/a n/a Average Table 3-8 Trip Production Model for Home Based Shop Trips Level Persons Less $20,000 $35,000 Greater Than Average Than - - $100,000 $20,000 $35,000 $100, Average Cambridge Systematics, Inc. 3-19

44 Table 3-9 Trip Production Model for Home Based Pickup/Dropoff Persons Number of Children Average n/a n/a n/a n/a n/a n/a Average Table 3-10 Trip Production Model for Home Based Social-Recreational Persons Less Than $20,000 $20,000 - $35,000 Level $35,000 - $100,000 Greater Than $100,000 Average Average Table 3-11 Trip Production Model for Home Based Other Trips Persons Less Than $20,000 $20,000 - $35,000 Level $35,000 - $100,000 Greater Than $100,000 Average Average Table 3-12 Trip Production Model for Non-Home Based Work Trips Workers Less Than $20,000 $20,000 - $35,000 Level $35,000 - $100,000 Greater Than $100,000 Average Average Cambridge Systematics, Inc.

45 Table 3-13 Trip Production Model for Non-Home Based Non-Work Trips Persons Less Than $20,000 $20,000 - $35,000 Level $35,000 - $100,000 Greater Than $100,000 Average Average Internal Person Trip Attractions Trip attraction models were developed from the expanded household survey data for the nine trip purposes. All models are ordinary least squares regressions with no intercept and are of the following form: Total attractions = A1 * employment for category 1 + A2 * employment for category 2 + B * total households + C * school/university enrollment The number of observations is 26, which corresponds to the number of districts for which the household survey data were aggregated within the survey sampling area. Some trip purposes have fewer observations due to no trips of the purpose being made in some districts. A variety of detailed employment categories were available for modeling and during estimation different combinations of these employment variables were included to identify the most robust modeling framework. The results presented in Table 3.14 represent the final models selected for implementation. The t-statistics are measures of the statistical significance of the parameter estimates. A t-statistic of 1.96 or greater shows significance at the 95% level. The results from trip attraction regression models were used as key explanatory variables for the destination choice model (Section 3.5). Cambridge Systematics, Inc. 3-21

46 Table 3-14 Journey to work chain Trip Attraction Model Summary Variable Parameter Estimate t-statistic Total Employment Home based school Variable Parameter Estimate t-statistic School Enrollment Home based university Variable Parameter Estimate t-statistic University Enrollment Home based shop Variable Parameter Estimate t-statistic Retail Employment Home based pickup/dropoff Variable Parameter Estimate t-statistic Service Employment School Enrollment Home based social recreational Variable Parameter Estimate t-statistic Service Employment Total Households Home based other Variable Parameter Estimate t-statistic Service Employment Total Households Non-home based work Variable Parameter Estimate t-statistic Office Employment Service Employment Retail Employment Non-home based non-work Variable Parameter Estimate t-statistic Service Employment Total Households Special Generators As in the previous version of the model, there are two attractions treated as special generators for passenger (non-truck) travel, the Memphis International Airport and Graceland Cambridge Systematics, Inc.

47 Airport The Memphis-Shelby County Airport Authority (MSCAA) last developed a master plan in the mid-2000s. Since that time, there have been significant changes in the airport s operations. Most notably, Delta Airlines no longer uses the airport as a hub as of This has caused a substantial decline in flights and enplanements, especially transferring passengers. To obtain information on current and projected air passenger demand, MSCAA was contacted to provide up to date information. For the base year of 2010, there were on average 17,700 enplanements per day, according to MSCAA. According to a MSCAA passenger survey prior to the de-hubbing of the airport by Delta, about 46 percent of those trips, or 8,100, were made by passengers originating from Memphis (as opposed to having a connecting flight). According to the same survey, each passenger was accompanied by, on average, 0.42 well-wishers. This results in approximately 11,600 persons going to the airport. The number of other airport visitors unrelated to persons flying to and from the airport (such as persons purchasing tickets for later travel, meeting attendees, or others doing business there) was estimated at 2,000 per day. In total, there are 13,600 persons going to the airport. According to the MSCAA survey, 42.7 percent of trips to the airport by air passengers were from the passenger's residence. Therefore, 42.7 percent of 13,600 daily airport trips in 2010 were assumed to be home based other (5,800 trips) and 57.3 percent assumed to be non-home based (7,800 trips). For future years, forecasts of passenger travel are assumed to grow at 2 percent per year from the 2014 total of average daily enplanements of 7,000. After the de-hubbing, it is estimated that about 98 percent of those trips, or 6,900, were made by passengers originating from Memphis (as opposed to having a connecting flight). Assuming the same number of well-wishers per passenger (0.42), this results in about 9,700 person trips. Adding the 2,000 other airport visitors yields 11,700 trips. Using the same percentages for home based and non-home based trips, there are 5,000 home based other trips and 6,700 non-home based trips for These numbers will be assumed to increase by 2 percent per year for future years. Cambridge Systematics, Inc. 3-23

48 Graceland In the previous version of the model, information from Graceland staff was used to estimate that the site has 1,300 visitors on the average weekday in 2005, the base year for that model. Graceland had estimated a future annual attendance of 700,000, and the weekday average attendance with this annual attendance is roughly 1,500 people per day. This number was used for forecast year scenarios in the previous model. It was estimated that approximately 95% of visitors are from outside Memphis, and so it was assumed that 5% of trips are home based social/recreation and 95% non-home based. For the new model, the Graceland web site 3 indicated that current annual attendance is 600,000, which is consistent with about the same number of daily visitors as in the previous model s base year (1,300). Therefore, the base year assumption for the new model will be 1,300 daily visitors to Graceland, with the same assumed percentages of home based and non-home based trips. The total of 1,500 daily trips will continue to be used for forecast years. 3.5 DESTINATION CHOICE MODELS This chapter covers the development of the destination choice (trip distribution) models for all person trip purposes. Destination choice models were developed for the following nine trip purposes: Journey to work (JTW) Home based university (HBU) Home based school (HBSc) Home based shopping (HBSh) Home based social-recreation (HBSR) Home based other (HBO) Home based pickup/dropoff (HBPD) Non-home based work (NHBW) Non-home based non-work (NHBO) 3 retrieved September 11, Cambridge Systematics, Inc.

49 These are the same trip purposes used in trip generation (see Section 3.4). The destination choice models were estimated using data from the household travel survey. Model Framework The destination choice models are multinomial logit models. Logit models are discrete choice models, which attempt to explain the behavior of individuals making a choice among a finite number of separate alternatives, in this case destination zones. In the logit model, the probability of choosing a particular alternative i is given by the following formula: P(i) = exp (U i)/ j exp(u j) where: P(i) = probability of choosing alternative i U i = utility of alternative i exp = exponential function The utility function Ui represents the worth of alternative i compared to other alternatives and is expressed as a linear function: U i = B 0i + B 1iX 1i + B 2iX 2i + + B nix ni where the X ki variables represent attributes of alternative i, the decision maker, or the environment in which the choice is made and B ki represents the coefficient reflecting the effect of variable X ki on the utility of alternative i. The coefficients are estimated using statistical maximum likelihood methods using specialized logit model estimation software, in this case ELM. Model Variables In the case of logit destination choice models, the alternatives are the destination zones while the attributes may include features of the zones (e.g., travel time from the origin zone) and the environment (e.g., production or attraction zone area type). The mode choice logsum is a measure of the impedance, or cost, of traveling from one zone to another. It is a combined measure of the impedance using the various available modes (highway, transit, and non-motorized) and is computed from the logit mode choice model utilities. The logsum is computed for each trip purpose as follows: Logsum ij = ln k exp(u ijk) where: Cambridge Systematics, Inc. 3-25

50 U ijk = utility of modal alternative k from zone i to zone j (from the mode choice model) Both the mode choice logsum and polynomial functions of the highway distance were tested for use as the impedance measure for each trip purpose. For the final models, for some trip purposes the logsum is used as the impedance measure while for others a function of highway distance is used. For some trip purposes, both measures were used. Size variables are used to measure the attractiveness of particular zones. For most trip purposes the size variable is the number of modeled attractions (see trip attraction rates models) for the trip purpose. Size variables are entered into the utilities as the natural logarithms of the particular variables (for example, ln(attractions)). Variables representing the density of population or employment at the attraction zone were used (in some cases, density of employment by certain types was also used). These were entered as piecewise linear functions. For example, for the journey to work model, the employment density coefficients are different for density levels of 0 to 300, 300 to 1000, 1000 to 3000, and greater than 3000 employees per square mile. Area type indicator variables were also used to identify special attractors such as the downtown districts and other key urban areas in Memphis. Intrazonal indicators that capture the effect of mixed land use on short travel are also included in the model. The models were partially segmented by income category to quantify the impact of household income on travel parameters such as travel distance. The first round of estimated models were shared with the Memphis MPO and TDOT. Based on feedback, the models were re-estimated using either new variables or dropping existing ones with poor explanatory power. Table 3.15 through Table 3.23 show the final destination choice models for the nine trip purposes Cambridge Systematics, Inc.

51 Table 3-15 Destination Choice Model for Journey to Work Trips Parameter Estimated Value t-stat ln(attractions) 1 N/A Mode choice logsum 0.6 N/A Distance (incgrp=1) Distance Squared (incgrp=1) Distance Cubed (incgrp=1) Distance (incgrp=2) Distance Squared (incgrp=2) Distance Cubed (incgrp=2) Distance (incgrp>=3) Distance Squared (incgrp>=3) Distance Cubed (incgrp>=3) Employment Density (0-300) (incgrp<=2) Employment Density ( ) (incgrp<=2) Employment Density (1000-) (incgrp<=2) Employment Density (0-300) (incgrp=3) Employment Density ( ) (incgrp=3) Employment Density (1000-) (incgrp=3) Employment Density (0-300) (incgrp=4) Employment Density ( ) (incgrp=4) Employment Density (1000-) (incgrp=4) CBD Indicator (incgrp=1) CBD Indicator (incgrp=2) CBD Indicator (incgrp=3) CBD Indicator (incgrp=4) Intrazonal Indicator (incgrp=1) Intrazonal Indicator (incgrp=2) Intrazonal Indicator (incgrp=3) Intrazonal Indicator (incgrp=4) Estimation statistics: Number of Observations 6400 Log Likelihood at Convergence Log Likelihood at Null Parameters Rho Squared w.r.t. Null Parameters Cambridge Systematics, Inc. 3-27

52 Table 3-16 Destination Choice Model for Home Based University Trips Parameter Estimated Value t-stat ln(attractions) Mode choice logsum 1 N/A Distance Distance Squared Employment Density CBD Indicator Districts 2,3,4,5 Indicators District 23 Indicator District 24 Indicator Intrazonal Indicator Estimation statistics: Number of Observations 128 Log Likelihood at Convergence Log Likelihood at Null Parameters Rho Squared w.r.t. Null Parameters Table 3-17 Destination Choice Model for Home Based School Trips Parameter Estimated Value t-stat ln(attractions) Mode choice logsum 1 N/A Distance Distance Squared Distance Cubed Population Density (0-1000) Population Density ( ) Population Density (Over 10000) Intrazonal Indicator Estimation statistics: Number of Observations 1254 Log Likelihood at Convergence Log Likelihood at Null Parameters Rho Squared w.r.t. Null Parameters Cambridge Systematics, Inc.

53 Table 3-18 Destination Choice Model for Home Based Shop Trips Parameter Estimated Value t-stat ln(attractions) 1 N/A Mode choice logsum 1 N/A Distance Distance Squared Distance Cubed Population Density (0-100) Population Density ( ) Population Density ( ) Population Density (Over 10000) Non-Retail Employment Density (0-100) Non-Retail Employment Density ( ) Non-Retail Employment Density (Over 1000) Retail Employment Density (0-100) Retail Employment Density ( ) Retail Employment Density (Over 1000) Intrazonal Indicator Estimation statistics: Number of Observations 2900 Log Likelihood at Convergence Log Likelihood at Null Parameters Rho Squared w.r.t. Null Parameters Table 3-19 Destination Choice Model for Home Based Pickup/Drop-off Parameter Estimated Value t-stat ln(attractions) 1 N/A Mode choice logsum 0.6 N/A Distance Distance Squared Distance Cubed Population Density (0-1000) Population Density ( ) Population Density (Over 10000) CBD Indicator Intrazonal Indicator Estimation statistics: Number of Observations 805 Log Likelihood at Convergence Log Likelihood at Null Parameters Rho Squared w.r.t. Null Parameters Cambridge Systematics, Inc. 3-29

54 Table 3-20 Destination Choice Model for Home Based Social-Recreational Trips Parameter Estimated Value t-stat ln(attractions) 1 N/A Mode choice logsum 1 N/A Distance Distance Squared Distance Cubed Population Density ( ) Population Density (Over 10000) Employment Density ( ) Employment Density (Over 10000) CBD Indicator Intrazonal Indicator Estimation statistics: Number of Observations 2866 Log Likelihood at Convergence Log Likelihood at Null Parameters Rho Squared w.r.t. Null Parameters Table 3-21 Destination Choice Model for Home Based Other Trips Parameter Estimated Value t-stat ln(attractions) 1 N/A Mode choice logsum 1 N/A Distance Distance Squared Distance Cubed Population Density (0-1000) Population Density ( ) Population Density (Over 10000) Retail Employment Density (0-100) Retail Employment Density ( ) Retail Employment Density (Over 1000) CBD Indicator Intrazonal Indicator Estimation statistics: Number of Observations 3718 Log Likelihood at Convergence Log Likelihood at Null Parameters Rho Squared w.r.t. Null Parameters Cambridge Systematics, Inc.

55 Table 3-22 Destination Choice Model for Non-Home Based Work Trips Parameter Estimated Value t-stat ln(attractions) 1 N/A Mode choice logsum 0.6 N/A Distance Distance Squared Distance Cubed Non-Service Employment Density (0-100) Non-Service Employment Density ( ) Non-Service Employment Density ( ) Non-Service Employment Density (Over 10000) Service Employment Density (0-1000) Service Employment Density (Over 1000) Intrazonal Indicator Estimation statistics: Number of Observations 1350 Log Likelihood at Convergence Log Likelihood at Null Parameters Rho Squared w.r.t. Null Parameters Table 3-23 Destination Choice Model for Non-Home Based Non-Work Trips Parameter Estimated Value t-stat ln(attractions) 1 N/A Mode choice logsum 0.6 N/A Distance Distance Squared Distance Cubed Employment (0-1000) Employment ( ) Employment (Over 10000) Intrazonal Indicator Estimation statistics: Number of Observations 5737 Log Likelihood at Convergence Log Likelihood at Null Parameters Rho Squared w.r.t. Null Parameters Cambridge Systematics, Inc. 3-31

56 3.6 JOURNEY-TO-WORK STOP MODELS The Memphis model treats journey to work trips as trips with or without stops between home and work. These journeys are sometimes referred to as halftours. This approach differs from most conventional four-step models in that the intermediate stops are not treated as separate trips. This chapter covers the development of the following specific submodels related to stops on the journeys to (and from) work including stop generation and stop destination choice. While both models were estimated, these models are not included in the model application platform. If, at some stage, the MPO were to operationalize these two models, the estimation results included below may be used to support the effort. Stop Generation The journey to work stops model is a multinomial logit model that estimates the number of stops (0, 1, or 2+) for journey to work trips. This model was also estimated from the household survey data. The zero-stop alternative is considered the base alternative with a utility fixed at zero. Table 3.24 shows the utility functions for the other two alternatives (tstatistics are shown in parentheses). The negative coefficients on the number of workers indicate that households with more workers make fewer stops, which makes sense since there are others in the household who could make stops. The presence of children tends to increase stops, which reflects the need to pick up and drop off children at school and other activities. Stops are more likely on the return journey from work than on the journey from home to work Cambridge Systematics, Inc.

57 Table 3-24 Journey to Work Stops Model Utility Functions Number of Stops 1 2 Alternative-specific constant (-11.5) (-2.5) Number of workers (-2.1) 1 worker household (-4.5) 2+ worker household (-5.5) Number of children and income > $20, (7.6) (7.2) Home to work direction (-7.0) (-9.4) Model Estimation Statistics: Number of Observations 4100 Log Likelihood at Convergence Rho Squared w.r.t. Null Parameters Rho Squared w.r.t. Zero Stop Destination Choice The intermediate stop destination choice model estimates the locations of the intermediate stops of journey to work half-tours. The number of stops is estimated by the journey to work stops model, which models whether journey to work chains have zero, one, or two stops. In effect, the intermediate stop destination choice splits the journey to work chains with one or two stops into the component trips that comprise them. Like the primary destination choice models (Section 3.5), the intermediate stop destination choice model is a multinomial logit model, with the alternatives being the potential destination zones for the stops. The variables included in the model are the following: Size variables that includes variables such as office employment, retail employment, service employment, school enrollment, and the number of households; The additional travel time to stop at the zone when compared to traveling directly between the home and the workplace. Different polynomial functions of this diversion travel time including square and cube functions; Indicator variables specifying whether the stop zone is the same as the home zone, the workplace zone, or the zone of a previously modeled stop; and A closeness measure of how close the stop zone is to either end of the journey to work half-tour. The closeness measure takes a value of 1 when the intermediate stop is exactly at the origin or destination zone, and a value of 0 when the stop is exactly equidistant (measured by travel time) Cambridge Systematics, Inc. 3-33

58 from the origin and destination. follows: Closeness = 1 (4*A*B / (A+B)2) Where: The formula for this variable is as A = time between the home and the intermediate stop B = time between the workplace and the intermediate stop For the second stop on chains with two stops, the time variable represents the additional time as opposed to traveling directly between the first stop and the destination. Table 3-25 Intermediate Stop Destination Choice Model Estimation Results Variables Coefficient Estimate t-stat Stop detour time Stop detour time squared Stop detour time cubed Stop TAZ = home TAZ, stop 1, home to work Stop TAZ = first stop TAZ, stop 2, home to work Stop TAZ = work TAZ, home to work Stop TAZ = first stop TAZ, stop 2, work to home Stop TAZ = work TAZ, work to home Closeness measure Size Variables Retail employment Service employment Office employment School enrollment* Households Model Estimation Statistics: Number of Observations 2009 Log Likelihood at Convergence Rho Squared w.r.t. Zero Cambridge Systematics, Inc.

59 3.7 MODE CHOICE MODELS Mode choice models were developed for the following nine trip purposes: Journey to work (JTW) Home based university (HBU) Home based school (HBSc) Home based shopping (HBSh) Home based social-recreation (HBSR) Home based other (HBO) Home based pickup/dropoff (HBPD) Non-home based work (NHBW) Non-home based non-work (NHBO) These are the same trip purposes used in trip generation and trip distribution. Because of limited data availability, the home based university trip purpose was combined with the journey to work trip purpose for the mode choice model estimation though most of the estimated coefficients were allowed to have different values for work and university. Similarly, the home based social/recreation trip purpose was combined with the home based other trip purpose. The mode choice models were estimated a combined data set from two data sources: the household travel survey and the transit on-board survey. Model Framework The mode choice models are estimated using a logit framework. Logit models are discrete choice models, which attempt to explain the behavior of individuals making a choice among a finite number of separate alternatives, in this case travel modes. In a logit model, the probability of choosing a particular alternative i is given by the following formula: P(i) = exp (U i)/ j exp(u j) where: P(i) = probability of choosing alternative i U i = utility of alternative i exp = exponential function The utility function U i represents the worth of alternative i compared to other alternatives and is expressed as a linear function: U i = B 0i + B 1iX 1i + B 2iX 2i + + B nix ni Cambridge Systematics, Inc. 3-35

60 where the X ki variables represent attributes of alternative i, the decision maker, or the environment in which the choice is made and B ki represents the coefficient reflecting the effect of variable X ki on the utility of alternative i. The coefficients are estimated using statistical maximum likelihood methods using specialized logit model estimation software, in this case ELM. In the case of logit mode choice models, the alternatives are the travel modes while the attributes may include attributes of the modes (e.g., travel time from the origin to destination by the particular mode), the decision maker or his or her household (e.g., income level), and the environment (e.g., population density). Model Estimation and Testing Procedure The modes considered for inclusion in the models are: Auto modes including: Drive-alone. Shared-ride with 2 occupants; and Shared-ride with 3 or more occupants. Transit modes including: Transit with auto access (including bus with auto access, trolley with auto access, and bus/trolley with auto access); and Transit with walk access (including bus with walk access, trolley with walk access and bus/trolley with walk access). School bus (for home based school trips only Non-motorized modes including: Walk; and Bicycle. Table 3.26 shows the number of trips by mode and trip purpose from the combined survey data set (household and transit on-board survey) Cambridge Systematics, Inc.

61 Table 3-26 Distribution by Chosen Mode and Purpose in the Survey Data Set Mode JTW HBU HBSc HBSh HBPD HBSR/ HBO NHBW NHBO All Transit auto access Transit walk access School bus Walk Bike Shared ride Shared ride Drive alone Total Based on the data availability and the discussion above, the final set of modes for each trip purpose was determined as follows: JTW/HBU, HBSh, HBSR/HBO: Transit Auto Access, Transit Walk Access, Walk, Bicycle, Shared Ride 3+, Shared Ride 2, Drive Alone HBSc: Transit Auto Access, Transit Walk Access, Walk, School Bus, Shared Ride 3+, Shared Ride 2, Drive Alone HBPD: Non-Walk, Shared-ride 3+, Shared Ride 2, Drive Alone NHBW, NHBO: Transit Auto Access, Transit Walk Access, Walk, Shared Ride 3+, Shared Ride 2, Drive Alone Observation Exclusions There are a nearly 30,000 trip observations in the original combined data set as shown in Table It was necessary to remove from the data set observations for which required data was unavailable. The following criteria were used to exclude observations that could not be used for estimation: Origin or destination zone missing; Invalid chosen mode for model (school bus for non-school trips, mode not included in model); Insufficient observations of mode for mode choice model (e.g., only 7 bike observations in home based shopping purpose); or Chosen mode not available especially relevant for trips where respondents reported taking transit, but no transit route was available. Unavailability of Modes A series of criteria was established for modal availability prior to model estimation. Cambridge Systematics, Inc. 3-37

62 It was assumed that the auto modes were available to all travelers since respondents that do now own automobiles may use either shared car services or use rental cars to drive alone. Transit modes (walk or auto access) were available only where the transit level of service variables for the origin-destination pair were defined in the transit network skims. It was also assumed that the walk mode is not available if the trip highway distance is greater than five miles, and the bike mode is not available if the trip highway distance is greater than 10 miles. School bus is available only for the home based school trip purpose. Model Variables The following variables were considered for the mode choice utilities: Level of service variables 4 In-vehicle time (including walk access time) Out-of-vehicle time including the following: Transit out-of-vehicle times that include:» Walk access time,» Walk egress time,» Initial wait time,» Transfer wait time,» Transfer walk time, Walk time for the walk mode, Bicycle time for the bicycle mode Auto out-of-vehicle travel times include terminal times. These terminal times were already defined in the 3-bridge model network and were not adjusted. Cost including transit fare, auto parking cost, and auto operating cost Daily parking costs were updated from the previous Memphis model. The daily cost was assumed to apply for JTW trips. For all other trip purposes, the average activity durations were estimated from the household survey data and expressed as a fraction of a four-hour period 4 All time variables are in minutes, all cost variables in dollars, and all distance variables in miles Cambridge Systematics, Inc.

63 (it was assumed that daily parking costs were reached when a vehicle was parked for four hours). This fraction was applied to the daily parking cost to obtain the parking cost for each trip purpose. Auto operating costs were estimated at 25 cents per mile. Auto operating costs for shared ride modes were divided by the number of persons in the vehicle. All other level of service variables were estimated directly from the network skims. For transit walk access and egress, the assumed walk speed is 3 mph. The walk and bicycle times were estimated from the highway distance skims and assuming a speed of 3 mph for walk and 9 mph for bicycle. Other variables Indicator variables representing income levels were used in the utility functions for some trip purposes. For some trip purposes, variables representing the density of employment or population at the production or attraction zone were used. Model Estimation Results Table 3.27 through Table 3.33 show the estimated mode choice models for the seven trip purposes/combinations. For many of the trip purposes, the travel time coefficients were constrained to levels that are deemed acceptable by the Federal Transit Administration. After several iterations, a nesting structure that resulted in reasonable models to explain regional travel behavior were selected. Figure 3.1 shows the final nests for each purpose. Based on feedback received from TDOT, the models were adjusted to account for landform variables such as connected node density which is the ratio of the total intersections in a TAZ divided by the number of cul-de-sacs in that TAZ. The values of time were measured for different purposes, and modes (nonmotorized vs. motorized) for reasonableness. Values of time are highest for mandatory purposes journey-to-work, school, and work-based other purposes, which is very reasonable. Cambridge Systematics, Inc. 3-39

64 Figure 3-1 Model Nesting Structure Journey-to-Work, Home-based Social Recreation & Home-based University Root Drive Alone Shared Ride Transit Non- Motorized Drive Alone Shared Ride 2 Shared Ride 3+ Walk to Transit Drive to Transit Walk Bike Home-based Other, Home-based Shop, Non-Home Based Work, & Non-Home Based Non-Work Root Drive Alone Shared Ride Transit Non- Motorized Drive Alone Shared Ride 2 Shared Ride 3+ Walk to Transit Drive to Transit Walk 3-40 Cambridge Systematics, Inc.

65 Home-based Escort Root Drive Alone Shared Ride Non- Motorized Drive Alone Shared Ride 2 Shared Ride 3+ Walk Home-based School Root No Car Drive to Transit Shared Ride 2 Shared Ride 3+ Drive Alone Walk to Transit Walk School Bus Drive to Transit Shared Ride 2 Shared Ride 3+ Drive Alone Cambridge Systematics, Inc. 3-41

66 Table 3-27 Mode Choice Model for Journey to Work and Home Based University Trips Variable Mode Specific Constants Constant Level of Service Transit Auto Access (13.6) Transit Walk Access (11.1) Walk (2.66) Mode Alternative Bike (0.288) Drive Alone Shared Ride (-16.2) Shared Ride (-20.9) In-vehicle time, JTW * * * * * Out-of-vehicle time, JTW * * * * * In-vehicle time, HBU * * * * * Out-of-vehicle time, HBU * * * * * Non-motorized time (-12.5) (-12.5) Cost, JTW * * * * * Cost, HBU (-6.6) (-6.6) (-6.6) (-6.6) (-6.6) Stops on Journey 1+ stops 2+ stops $20,000-$35,000 $35,000-$100,000 $100,000+ Development Density Population density at production zone (-11.1) (-10.9) (-32.2) (-23.5) (-11.1) (-10.9) (-32.2) (-23.5) (-3.1) (-1.7) (-5.8) (-5.6) (-4.9) (-2.4) (-1.7) (-5.8) (-5.6) (12.9) (4.67) (-4.8) (-9.0) (-9.0) (-4.9) (9.8) (6.15) (-4.8) (-9.0) (-9.0) (-4.9) Connected Node Ratio Production End (-2.14) (-2.48) (-3.8) (-4.38) Attraction End (2.9) (3.3) (0.6) (1.95) t-statistics in parentheses * indicates constrained parameter All nesting parameters = 0.6 (constrained) Sampling bias coefficient estimate for transit walk access = (-2.1) (not used in application) Model Estimation Statistics: Log Likelihood at Convergence Log Likelihood at Constants Log Likelihood at Null Parameters Log Likelihood with No Model Rho Squared w.r.t. Constants Rho Squared w.r.t. Null Parameters Rho Squared w.r.t. No Model Cambridge Systematics, Inc.

67 Table 3-28 Mode Choice Model for Home Based School Trips Variable Mode Specific Constants Constant Transit Auto Access (7.2) Transit Walk Access (7.4) Walk (12.6) Mode Alternative School Bus (12.4) Drive Alone Shared Ride (7.0) Shared Ride 3+ Level of Service In-vehicle time * * (-9.0) * * * Out-of-vehicle time * * * * * Non-motorized time (-6.7) Cost $35,000-$100,000 $100,000+ Connected Node Ratio Production End Attraction End t-statistics in parentheses * indicates constrained parameter (-1.3) (-4.8) (-7.8) (-0.3) (2.1) Nesting parameter (non-car nest) = (-4.6) (-1.3) (-4.8) (-7.8) (-2.91) (3.2) (-5.0) (-8.5) (-3.3) (-1.7) (-4.3) (-9.0) (-1.3) Sampling bias coefficient estimate for transit walk access = (-4.3) (not used in application) Model Estimation Statistics: Log Likelihood at Convergence Log Likelihood at Constants Log Likelihood at Null Parameters Log Likelihood with No Model Rho Squared w.r.t. Constants Rho Squared w.r.t. Null Parameters Rho Squared w.r.t. No Model (-1.3) (-2.0) (-4.4) (6.9) (-1.3) (-2.0) (-4.4) Cambridge Systematics, Inc. 3-43

68 Table 3-29 Mode Choice Model for Home Based Shop Trips Variable Mode Specific Constants Constant Level of Service Transit Auto Access (-0.8) Transit Walk Access (1.0) Mode Alternative Walk (4.7) Drive Alone Shared Ride (-15.5) Shared Ride (-20.4) In-vehicle time * * * * * Out-of-vehicle time * * * * * Non-motorized time (-7.7) Cost $20,000-$35,000 $35,000-$100,000 $100,000+ Development Density Population density at production zone Employment density at attraction zone Connected Node Ratio Production End Attraction End t-statistics in parentheses * indicates constrained parameter (-6.6) (-3.8) (-8.6) (-8.6) (1.41) (1.34) All nesting parameters = 0.6 (constrained) (-6.6) (-3.8) (-8.6) (-8.6) (2.9) (3.0) (-0.86) (1.34) (-4.6) (-8.3) (-8.3) (-1.9) (-1.45) (-6.6) (-6.6) Sampling bias coefficient estimate for transit walk access = (-1.4) (not used in application) Model Estimation Statistics: Log Likelihood at Convergence Log Likelihood at Constants Log Likelihood at Null Parameters Log Likelihood with No Model Rho Squared w.r.t. Constants Rho Squared w.r.t. Null Parameters Rho Squared w.r.t. No Model (-6.6) 3-44 Cambridge Systematics, Inc.

69 Table 3-30 Mode Choice Model for Home Based Pickup/Dropoff Variable Mode Specific Constants Constant (-2.1) Level of Service Mode Alternative Walk Drive Alone Shared Ride 2 Shared Ride (2.2) (0.5) In-vehicle time * * * Non-motorized time (-5.3) Cost * * * $20,000-$35, (-2.4) $35,000-$100, (-2.4) $100, (-2.3) Development Density Population density at production zone (2.9) (-2.3) (-2.3) Connected Node Ratio Production End Attraction End t-statistics in parentheses * indicates constrained parameter (1.1) (0.4) Nesting parameter (shared ride) = 0.6 (constrained) Model Estimation Statistics: Log Likelihood at Convergence Log Likelihood at Constants Log Likelihood at Null Parameters Log Likelihood with No Model Rho Squared w.r.t. Constants Rho Squared w.r.t. Null Parameters Rho Squared w.r.t. No Model Cambridge Systematics, Inc. 3-45

70 Table 3-31 Mode Choice Model for Home Based Social-Recreational and Home Based Other Trips Variable Transit Auto Access Mode Specific Constants Constant (-3.7) Level of Service Transit Walk Access (1.8) Walk (2.9) Mode Alternative Bike (-7.1) Drive Alone Shared Ride (-7.6) Shared Ride (-2.1) In-vehicle time * * * * * Out-of-vehicle time * * * * * Non-motorized time (-19.8) (-19.8) Cost (-8.0) (-8.0) (-8.0) (-8.0) (-8.0) less than $20, (20.4) (20.4) (9.0) (9.0) $20,000-$35, (8.7) $100, (-2.9) Household Size Percentage of households in production zone with 1 person Development Density Population density at attraction zone (4.0) (8.7) (-2.9) (4.0) (-2.3) (-2.1) (-4.3) (-2.3) (-2.1) (-4.3) Connected Node Ratio Production End Attraction End (-0.8) (-2.1) (0.0) (-2.3) (-1.2) (0.2) 2.62 (-2.5) (-0.9) t-statistics in parentheses * indicates constrained parameter All nesting parameters = 0.6 (constrained) Sampling bias coefficient estimate for transit walk access = (-3.8) (not used in application) Model Estimation Statistics: Log Likelihood at Convergence Log Likelihood at Constants Log Likelihood at Null Parameters Log Likelihood with No Model Rho Squared w.r.t. Constants Rho Squared w.r.t. Null Parameters Rho Squared w.r.t. No Model Cambridge Systematics, Inc.

71 Table 3-32 Mode Choice Model for Non-Home Based Work Trips Variable Mode Specific Constants Transit Auto Access Constant (-2.5) Level of Service Transit Walk Access (-0.5) Mode Alternative Walk (-2.4) Drive Alone Shared Ride (-17.6) Shared Ride (-20.3) In-vehicle time * * * * * Out-of-vehicle time * * * * * Non-motorized time (-7.0) Cost (-2.6) less than $20, (14.0) $20,000-$35, (6.4) $100, (-1.3) Development Density Employment density at attraction zone (-2.6) (14.0) (6.4) (-1.3) (3.5) (-2.6) (-2.6) (-2.6) Retail employment density at attraction zone (1.8) Connected Node Ratio Production End Attraction End (-0.2) (-1.0) (0.31) (-1.0) (-2.6) (1.4) t-statistics in parentheses * indicates constrained parameter All nesting parameters = 0.6 (constrained) Sampling bias coefficient estimate for transit walk access = (-0.6) (not used in application) Model Estimation Statistics: Log Likelihood at Convergence Log Likelihood at Constants Log Likelihood at Null Parameters Log Likelihood with No Model Rho Squared w.r.t. Constants Rho Squared w.r.t. Null Parameters Rho Squared w.r.t. No Model Cambridge Systematics, Inc. 3-47

72 Table 3-33 Mode Choice Model for Non-Home Based Non-Work Trips Variable Transit Auto Access Mode Specific Constants Constant (-3.7) Level of Service Transit Walk Access (-0.1) Mode Alternative Walk (-5.3) Drive Alone Shared Ride (-5.0) Shared Ride (-14.1) In-vehicle time * * * * * Out-of-vehicle time * * * * * Non-motorized time (-9.7) Cost (-10.0) (-10.0) less than $20, (11.7) (11.7) (7.6) (-10.0) (-10.0) (-10.0) $20,000-$35, (2.6) Development Density Population density at production zone (2.2) (2.6) (2.2) (3.3) (2.0) Retail employment density at attraction zone (1.9) (-3.4) (-3.4) Connected Node Ratio Production End Attraction End (-0.6) (-3.0) (-3.2) (-3.9) (-3.3) (-0.4) t-statistics in parentheses * indicates constrained parameter All nesting parameters = 0.6 (constrained) Sampling bias coefficient estimate for transit walk access = (-1.6) (not used in application) Model Estimation Statistics: Log Likelihood at Convergence Log Likelihood at Constants Log Likelihood at Null Parameters Log Likelihood with No Model Rho Squared w.r.t. Constants Rho Squared w.r.t. Null Parameters Rho Squared w.r.t. No Model Cambridge Systematics, Inc.

73 3.8 TIME-OF-DAY CHOICE MODELS The time-of-day choice models were also calculated using the household travel survey. These models were aimed to address three questions: What are the peak periods of travel? What is the share of travel by purpose during individual time periods? What is the directionality of travel by purpose and time period? This section outlines the findings from the analysis of the household travel survey used to address the three questions listed above. Definition of Peak Periods The previous Memphis MPO model had four time periods: AM Peak: 6 AM 9 AM Mid-Day: 9 AM - 2 PM PM Peak: 2 PM 6 PM Overnight: 6 PM 6 AM The 2014 household travel survey was analyzed to identify peak and off-peak time periods (Figure 3.2). Figure 3-2 Temporal Distribution of Trips in the 2014 Household Survey 10% 9% 8% 7% 6% 5% 4% 3% 2% 1% 0% Cambridge Systematics, Inc. 3-49

74 AM Peak. The 6 AM 9 AM travel window accounts for nearly 21 percent of all travel in the region. Further, this travel window accounts for almost one-third of all journeyto-work travel. There is a significant drop in the travel in the hours preceding this time window, and in the hours that follow right after. Therefore, it was determined that the 6 AM 9 AM time period would continue to be the AM Peak period. PM Peak. The 2 PM 6 PM travel window carries nearly 32 percent of all travel in the region. The study team recommended keeping the 2 PM 6 PM window as the PM peak period. Off-Peak Period. The time window in the middle of the day that is between the two peaks is considered the mid-day period and the time window after the PM peak leading to the next AM peak is considered the overnight period. Travel Share by Time Period The survey data were analyzed to developed time-of-day factors for each time period (Table 3.34). A majority of journey-to-work trips happen during the two peak periods (67 percent) which is consistent with travel patterns observed across the country. Home-based escort (66 percent) and home-based school trips (83 percent) also occur predominantly during the two peak periods. Home-based non-mandatory travel happens is limited during the AM peak period, but is reasonably well-distributed across the rest of the day. Nearly 80 percent of all non-home based work trips happen during the midday period which is also consistent with lunch patterns observed for workers. Most non-home based non-work trips happen during the Mid-day and PM peak periods. Each of the percentages discussed in Table 3.34 were used to split the trip tables generated after trip generation/distribution into four separate time period specific trip tables. These time-period specific trip tables were used during mode choice model estimation Cambridge Systematics, Inc.

75 Table 3-34 Time-of-Day Person Trip Factors for Internal Trips Trip Purpose AM Peak Mid-Day PM Peak Overnight Total Journey-to-Work Chain Home-based School Home-based University Home-based Shop Home-based Escort Home-based Social Recreation Home-based Other Non-home based Work Non-home based Non-Work Total Convert P-A Trips to O-D Trips The trip tables generated in the model are in a production-attraction (P-A) format. Prior to assignment, it is necessary and vitally important to convert this P-A format to an origin-destination (O-D) format to ensure that the highways are loaded the right way (for e.g. inbound segments headed to downtown must be congested in the AM while outbound segments must be congested in the PM peak). Table 3.34 outlines the factors that must be applied to the P-A tables to convert into the O-D format. Separate factors are generated for each time period and purpose. These factors are applied after the application of the mode choice model. For the non-home based trip purposes, it is assumed that the directionality factors equal 50 percent in all time periods. A majority of travel in the AM peak period has an origin at home (production = origin). Home-based escort trips have the largest share of return-home trips in the AM peak (33 percent). In the PM peak, the exact opposite happens with a majority of trips traveling from a non-home location to home especially for the journey to work chain. Cambridge Systematics, Inc. 3-51

76 Table 3-35 Time-of-Day Directional Trip Factors (Origin = Production) Trip Purpose Direction AM Peak Mid-Day PM Peak Overnight Journey-to-Work Chain % from Home % to Home Home-based School % from Home % to Home Home-based University % from Home % to Home Home-based Shop % from Home % to Home Home-based Escort % from Home % to Home Home-based Social % from Home Recreation % to Home Home-based Other % from Home % to Home Non-home based Work N/A Non-home based Non-Work N/A AUTO OCCUPANCY FACTORS Prior to conducting highway assignment, it is important to convert passenger trip tables into vehicle-based trip tables. There are three auto modes in the Memphis MPO model. For the drive alone mode, by definition, the passenger occupancy factor is 1.0. Similarly, for the shared ride 2 mode, the passenger occupancy factor is 2.0 For the shared ride 3+ mode, the average passenger occupancy factor was calculated using the household travel survey data. Occupancy factors were calculated for each of the nine trip purposes. These factors are listed below: Journey-to-work chain 3.43 Home-based school 3.67 Home-based university 3.26 Non-home based work 3.59 Home-based shop 3.67 Home-based escort 3.87 Home-based social recreation 3.83 Home-based other 3.60 Non-home based non-work Cambridge Systematics, Inc.

77 3.10 TRUCK MODEL This chapter covers the development of the following specific submodels related to truck modeling: External truck trip generation External truck trip distribution Internal truck trip generation Internal truck trip distribution External Truck Models The external truck trip models estimate truck trips that have one end inside the model area and the other end outside. The main data source for the external truck models is the TRANSEARCH database obtained from TDOT. The information in this database was windowed to the Memphis model boundary. Table 3.36 shows the commodity groups that are used in the external truck models. Table 3-36 Commodity Groups for External Truck Models Commodity Group # SCTGs Daily Trucks Produced Internally (% of IE) Daily Trucks Attracted Internally (% of IE) Farm Products % 1,122 28% Food Products % 242 6% Sand and Gravel % % Gasoline & Fuel % 324 8% Chemicals % 47 1% Non-Durable % 134 3% Manufacture Clay, Concrete, Glass % 168 4% Durable Manufacture % 141 3% Waste % 74 2% Secondary and Mixed Freight & 50 1,859 48% % Total 3, % 4, % External Truck Trip Generation The external truck trip generation process required estimation of truck trip ends for both the internal and external ends of truck trips at the zone level. Internal zone truck trip ends were estimated as linear functions of employment by NAICS category. Production and attraction equations were estimated for each commodity group using linear regression models. Cambridge Systematics, Inc. 3-53

78 The initial estimation of rates for all commodity groups was calculated using the reported annual, converted to daily, truck trip ends from TRANSEARCH for the counties in the Memphis MPO modeling region. Acceptable regression equations with zero intercepts were found between internal truck productions and NAICS employment at county level, which was the most detailed geographic level reported in TRANSEARCH. The assumption is that rates developed at county (i.e. district) level can then be applied to same NAICS3 employment at TAZ level. North American Industry Classification System (NAICS) employment calculated using InfoGroup data served as the independent variables for the linear regression models. The estimated parameters are shown in Table 3.37 (productions) and Table 3.38 (attractions). These estimated coefficients will be applied to model employment and other explanatory variables (such as population in each TAZ). To confirm that these rates were reasonable, the estimated rates were applied to the employment at a zonal level and compared to the reported truck trips ends that originated in all Tennessee counties. For the proposed attraction rates there was substantial agreement for all commodity groups. For productions, in all but one Commodity Group there was substantial agreement between the resulting estimated and reported truck trip ends. The one exception for the production rates was for the Commodity Group that includes Secondary and Mixed Freight. For this commodity group, the rate was estimated using all Tennessee counties. This produced the rate of daily external truck productions per NAICS_42, wholesale, employees that is shown in Table 3.36, as well as the recommendation that 978 trucks/days be included as special generator external truck productions in Shelby County Cambridge Systematics, Inc.

79 Table 3-37 # Commodity Group External Truck Trip Production Rates for Internal Zones 2012 Daily Trucks Variable NAICS TRANSEARCH Estimated Coeff. Categories t- stat R 2 1 Farm Products Food Products Sand and Gravel , Gasoline & Fuel Chemicals Non-Durable Manufacture , Clay, Concrete, Glass Durable Manufacture Waste Total Secondary and Mixed 1,859 1, Freight * n/a 1.00 *Adjusted rates to be used with Special Generator productions. Table 3-38 # Commodity Group External Trip Attraction Equations for Internal Zones 2012 Daily Trucks Variable TRANSEARCH Estimated NAICS Categories Coeff. t-stat R 2 1 Farm Products 1,122 1,060 Pop Food Products Sand and Gravel 990 1,050 Pop Gasoline & Fuel Pop Chemicals Non-Durable Manufacture Clay, Concrete, Glass Pop Durable Manufacture Waste Secondary and Mixed Freight External Truck Trip Distribution The distribution of the External Internal (EI) and Internal External (IE) daily truck trips was estimated using a standard gravity model distribution. In this model, all internal-internal external-external zonal interchanges were constrained to be zero by using a k-factor of zero. For the internal portion of the EI/IE trips, the productions and attractions are developed using the rates shown in Table 3.37 and Table 3.38 and applied to the TAZs. Cambridge Systematics, Inc. 3-55

80 The productions and attractions at the external stations for the Memphis MPO model region was developed from TRANSEARCH. The development will be discussed in the next sub-section. At these external stations, the reported trucks represent not only the origins and destinations, but also the time travelled by trucks from all of North America in regions outside of the Memphis MPO model region until they reach the external stations. The time travelled within the region is computed using levels of service matrices from the Memphis MPO highway network. The impedance function in the gravity model requires a time which includes both time within the Memphis MPO model region, and also and the average time at those external stations for travel to and from the external zones. A windowing procedure that calculated average impedance between the external stations and all TRANSEARCH zones was calculated. This was done for each commodity group separately since different commodities come from different external regions. The resulting average time between all external TRANSEARCH zones and the external stations served as an input to the gravity model. The gravity model was then applied where the impedance between a Memphis TAZ and an external station was the addition of the time within the region, as found by skimming the Memphis MPO highway network, and the average time between that external station and the all external TRANSEARCH zones. External (Station) Truck Trips and External-External Truck Table. The truck volumes at the externals stations of the Memphis MPO model include not only the EI and IE trucks but also the External-External (E-E) trucks that pass through the Memphis MPO model region without stopping. The truck counts at the external stations include E-I, I-E and E-E travel. The truck counts were as reported by TDOT, Mississippi DOT and Arkansas DOT or from the daily tuck volumes reported in the Freight Analysis Framework 3.4 network for the links on which these externals stations were located. The TRANSEARCH table that was windowed to the Memphis MPO model region described was aggregated over all ten Commodity Groups serves as the initial seed matrix. An adaptation of FRATAR with the windowed TRANSEARCH tables as the seed table and the observed truck counts as constraints at the external stations was then run. This produces a truck trip table consistent with those counts Cambridge Systematics, Inc.

81 Travel between external stations represent the E-E truck table that will be used in the Memphis MPO model. The cells in the row of internal zones represent the portion of totals trucks at an external station that should be associated with internal attractions. Similarly the cells in the columns of internal zones represent the portion of total trucks at that that stations that are the external attractions at those stations that should be associated with internal productions. After the FRATAR process, TRANSEARCH trucks at each external station were adjusted to be consistent with observed trucks. This creates both the E- E truck table and the sum of the External truck productions and attractions at each external station. Using the share of truck by Commodity Group of total trucks, these external station truck productions and attractions were allocated to each Commodity Group. In addition to adjusting the windowed TRANSEARCH trucks to the observed trucks, the base year and forecast TRANSEARCH trucks windowed to the Memphis MPO model region were also used to develop growth factors that should be applied to the external portion of EI/IE trips, but also to the E- E truck table. These factors were calculated by comparing the TRANSEARCH future year daily trucks with the base year TRANSEARCH daily trucks for each Commodity Group at each external station. Internal Truck Models The internal model includes all trucks, not just the truck that carry freight. Therefore, the TDOT TRANSEARCH database is not appropriate to develop the internal truck model. Therefore, a different dataset that captures all truck movements within the region was used for the internal models. This section outlines the internal truck generation and distribution models. Internal Truck Trip Generation TDOT provided a trip table of heavy truck between Statewide Model (SWM) TAZs that was inferred from American Trucking Research Institute (ATRI) GPS records. This truck trip table does not include medium and light truck trip ends. The ATRI inferred truck trip ends for statewide model TAZs within the Memphis MPO model region were regressed against employment, and households from Memphis TAZs that were aggregated to SWM TAZs. The independent variables used in this regression were limited to the explanatory variables that were used in the Quick Response Freight Model (QRFM). The Quick Response Freight Manual (QRFM) includes default parameters for light, medium and heavy trucks. Cambridge Systematics, Inc. 3-57

82 The results of this regression provide a production rate for heavy duty truck trips. The relationship between the default QRFM and the new estimated parameters for heavy trucks was calculated. This relationship provides an explanation of local conditions. These same adjustment rates were then applied to the default QRFM rates for medium and light trucks to produce inferred rates for each class of trucks that could be used in the Memphis MPO model. As is conventional for internal truck models, including the QRFM, by definition, the rates shown in Table 3.39 apply for both truck productions and attractions. This includes the default QRFM rates as well as the rates calculated by a regression with Memphis ATRI. Table 3-39 Internal Truck Production/Attraction Rates Default QRFM vs. ATRI Regression QRFM Trucks ATRI trucks Variable Light Medium Heavy TN SWM TAZs R2=0.652 Agriculture, Mining and Construction Manufacturing, TCU and Wholesale Trade Retail Trade Office and Services Households The coefficients for office/service employment and households in a regression with the inferred ATRI truck trip ends resulted in negative or otherwise statistically inappropriate coefficients. These variables were excluded from further regressions and their coefficients were set to a minimal value. The comparison between the default QRFM rates for heavy, Combination Unit tractor trailer, truck and the ATRI regressions for these same trucks, was applied to the remaining QRFM truck types. The adjusted rates are shown in Table Table 3-40 Internal Truck Production/Attraction Rates Adjusted Rates Trucks per Day per Employee (Household) Variable Light Medium Heavy Agriculture, Mining and Construction Manufacturing, TCU and Wholesale Trade Retail Trade Office and Services Households Cambridge Systematics, Inc.

83 In addition to establishing proposed heavy truck trip generation rates, the regression of the ATRI truck trip ends with the explanatory data was used to identify special generators. Special generators are those locations where the explanatory variables alone do not explain all observed trip ends. The location and value of these special generators can be determined by examining outliers to the regression. The locations of those outliers, which were more than ½ of a Standard Deviation from the estimated regression line, are shown in Table Also shown is an estimate of the special generators that must be added at this location, defined as the difference between the inferred trips and ½ of a Standard Deviation from the estimated regression line. These locations were observed to all be associated with expected intermodal facilities that would be expected to generate truck trip ends in excess of explanatory employment at that facility. Table 3-41 Special Generator Locations SWM TAZ ATRI Inferred Estimated from Regression Potential Special Generator ,905 5,751 11, ,355 6,662 8, ,119 3,518 4,720 Facility BNSF Memphis IM Yard BNSF Memphis IM Yard BNSF Memphis IM Yard ,997 1,682 8,839 Port of Memphis Internal Truck Trip Distribution Truck trips in the QRFM are proposed to be distributed through the use of standard gravity model. The impedance function in that gravity model is a standard exponential decay function of travel time. In these functions, the parameter applied to the travel time is the negative inverse of the average travel time. The average travel time for ATRI internal-internal truck trips can be calculated from the product of the ATRI inferred truck trips and the travel times between TDOT SWM TAZs. The relationship between this calculated average travel times, converted to the coefficient of the impedance function of the gravity model, can be compared to the default coefficients in the QRFM. Cambridge Systematics, Inc. 3-59

84 As in the internal truck generation model, the calculated ATRI trip distribution parameter for heavy trucks was compared against the default QRFM parameter. That relationship was applied to the default QRFM coefficients for other truck types to calculate the proposed coefficients to the trip distribution impedance function for medium and light trucks. These are shown in Table Table Internal Trip Distribution Parameters QRFM Trucks ATRI trucks Category Coeff 1/ Coeff Coeff 1/ Coeff Light min min Medium min min Heavy min min It was observed that the adjustments to the impedance function required a considerable reduction to the default QRFM impedance function coefficient. It was also observed that the adjustments to the default QRFM truck trip generation rates required a consideration increase. To ensure that the combined use of these adjusted values is consistent with observed truck counts, an Origin Destination Matrix Estimation (ODME) was performed. This ODME process uses the internal-internal truck tables created as a result of using the proposed adjusted parameters as a seed and is constrained by observed truck counts on highway links internal to the Memphis region. The internal-internal truck table created using the proposed equations was consistent with the truck counts. The conclusion is that while the proposed trip generation rates might be too high and the proposed trip distribution impedance function might be too low, but this would result in a larger than expected number on intra-zonal truck trips. These intrazonal trips would never be assigned. Thus even though the internal model truck parameters might be too high/low, the resulting inter-zonal portion of the resulting truck trip table would still be consistent with observed truck counts and the use of the TDOT supplied ATRI inferred truck trips as an estimation dataset for internalinternal truck trips was justified Cambridge Systematics, Inc.

85 4.0 Model Validation One of the key aspects of the model validation task was to ensure that the updated travel demand model provided an accurate representation of regional travel patterns and behavior. The model calibration and validation process was conducted in a sequential fashion with each model (and purpose) being calibrated in the order in which it would be applied. The entire model was then re-run up to the step that was being calibrated before moving on to the next model. The model calibration sequence was consistent with the model application sequence in order to ensure that the calibrated results did not change with the adjustment of subsequent models. 4.1 DATA SOURCES USED FOR CALIBRATION Three separate data sources were used for the validation of the Memphis MPO model: The Household Travel Survey served as the primary dataset for calibration of all the major modeling components. The Transit On-board Survey data served as a key secondary dataset for the calibration of the trip mode choice and with the calibration of the transit components of the model. Regional Traffic Counts, provided by the Tennessee Department of Transportation, Mississippi Department of Transportation, and Arkansas Department of Transportation, served as the primary dataset for the calibration/validation of the highway assignment procedures. 4.2 MODEL CALIBRATION OVERVIEW Some validation summaries were prepared at the district level (see Figure 4-1 for a map of the 28 districts) while others were done at a more aggregate subregional level. The sub-regional level of comparison includes six sub-areas: Central Business District, Rest of City of Memphis, DeSoto County, Rest of Tennessee, Rest of Mississippi, and Crittenden County. The boundaries of these sub-areas can be seen in Figure 4-1 as they are, for the most part, aggregations of the 28 districts. The boundaries of the Central Business District, DeSoto County, and Crittenden County sub-areas are shown in Figure 4-1. The Rest of City of Memphis is represented by the following districts: Airport, East Memphis, Frayser, Hickory Hill, McKellar Lake, Midtown and Depot, North Memphis, Southwest Memphis, and University. The Rest of Tennessee comprises the following districts in Shelby County Collierville, East Shelby County, Millington, Northeast Shelby County, Northwest Shelby County, Raleigh Cambridge Systematics, Inc. 4-1

86 Bartlett, and Shelby Farms Germantown - plus all other counties in Tennessee. The Rest of Mississippi comprises all Mississippi Counties except DeSoto County. A customized reporting template was developed for the validation of the Memphis MPO model. This reporting template allowed comparison of the model results against survey data across several key dimensions including: Comparisons at a Regional and Sub-Regional level It must be noted that no household survey data collection occurred in Crittenden County. However, the model is applied in Crittenden County. Therefore, it is expected that there will be some differences in regional patterns between the model and the survey data. Table 4.1 presents an example of regional summaries from the destination choice model. Comparisons Using Socio-Demographic Data. Since the Memphis MPO model retains information about income and household size until the assignment procedures, it is possible and necessary to calibrate the model across these criteria. Comparisons using Network-Level Characteristics. Several network and level of service characteristics were used to calibrate the destination choice models. Travel times (and distributions); Travel distances (and distributions); District-district flows; and Intrazonal travel patterns. 4-2 Cambridge Systematics, Inc.

87 Figure 4-1 Detailed Memphis MPO Model Districts Cambridge Systematics, Inc. 4-3

88 Table 4-1 Destination Choice Model Sub-Regional Calibration Table All Trip Purposes (Model Output) CBD Memphis Rest of TN DeSoto Rest of MS Total CBD 45,837 32,752 5, ,744 Memphis 110, , ,577 28,808 5,860 1,059,056 Rest of TN 43, ,104 1,450,927 36,325 9,619 1,879,765 DeSoto 7,810 52,020 53, ,863 36, ,171 Rest of MS 1,071 6,547 16,671 23, , ,289 Total 208,916 1,119,825 1,750, , ,693 3,678,026 All Trip Purposes (Household Survey) CBD Memphis Rest of TN DeSoto Rest of MS Total CBD 22,629 25,620 9, ,998 Memphis 99, , ,218 20,003 3,743 1,090,259 Rest of TN 60, ,862 1,484,122 30,114 3,751 1,862,067 DeSoto 8,146 35,828 44, ,856 13, ,095 Rest of MS 992 5,266 16,106 25, , ,074 Total 191,836 1,116,020 1,756, , ,984 3,629,493 All Trip Purposes (Survey vs. Model % Difference) CBD Memphis Rest of TN DeSoto Rest of MS Total CBD 0.6% 0.2% -0.1% 0.0% 0.0% 0.7% Memphis 0.3% -2.3% 0.6% 0.2% 0.1% -1.2% Rest of TN -0.5% 1.4% -1.4% 0.2% 0.2% -0.2% DeSoto 0.0% 0.4% 0.2% -1.2% 0.6% 0.0% Rest of MS 0.0% 0.0% 0.0% -0.1% 0.8% 0.8% Total 0.4% -0.3% -0.8% -0.9% 1.6% 0.0% Separate calibration routines were run for each of the major trip purposes including trip generation, destination choice, mode choice, and traffic assignment. The rest of this section outlines the checks and validation procedures conducted for each of the model steps. 4.3 TRIP GENERATION CHECKS The trip generation model results are presented in Section 3.4. Three types of checks were conducted for the trip generation model. First, the model estimation results were compared to the previous Memphis model in the form of trips per household by purpose. These comparisons are 4-4 Cambridge Systematics, Inc.

89 summarized in Table 4.2. As this table shows, the results are similar 5. There are slightly more trips per household in the new model. Second, TDOT has guidelines for the expected number of trips per household. To make this comparison, the rate from the Memphis model had to be adjusted to provide a consistent basis for comparison, since the TDOT guidelines treat stops on the way to and from work as separate trips, and the Memphis model treats the journey to or from work as a single trip. The adjusted Memphis model trip rate is 8.3 trips per household while the TDOT range is 8.0 to 10.0 trips per household. TDOT also has guidelines for percentages for trips by purpose, with suggested ranges shown in Table 4.3. As the table shows, the Memphis model s percentages are mostly within the TDOT guidelines, and all trip purposes are close to the ranges. Table 4-2 Trips per Household Trip Purpose New Model Old Model Journey to Work Home Based School Home Based University Home Based Shopping Home Based Social-Recreation Home Based Escort Home Based Other Non-Home Based Work Non-Home Based Other Total There may be some definitional differences between the surveys used for the two models for the home based shopping, social-recreation, and other purposes Cambridge Systematics, Inc. 4-5

90 Table 4-3 Percentage Trips by Purpose Compared to TDOT Ranges Trip Purpose TDOT Low Range TDOT High Range Model Results Home Based Work 12% 24% 15% Home Based Shopping 10% 20% 9% Home Based Social-Recreation 9% 12% 10% Home Based School 5% 8% 10% Home Based Other 14% 28% 17% All Home Based Non-Work 45% 60% 54% Non-Home Based 20% 33% 30% The model results were then compared against TDOT s benchmarks. Results are included in Table 4.4. Overall, the model does reasonably well against TDOT s benchmarks with a couple of exceptions. First, the overall trip rate is slightly lower than TDOT benchmarks. This slight deviation can be explained by two key factors: (a) large percentage of low income households in the region, and (b) lower levels of car ownership than in other parts of the country. The average household size in the region is about 2.68 which is higher than statewide average of This could explain the slightly higher share of Home-based School trips in the Memphis model when compared to TDOT s benchmarks. Table 4-4 Aggregate Trip Rate Benchmark Statistic Benchmarks Memphis Low High Model Person Trips / TAZ N/A 15,000 2,420 Person Trips / Person Person Trips / DU (or household) HBW Person Trips / Employee Finally, the model results were compared against the household survey data that were used to estimate the models. Table 4.4 compares the percentages of trips by purpose for each income level and for total trips. The comparison shows a high degree of agreement between the model results and the survey. 4-6 Cambridge Systematics, Inc.

91 Table 4-5 Percentage Trips by Purpose Compared to Expanded Household Survey by Level Expanded Household Survey Trip Purpose <$20k $20k-$35k $35k-$100k $100k+ Total Journey to Work 16.1% 19.8% 25.0% 26.8% 23.2% Home Based School 14.1% 7.9% 8.4% 11.6% 10.0% Home Based University 3.7% 2.6% 1.4% 1.2% 1.9% Home Based Shopping 12.4% 11.4% 9.5% 7.3% 9.7% Home Based Social-Recreation 7.7% 11.0% 11.5% 10.9% 10.6% Home Based Escort 7.4% 8.3% 7.1% 7.5% 7.4% Home Based Other 12.2% 13.1% 11.8% 10.7% 11.8% Non-Home Based Work 1.4% 3.1% 5.8% 6.8% 4.9% Non-Home Based Other 25.0% 22.7% 19.4% 17.3% 20.3% Model Results Trip Purpose <$20k $20k-$35k $35k-$100k $100k+ Total Journey to Work 15.5% 19.3% 24.2% 27.0% 23.5% Home Based School 13.4% 11.8% 10.0% 8.9% 10.9% Home Based University 2.5% 2.1% 1.7% 1.5% 1.9% Home Based Shopping 12.1% 10.4% 9.3% 8.9% 10.2% Home Based Social-Recreation 8.2% 11.0% 11.2% 11.1% 11.0% Home Based Escort 9.8% 8.5% 7.0% 6.4% 7.8% Home Based Other 12.0% 12.4% 11.7% 11.5% 12.2% Non-Home Based Work 1.3% 3.0% 5.8% 6.7% 5.0% Non-Home Based Other 25.2% 21.4% 19.1% 18.1% 20.9% 4.4 DESTINATION CHOICE CALIBRATION The destination choice model is a key component of the travel demand model. Therefore, a series of checks were conducted on this model to ensure that the model matches household travel survey results. Model Framework Key aspects of the destination choice model include: The destination choice model is partially segmented by household income categories. Therefore, the model has the ability to capture differences in travel patterns of different household income segments. The destination choice models include distance variables as a means to capture the effects of levels of service on destination choice. The distance coefficients was adjusted during calibration to match household travel survey patterns. Cambridge Systematics, Inc. 4-7

92 In addition, the coefficients for intrazonal dummy variables that capture the effect of traveling within a TAZ were also adjusted to match overall travel distribution patterns. Calibration Process Adjustment of Coefficients The model results were compared against household survey data across several dimensions including: Average travel time by trip purpose and income segment; Intrazonal trips percentage by trip purpose and income segment; Travel time and travel distance histograms by trip purpose; and Travel patterns between different regions by trip purpose. The models were calibrated in an iterative fashion. Changes were made to the distance and intrazonal model coefficients in a controlled fashion and the model was re-run to produce output summaries that were compared with the household travel survey data. In total, nearly 20 iterations were run before the model was found to reasonably reproduce household survey patterns. Table 4.6 outlines the changes in the distance and intrazonal coefficients between the estimated models and the calibrated results. 4-8 Cambridge Systematics, Inc.

93 Table 4-6 Distance Coefficient Summary Trip Purpose Journey to Work Home Based School Home Based University Home Based Shopping Home Based Social- Recreation Home Based Escort Home Based Other Non-Home Based Work Non-Home Based Other Category Estimated Distance Coefficient Calibrated Distance Coefficient Estimated Intrazonal Coefficient Calibrated Intrazonal Coefficient < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k Cambridge Systematics, Inc. 4-9

94 Use of K-Factors K-factors are used in trip distribution models to adjust the number of trips between subareas of the model region. They are used in cases when travel time alone is not a good indicator of the propensity to travel between subregions. Common cases where K-factors are used in models include inter-jurisdictional trip exchanges and crossings of major rivers or other bodies of water. School activities are a primary example, as well as other activities where governmental policies may affect interstate travel (for example, banking or errands such as license renewals or library use). In many cases, even social circles are more likely to be within a state because some relationships may be based on friendships from schools or other institutions. For the Memphis model, K-factors were introduced for interstate movements. These factors reduce travel across state lines, which may represent the propensity to perform many activities within one s own state. K-factors were calibrated by comparing the model generated interstate movements with the observed interstate movements. The propensity of interstate movements varies by trip purposes and by income levels. Table 4-7 shows the K-factors used in the model Cambridge Systematics, Inc.

95 Table 4-7 Trip Purpose Journey to Work Home Based School Home Based University Home Based Shopping Home Based Social- Recreation Home Based Escort Home Based Other Non-Home Based Work Non-Home Based Other K-factors Category From MS to TN From TN to MS From TN to AR From AR to TN From MS to AR From AR to MS < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k < 20k k-35k k-100k > 100k Cambridge Systematics, Inc. 4-11

96 The model results for interstate trips, compared to the expanded household survey results are shown in Table 4.8. Trips to and from Arkansas are not shown because the household survey did not include Arkansas. These numbers are presented in production-attraction format. Table 4-8 Modeled Trips between Tennessee and Mississippi Compared to Expanded Household Survey Results Expanded Household Survey (Thousands) Tennessee Mississippi Total Tennessee 2, ,011 Mississippi Total 3, ,629 Model Results (Thousands) Tennessee Mississippi Total Tennessee 2, ,028 Mississippi Total 3, ,683 Calibration Results Results from the calibrated models are presented in this section. Table 4.9 and Table 4.10 compare average impedance and percentage intrazonal by trip purpose and household income. As a first step, the household survey data were assessed to ensure that they were reasonable and had a reasonable pattern. The distance coefficient and the intrazonal coefficient were adjusted until the modeled average impedance was comparable to the average travel impedance from the household travel survey. The percentage intrazonals from the survey were found to be higher than the results from the previous model. Therefore, a decision was made to match the patterns observed in the survey, but to not match the total intrazonals. Therefore, the model intrazonals are slightly lower than the survey intrazonals for every purpose. The calibrated results were shared with TDOT for review and finalized after their comments. Figure 4.2 outlines the summary of all the checks conducted during destination choice calibration for the journey-to-work purpose. Overall, the model represents observed data from the household survey reasonably well. Similarly, Figure 4.3 Figure 4.10 showcase the calibration comparisons conducted for the remaining eight trip purposes Cambridge Systematics, Inc.

97 Table 4-9 Expanded Household Survey Comparison of Average Impedance by Trip Purpose and Trip Purpose <$20k $20k-$35k $35k-$100k $100k+ Total Journey to Work Home Based School Home Based University Home Based Shopping Home Based Social-Recreation Home Based Escort Home Based Other Non-Home Based Work Non-Home Based Other Model Results Trip Purpose <$20k $20k-$35k $35k-$100k $100k+ Total Journey to Work Home Based School Home Based University Home Based Shopping Home Based Social-Recreation Home Based Escort Home Based Other Non-Home Based Work Non-Home Based Other Percentage Difference Trip Purpose <$20k $20k-$35k $35k-$100k $100k+ Total Journey to Work -2.9% -1.8% -0.8% -0.3% -0.9% Home Based School -2.9% -4.2% -2.2% -2.1% -3.7% Home Based University -8.1% -9.0% -9.7% -6.0% -4.1% Home Based Shopping -5.7% -4.2% -5.3% -2.8% -4.2% Home Based Social-Recreation -2.3% -1.6% -2.0% -0.8% -1.6% Home Based Escort -5.2% -6.0% -2.3% -1.6% -4.2% Home Based Other -3.4% -3.0% -3.8% -3.8% -3.5% Non-Home Based Work -1.7% -2.6% -2.0% -2.1% -2.1% Non-Home Based Other -2.9% -2.2% -2.5% -2.2% -2.4% Cambridge Systematics, Inc. 4-13

98 Table 4-10 Expanded Household Survey Comparison of Percentage Intrazonal by Trip Purpose and Trip Purpose <$20k $20k-$35k $35k-$100k $100k+ Total Journey to Work 1.9% 2.3% 1.8% 1.1% 1.7% Home Based School 11.2% 8.2% 8.1% 5.8% 8.2% Home Based University 3.0% 8.4% 1.3% 9.0% 4.3% Home Based Shopping 5.5% 7.3% 7.9% 4.5% 6.7% Home Based Social-Recreation 8.1% 12.5% 12.5% 7.6% 10.8% Home Based Escort 23.2% 11.0% 8.8% 6.2% 10.9% Home Based Other 6.7% 4.3% 2.7% 4.5% 4.0% Non-Home Based Work 24.4% 15.3% 13.3% 12.8% 13.9% Non-Home Based Other 10.4% 10.2% 13.7% 12.2% 12.2% Model Results Trip Purpose <$20k $20k-$35k $35k-$100k $100k+ Total Journey to Work 1.6% 2.0% 1.3% 0.8% 1.3% Home Based School 6.9% 6.6% 5.3% 5.5% 5.9% Home Based University 2.0% 2.4% 1.3% 2.0% 1.8% Home Based Shopping 5.2% 4.7% 4.8% 3.2% 4.6% Home Based Social-Recreation 9.5% 9.4% 10.5% 8.3% 9.7% Home Based Escort 12.1% 9.0% 7.1% 7.1% 8.5% Home Based Other 5.3% 3.3% 2.8% 3.5% 3.4% Non-Home Based Work 21.6% 15.0% 13.0% 12.5% 13.4% Non-Home Based Other 12.8% 10.7% 12.2% 10.5% 11.8% Percentage Difference Trip Purpose <$20k $20k-$35k $35k-$100k $100k+ Total Journey to Work 0.3% 0.3% 0.5% 0.3% 0.4% Home Based School 4.3% 1.6% 2.8% 0.3% 2.3% Home Based University 1.0% 6.0% 0.0% 7.0% 2.5% Home Based Shopping 0.3% 2.6% 3.1% 1.3% 2.1% Home Based Social-Recreation -1.4% 3.1% 2.0% -0.7% 1.1% Home Based Escort 11.1% 2.0% 1.7% -0.9% 2.4% Home Based Other 1.4% 1.0% -0.1% 1.0% 0.6% Non-Home Based Work 2.8% 0.3% 0.3% 0.3% 0.5% Non-Home Based Other -2.4% -0.5% 1.5% 1.7% 0.4% 4-14 Cambridge Systematics, Inc.

99 Cambridge Systematics, Inc Figure 4-2 F Journey-to-Work Purpose Destination Choice Calibration Avg. Impedance <$20k $20k - $35k $35k - $100k $100k+ Total Intrazonals <$20k $20k - $35k $35k - $100k $100k+ Total Survey Survey 1.9% 2.3% 1.8% 1.1% 1.7% Model Model 1.6% 2.0% 1.3% 0.8% 1.3% i g u r e Survey CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 4,971 3,740 3, ,694 Memphis 39, ,495 82,565 7, ,283 Rest of TN 37, , ,225 9,069 1, ,094 DeSoto 6,591 23,076 29,833 48,858 6, ,841 Rest of MS 896 3,219 8,210 6,152 15, ,356 Crittenden Home Based School Purpose Destination Choice Calibration Total 89, , ,432 71,856 24, ,269 Model CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 4,007 3,761 1, ,909 Memphis 31, ,611 79,532 10,576 3,584 6, ,876 Rest of TN 27, , ,610 18,246 5,652 8, ,884 DeSoto 5,497 23,340 25,976 36,876 12,721 2, ,409 Rest of MS 726 3,431 6,571 5,606 13, ,994 Crittenden 2,213 4,237 2, ,443 10,427 Total 69, , ,430 71,585 35,736 26, ,073 Memphis MPO Model Update

100 4-16 Cambridge Systematics, Inc. Avg. Impedance <$20k $20k - $35k $35k - $100k $100k+ Total Intrazonals <$20k $20k - $35k $35k - $100k $100k+ Total Survey Survey 11.2% 8.2% 8.1% 5.8% 8.2% Model Model 6.9% 6.6% 5.3% 5.5% 5.9% Survey CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 729 1, ,663 Memphis 5,645 83,608 7, ,094 Rest of TN , ,855 2, ,020 DeSoto , ,064 Rest of MS , ,524 Crittenden Total 6, , ,900 58,747 17, ,365 Figure 4-4 Home Based University Purpose Destination Choice Calibration Model CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 1,320 1, ,365 Memphis 4,557 85,891 10, ,130 Rest of TN , ,663 1, ,685 DeSoto 135 4,208 2,070 59,146 2, ,619 Rest of MS ,712 16, ,551 Crittenden 280 1, ,157 1,468 Total 6, , ,359 63,818 18, ,350 Memphis MPO Model Update

101 Cambridge Systematics, Inc Avg. Impedance <$20k $20k - $35k $35k - $100k $100k+ Total Intrazonals <$20k $20k - $35k $35k - $100k $100k+ Total Survey Survey 3.0% 8.4% 1.3% 9.0% 4.3% Model Model 2.0% 2.4% 1.3% 2.0% 1.8% Survey CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 1, ,161 Memphis 7,895 17,650 4, ,005 Rest of TN 3,577 8,073 15, ,452 DeSoto 0 1, , ,424 Rest of MS , ,229 Crittenden Total 12,907 28,407 20,524 2,520 1, ,271 Figure 4-5 Home Based Shopping Purpose Destination Choice Calibration Model CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 1, ,103 Memphis 6,906 15, ,172 Rest of TN 1,291 12,094 20, ,429 DeSoto , ,631 Rest of MS , ,877 Crittenden , Total 9,439 28,259 22,017 5,179 1, ,212 Memphis MPO Model Update

102 4-18 Cambridge Systematics, Inc. Avg. Impedance <$20k $20k - $35k $35k - $100k $100k+ Total Intrazonals <$20k $20k - $35k $35k - $100k $100k+ Total Survey Survey 5.5% 7.3% 7.9% 4.5% 6.7% Model Model 5.2% 4.7% 4.8% 3.2% 4.6% Survey CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 674 3,067 1, ,834 Memphis 3,494 87,436 10,116 2, ,717 Rest of TN , ,694 2, ,970 DeSoto ,388 42, ,042 Rest of MS ,240 5,704 9, ,270 Crittenden Total 5, , ,459 53,274 10, ,834 Figure 4-6 Home Based Social Recreation Purpose Destination Choice Calibration Model CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 2,926 1, ,088 Memphis 7,209 75,714 22,717 3, ,767 Rest of TN , ,744 1, ,573 DeSoto 117 2,746 5,659 39,580 1, ,377 Rest of MS ,964 1,853 13, ,445 Crittenden ,668 1,030 Total 11, , ,248 46,344 14, ,251 Memphis MPO Model Update

103 Cambridge Systematics, Inc Avg. Impedance <$20k $20k - $35k $35k - $100k $100k+ Total Intrazonals <$20k $20k - $35k $35k - $100k $100k+ Total Survey Survey 8.1% 12.5% 12.5% 7.6% 10.8% Model Model 9.5% 9.4% 10.5% 8.3% 9.7% Survey CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 1,400 1, ,568 Memphis 8,338 95,854 17,683 1,357 1, ,766 Rest of TN 1,927 19, ,071 2, ,631 DeSoto 0 1,756 1,876 41,563 1, ,068 Rest of MS ,417 15, ,093 Crittenden Total 11, , ,472 50,945 19, ,126 Model CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 2,717 1, ,393 Memphis 10,343 76,984 19,677 2, ,722 Rest of TN 2,605 31, ,536 2, ,532 DeSoto 335 3,877 3,433 42,583 4, ,168 Rest of MS ,131 2,018 15, ,779 Crittenden ,788 2,008 Total 16, , ,934 49,015 21,559 1, ,594 Memphis MPO Model Update

104 4-20 Cambridge Systematics, Inc. Figure 4-7 Home Based Escort Purpose Destination Choice Calibration Avg. Impedance <$20k $20k - $35k $35k - $100k $100k+ Total Intrazonals <$20k $20k - $35k $35k - $100k $100k+ Total Survey Survey 23.2% 11.0% 8.8% 6.2% 10.9% Model Model 12.1% 9.0% 7.1% 7.1% 8.5% Survey CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD ,211 Memphis 11,355 71,224 11, ,836 Rest of TN 2,344 21, ,748 1, ,133 DeSoto 0 1, , ,000 Rest of MS ,075 2, ,998 Crittenden Total 14,353 95, ,025 28,448 3, ,178 Model CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 2, ,129 Memphis 10,444 60,401 12,544 1, ,796 Rest of TN 3,064 24, ,854 1, ,126 DeSoto ,011 3, ,685 Rest of MS ,725 8, ,690 Crittenden , Total 15,797 87, ,508 31,260 12,812 1, ,426 Memphis MPO Model Update

105 Cambridge Systematics, Inc Figure 4-8 Home Based Other Purpose Destination Choice Calibration Avg. Impedance <$20k $20k - $35k $35k - $100k $100k+ Total Intrazonals <$20k $20k - $35k $35k - $100k $100k+ Total Survey Survey 6.7% 4.3% 2.7% 4.5% 4.0% Model Model 5.3% 3.3% 2.8% 3.5% 3.4% F i g u r e Survey CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 3,125 1, ,743 Memphis 10,342 94,114 20,743 1, ,618 Rest of TN 5,007 29, ,574 2, ,306 DeSoto 120 1,711 4,525 50,023 1, ,857 Rest of MS ,659 3,138 10, ,190 Crittenden Total 18, , ,233 57,571 12, , Non Home Based Work Purpose Destination Choice Calibration Model CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 3,693 2, ,974 Memphis 15,952 82,504 24,189 2, ,011 Rest of TN 3,650 35, ,738 2, ,620 DeSoto 695 6,969 5,670 41,682 7, ,372 Rest of MS ,007 2,342 16, ,740 Crittenden 757 1, ,466 2,457 Total 24, , ,800 49,152 25,716 1, ,718 Memphis MPO Model Update

106 4-22 Cambridge Systematics, Inc. Avg. Impedance <$20k $20k - $35k $35k - $100k $100k+ Total Intrazonals <$20k $20k - $35k $35k - $100k $100k+ Total Survey Survey 24.4% 15.3% 13.3% 12.8% 13.9% Model Model 21.6% 15.0% 13.0% 12.5% 13.4% Survey CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 4,549 3, ,499 Memphis 3,694 31,217 7,842 3, ,209 Rest of TN 2,973 13,375 79,526 2, ,864 DeSoto 942 1,316 1,272 8, ,376 Rest of MS , ,998 Crittenden Total 12,158 49,061 89,773 15,493 2, ,946 Figure 4-10 Non Home Based Other Purpose Destination Choice Calibration Model CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 9,593 4, ,660 Memphis 6,167 35,550 11,625 2, ,670 Rest of TN 1,216 13,842 66,311 2, ,411 DeSoto 242 1,968 2,495 10, ,625 Rest of MS , ,247 Crittenden ,282 1,409 Total 17,318 56,017 81,709 15,942 7,627 1, ,613 Memphis MPO Model Update

107 Cambridge Systematics, Inc Avg. Impedance <$20k $20k - $35k $35k - $100k $100k+ Total Intrazonals <$20k $20k - $35k $35k - $100k $100k+ Total Survey Survey 10.4% 10.2% 13.7% 12.2% 12.2% Model Model 12.8% 10.7% 12.2% 10.5% 11.8% Survey CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 5,092 9,553 2, ,625 Memphis 9, ,847 38,897 2,937 1, ,731 Rest of TN 5,488 42, ,719 8, ,596 DeSoto 59 4,233 3,900 91,097 2, ,421 Rest of MS 0 0 2,062 2,448 25, ,417 Crittenden Total 20, , , ,726 29, ,790 Model CBD Memphis Rest of TN DeSoto Rest of MS Crittenden Total CBD 18,393 16,361 1, ,122 Memphis 17, ,407 42,562 6, , ,912 Rest of TN 2,712 48, ,599 6,359 1, ,505 DeSoto 492 7,327 7,174 64,161 3, ,283 Rest of MS 90 1,202 2,371 5,597 40, ,967 Crittenden 844 2,755 1, ,011 5,213 Total 38, , ,682 82,609 45,788 3, ,790 Memphis MPO Model Update

108 Calibrated model results were compared against TDOT benchmarks to assess model performance. The average trip lengths in the model for each trip purpose fall within the benchmarks established by TDOT. The percentage intrazonals also fall within the prescribed guidelines except for a couple of trip purposes. Overall, the model does a good job in replicating survey reported travel purposes. Table 4-11 Average Trip Length and Frequencies by Purpose Statistic Benchmarks Memphis Low High Model Average Trip Length HBW (minutes) Average Trip Length HBSH (minutes) Average Trip Length HBSR (minutes) Average Trip Length HBSC (minutes) Average Trip Length HBO (minutes) Average Trip Length NHB (minutes) Standards Mean Trip Length, Observed Total Trips +/-3% -2% Trip Length Frequency Distribution versus observed +/-5% Table 4-12 Percent Intrazonal Trips Statistic Benchmarks Memphis Low High Model Percent Intrazonal HBW 1% 4% 1% Percent Intrazonal HBSH 3% 9% 5% Percent Intrazonal HBSR 4% 10% 10% Percent Intrazonal HBSC 10% 12% 6% Percent Intrazonal HBO 3% 7% 3% Percent Intrazonal NHB 5% 9% 12% Percent Intrazonal Total Trips 3% 5% 6% Standards Percent Intrazonal, Observed Total Trips +/-5% -1% 4.5 MODE CHOICE CALIBRATION The calibration of the mode choice model is very important for several reasons. First, it is critical to accurately capture mode splits in the model so the resulting model may be used for transit, bike-pedestrian, and highway corridor analysis studies Cambridge Systematics, Inc.

109 Second, the mode-specific person trips generated after the mode choice model are assigned to the model networks and used to assess regional congestion. Third, the calibrated mode choice models are used to forecast adoption of modes under several operating scenarios under consideration for the future year. Therefore, the study team implemented a detailed mode choice calibration approach so the model better represents observed patterns. As in the case of the destination choice model, the mode choice calibration was designed to test the performance of the model across several different dimensions. Model Framework Key aspects of the mode choice model include: The mode choice model is partially segmented by household income categories. Therefore, the model has the ability to capture differences in travel patterns of different household income segments. The mode choice models use level of service variables as key explanatory variables. Type of variables include: in-vehicle and out-of-vehicle travel time, travel distance, parking cost, auto operating costs, and transit fare. Mode choice models were formulated using a nested logit model structure. The model coefficients were coded to ensure that the nested structure was properly represented. The number of available options varied by trip purpose. The mode choice options are shown in Table Among all modes, bike had the fewest number of data records and therefore, in many purposes, bike is not a viable option. Based on a review of the models by TDOT, new variables such as connected node ratio were included in the final model formulation. Connected node ratio is calculated as the ratio of the total intersections in a TAZ divided by the number of cul-de-sacs in that TAZ. This variable is to be forecasted separately and cannot be obtained directly from the TAZ file. All other variables in the model are readily calculated from the TAZ file and level of service matrix. Cambridge Systematics, Inc. 4-25

110 Table 4-13 Mode Choice Availability by Trip Purpose Trip Purpose Drive Alone Shared Ride 2 Shared Ride 3+ Walk to Transit Drive to Transit Walk Bike School Bus Journey to Work Home Based School Home Based University Home Based Shopping Home Based Social- Recreation Home Based Escort Home Based Other Non-Home Based Work Non-Home Based Other Calibration Process The model results were compared against household survey data across trip purpose and income segment. Alternative specific constants that capture modal preference by income segment were adjusted. All other coefficients were left unchanged from the modeling process. The models were calibrated in an iterative fashion. Changes were made to only to the alternative specific constants in a controlled fashion and the model was rerun to produce output summaries that were compared with the household travel survey data. In total, nearly 20 iterations were run before the model was found to reasonably reproduce household survey patterns. During model estimation, only one constant was estimated for each mode and purpose combination except for the journey to work purpose where the constants were further segmented by income. During mode calibration, it was determined that separate constants would be estimated for every combination of mode, purpose, and income segment. This will allow the Memphis MPO to assess policy impacts at a fine-grained level. Calibration Results Table 4.14 Table 4.23 shows the original estimated constants as well as the calibrated constants to show the net impacts of model calibration. It must be noted that since the original estimation dataset for the mode choice model was an enriched sample that also used the transit on-board survey, the estimated constants were biased and showed a much higher than observed propensity for transit. The calibration process had a significant impact on the magnitude and sometimes sign of the modal constants Cambridge Systematics, Inc.

111 Figure 4.11 outlines the summary of mode choice calibration for the journeyto-work purpose. Overall, the model represents observed data from the household survey reasonably well. Figure 4.12 Figure 4.19 summarize results for all other trip purposes. Table 4-14 Estimated Constants Mode Estimated vs. Calibrated Alternative Specific Mode Choice Constants Journey to Work Purpose <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk Bike Calibrated Constants Mode <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk Bike Cambridge Systematics, Inc. 4-27

112 Table 4-15 Estimated Constants Mode Estimated vs. Calibrated Alternative Specific Mode Choice Constants Home-based School Purpose <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk School Bus Calibrated Constants Mode <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk School Bus Cambridge Systematics, Inc.

113 Table 4-16 Estimated Constants Mode Estimated vs. Calibrated Alternative Specific Mode Choice Constants Home-based University Purpose <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk Bike Calibrated Constants Mode <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk Bike Cambridge Systematics, Inc. 4-29

114 Table 4-17 Estimated Constants Mode Estimated vs. Calibrated Alternative Specific Mode Choice Constants Home-based Shop Purpose <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk Bike N/A N/A N/A N/A Calibrated Constants Mode <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk Bike N/A N/A N/A N/A 4-30 Cambridge Systematics, Inc.

115 Table 4-18 Estimated Constants Mode Estimated vs. Calibrated Alternative Specific Mode Choice Constants Home-Based Social-Recreation Purpose <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk Bike Calibrated Constants Mode <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk Bike Cambridge Systematics, Inc. 4-31

116 Table 4-19 Estimated Constants Mode Estimated vs. Calibrated Alternative Specific Mode Choice Constants Home-based Other Purpose <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk Bike Calibrated Constants Mode <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk Bike Cambridge Systematics, Inc.

117 Table 4-20 Estimated Constants Mode Estimated vs. Calibrated Alternative Specific Mode Choice Constants Home-based Escort Purpose <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit N/A N/A N/A N/A Drive to Transit N/A N/A N/A N/A Walk School Bus N/A N/A N/A N/A Calibrated Constants Mode <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit N/A N/A N/A N/A Drive to Transit N/A N/A N/A N/A Walk School Bus N/A N/A N/A N/A Cambridge Systematics, Inc. 4-33

118 Table 4-21 Estimated Constants Mode Estimated vs. Calibrated Alternative Specific Mode Choice Constants Non-Home based Work Purpose <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk Bike N/A N/A N/A N/A Calibrated Constants Mode <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk Bike N/A N/A N/A N/A 4-34 Cambridge Systematics, Inc.

119 Table 4-22 Estimated Constants Mode Estimated vs. Calibrated Alternative Specific Mode Choice Constants Non-Home based Non-Work Purpose <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk School Bus N/A N/A N/A N/A Calibrated Constants Mode <$ 20,000 $20,000 - $35,000 $35,000 - $100,000 > $100,000 Drive Alone Shared Ride Shared Ride Walk to Transit Drive to Transit Walk School Bus N/A N/A N/A N/A Cambridge Systematics, Inc. 4-35

120 4-36 Cambridge Systematics, Inc. Figure 4-11 Mode Choice Calibration Sheet for Journey-to-Work Purpose Mode- Survey < 20k 20k - 35k 35k - 100k > 100k Total Mode- Model < 20k 20k - 35k 35k - 100k > 100k Total Drive Alone 61.6% 72.1% 83.0% 85.0% 79.9% Drive Alone 60.1% 72.7% 83.0% 84.7% 79.7% Shared Ride % 16.3% 10.4% 9.7% 12.0% Shared Ride % 16.6% 11.0% 10.0% 12.4% Shared Ride % 6.0% 4.2% 4.7% 4.8% Shared Ride % 6.0% 4.3% 4.8% 5.0% Walk to Transit 3.9% 1.5% 0.2% 0.1% 0.7% Walk to Transit 4.4% 1.3% 0.1% 0.0% 0.7% Drive to Transit 1.6% 0.4% 0.1% 0.0% 0.3% Drive to Transit 2.3% 0.5% 0.1% 0.0% 0.3% Bike 0.7% 0.6% 0.7% 0.2% 0.6% Bike 1.5% 0.5% 0.5% 0.2% 0.5% Walk 4.9% 2.9% 1.3% 0.4% 1.6% Walk 4.3% 2.4% 1.0% 0.3% 1.3% School-Bus 0.0% 0.2% 0.0% 0.0% 0.0% School-Bus 0.0% 0.0% 0.0% 0.0% 0.0% Total 10.9% 12.0% 49.8% 27.3% 100.0% Total 10.7% 12.6% 50.4% 26.3% 100.0% 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Drive AloneShared RideShared Ride 2 3+ Walk to Transit Drive to Transit Bike Walk School-Bus < 20k 20k - 35k 35k - 100k > 100k Survey 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Drive Alone Shared Ride Shared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Model Bike Walk School-Bus Memphis MPO Model Update

121 Cambridge Systematics, Inc Figure 4-12 Mode Choice Calibration Sheet for Home Based School Purpose Mode- Survey < 20k 20k - 35k 35k - 100k > 100k Total Mode- Model < 20k 20k - 35k 35k - 100k > 100k Total Drive Alone 2.6% 2.8% 4.4% 9.2% 5.1% Drive Alone 1.7% 1.7% 5.0% 11.1% 4.9% Shared Ride % 18.8% 23.5% 34.8% 23.6% Shared Ride % 16.5% 24.8% 33.5% 23.5% Shared Ride % 25.4% 36.6% 32.1% 32.9% Shared Ride % 22.3% 38.8% 36.4% 32.4% Walk to Transit 0.4% 0.3% 0.1% 0.0% 0.1% Walk to Transit 0.3% 0.2% 0.0% 0.0% 0.1% Drive to Transit 0.3% 0.1% 0.0% 0.0% 0.1% Drive to Transit 0.2% 0.2% 0.0% 0.0% 0.1% Bike Bike 0.0% 0.0% 0.0% 0.0% 0.0% Walk 20.8% 5.3% 2.5% 4.8% 7.5% Walk 15.2% 14.8% 2.3% 3.9% 7.2% School-Bus 32.5% 47.3% 33.0% 19.1% 30.6% School-Bus 42.9% 44.3% 29.0% 15.1% 31.7% Total 22.3% 11.3% 38.8% 27.6% 100.0% Total 20.0% 16.5% 44.8% 18.7% 100.0% 50.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% Drive Alone Shared RideShared Ride 2 3+ Walk to Transit Drive to Transit Survey < 20k 20k - 35k 35k - 100k > 100k Bike Walk School-Bus 50.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% Drive Alone Shared Ride Shared Ride 2 3+ Walk to Transit Drive to Transit Model < 20k 20k - 35k 35k - 100k > 100k Bike Walk School-Bus Memphis MPO Model Update

122 4-38 Cambridge Systematics, Inc. Figure 4-13 Mode Choice Calibration Sheet for Home Based University Purpose Mode- Survey < 20k 20k - 35k 35k - 100k > 100k Total Mode- Model < 20k 20k - 35k 35k - 100k > 100k Total Drive Alone 45.9% 67.9% 66.5% 56.3% 59.4% Drive Alone 40.5% 60.7% 63.6% 62.4% 57.9% Shared Ride % 10.6% 18.3% 20.6% 17.8% Shared Ride % 13.8% 16.7% 19.1% 17.6% Shared Ride % 4.4% 14.5% 10.2% 11.7% Shared Ride % 7.4% 13.7% 10.8% 11.7% Walk to Transit 3.2% 1.6% 0.4% 0.6% 1.5% Walk to Transit 5.4% 0.9% 0.2% 0.1% 1.4% Drive to Transit 1.0% 0.8% 0.2% 0.0% 0.5% Drive to Transit 2.6% 0.6% 0.1% 0.0% 0.7% Bike 1.7% 0.0% 0.0% 0.0% 0.5% Bike 1.0% 0.6% 0.3% 0.1% 0.5% Walk 13.4% 14.6% 0.0% 12.3% 8.6% Walk 17.5% 16.0% 5.5% 7.4% 10.2% School-Bus 2.8% 0.0% 0.0% 0.0% School-Bus 0.0% 0.0% 0.0% 0.0% 0.0% Total 28.2% 19.9% 36.3% 15.6% 100.0% Total 21.5% 17.1% 43.5% 18.0% 100.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Drive Alone Shared RideShared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Survey Bike Walk School-Bus 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Drive Alone Shared Ride Shared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Model Bike Walk School-Bus Memphis MPO Model Update

123 Cambridge Systematics, Inc Figure 4-14 Mode Choice Calibration Sheet for Home Based Shopping Purpose Mode- Survey < 20k 20k - 35k 35k - 100k > 100k Total Mode- Model < 20k 20k - 35k 35k - 100k > 100k Total Drive Alone 37.0% 35.8% 42.0% 51.2% 41.6% Drive Alone 36.1% 37.5% 42.1% 49.1% 41.6% Shared Ride % 32.5% 27.5% 28.2% 28.1% Shared Ride % 30.2% 28.7% 29.0% 28.2% Shared Ride % 27.6% 29.9% 19.8% 26.0% Shared Ride % 28.1% 28.5% 21.2% 26.2% Walk to Transit 0.7% 0.2% 0.0% 0.0% 0.2% Walk to Transit 0.7% 0.2% 0.0% 0.0% 0.2% Drive to Transit 0.1% 0.1% 0.0% 0.0% 0.0% Drive to Transit 0.2% 0.1% 0.0% 0.0% 0.1% Bike Bike 0.0% 0.0% 0.0% 0.0% 0.0% Walk 15.2% 3.7% 0.6% 0.8% 4.1% Walk 13.6% 4.0% 0.7% 0.7% 3.7% School-Bus 0.0% School-Bus 0.0% 0.0% 0.0% 0.0% 0.0% Total 20.3% 16.4% 45.4% 17.9% 100.0% Total 19.4% 15.7% 44.9% 20.0% 100.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Drive Alone Shared RideShared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Survey Bike Walk School-Bus 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Drive Alone Shared Ride Shared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Model Bike Walk School-Bus Memphis MPO Model Update

124 4-40 Cambridge Systematics, Inc. Figure 4-15 Mode Choice Calibration Sheet for Home Based Other Purpose Mode- Survey < 20k 20k - 35k 35k - 100k > 100k Total Mode- Model < 20k 20k - 35k 35k - 100k > 100k Total Drive Alone 26.3% 39.6% 35.4% 45.5% 36.8% Drive Alone 32.9% 37.8% 37.9% 36.9% 36.9% Shared Ride % 34.9% 34.0% 31.0% 32.9% Shared Ride % 33.3% 33.6% 33.4% 32.7% Shared Ride % 20.6% 29.4% 21.8% 25.6% Shared Ride % 25.5% 25.6% 27.8% 25.5% Walk to Transit 2.6% 0.6% 0.1% 0.0% 0.6% Walk to Transit 3.0% 0.3% 0.2% 0.0% 0.6% Drive to Transit 1.0% 0.2% 0.1% 0.0% 0.2% Drive to Transit 0.6% 0.4% 0.1% 0.0% 0.2% Bike 0.6% 0.2% 0.1% 0.0% 0.2% Bike 0.3% 0.1% 0.1% 0.1% 0.2% Walk 14.8% 4.0% 0.9% 1.7% 3.8% Walk 12.1% 2.6% 2.4% 1.7% 3.9% School-Bus 0.0% 0.0% 0.0% 0.0% 0.0% School-Bus 0.0% 0.0% 0.0% 0.0% 0.0% Total 16.0% 15.6% 46.8% 21.6% 100.0% Total 16.1% 15.6% 46.8% 21.6% 100.0% 50.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% Drive Alone Shared Ride Shared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Survey Bike Walk School-Bus 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% Drive Alone Shared Ride Shared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Model Bike Walk School-Bus Memphis MPO Model Update

125 Cambridge Systematics, Inc Figure 4-16 Mode Choice Calibration Sheet for Non-Home Based Work Purpose Mode- Survey < 20k 20k - 35k 35k - 100k > 100k Total Mode- Model < 20k 20k - 35k 35k - 100k > 100k Total Drive Alone 69.6% 65.9% 72.9% 69.7% 71.1% Drive Alone 65.1% 70.9% 71.5% 71.6% 71.2% Shared Ride % 13.7% 15.6% 16.6% 15.6% Shared Ride % 15.6% 16.0% 16.0% 15.9% Shared Ride % 9.4% 4.4% 7.6% 5.8% Shared Ride % 5.8% 6.0% 6.0% 6.0% Walk to Transit 4.9% 0.7% 0.1% 0.0% 0.4% Walk to Transit 2.9% 0.8% 0.2% 0.1% 0.3% Drive to Transit 3.6% 0.4% 0.1% 0.1% 0.3% Drive to Transit 5.3% 0.2% 0.1% 0.1% 0.4% Bike Bike 0.0% 0.0% 0.0% 0.0% 0.0% Walk 6.3% 9.9% 6.9% 5.9% 6.8% Walk 6.3% 6.7% 6.2% 6.2% 6.3% School-Bus School-Bus 0.0% 0.0% 0.0% 0.0% 0.0% Total 4.9% 8.6% 54.1% 32.4% 100.0% Total 4.3% 9.1% 56.0% 30.6% 100.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Drive AloneShared RideShared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Survey Bike Walk School-Bus 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Drive Alone Shared Ride Shared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Model Bike Walk School-Bus Memphis MPO Model Update

126 4-42 Cambridge Systematics, Inc. Figure 4-17 Mode Choice Calibration Sheet for Non-Home Based Other Purpose Mode- Survey < 20k 20k - 35k 35k - 100k > 100k Total Mode- Model < 20k 20k - 35k 35k - 100k > 100k Total Drive Alone 22.5% 33.4% 31.1% 37.8% 31.1% Drive Alone 30.5% 29.9% 31.6% 30.9% 31.0% Shared Ride % 36.3% 34.2% 33.0% 32.3% Shared Ride % 32.6% 32.3% 32.3% 32.6% Shared Ride % 24.6% 32.9% 27.6% 32.3% Shared Ride % 34.4% 34.2% 35.1% 32.6% Walk to Transit 0.2% 0.1% 0.1% 0.1% 0.1% Walk to Transit 0.2% 0.1% 0.0% 0.0% 0.1% Drive to Transit 0.2% 0.0% 0.0% 0.0% 0.1% Drive to Transit 0.1% 0.1% 0.1% 0.1% 0.1% Bike Bike 0.0% 0.0% 0.0% 0.0% 0.0% Walk 11.3% 5.6% 1.6% 1.6% 4.1% Walk 10.9% 2.9% 1.8% 1.6% 3.7% School-Bus School-Bus 0.0% 0.0% 0.0% 0.0% 0.0% F Total 19.5% 15.9% 44.3% 20.3% 100.0% Total 19.7% 15.7% 44.8% 19.8% 100.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% i g u r e Drive AloneShared RideShared Ride 2 3+ Walk to Transit Drive to Transit M o d e Choice Calibration Sheet for Home Based Social-Recreation Purpose < 20k 20k - 35k 35k - 100k > 100k Survey Bike Walk School-Bus 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% Drive Alone Shared Ride Shared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Model Bike Walk School-Bus Memphis MPO Model Update

127 Cambridge Systematics, Inc Mode- Survey < 20k 20k - 35k 35k - 100k > 100k Total Mode- Model < 20k 20k - 35k 35k - 100k > 100k Total Drive Alone 25.8% 32.4% 29.3% 35.5% 30.8% Drive Alone 24.4% 29.4% 32.7% 32.7% 31.2% Shared Ride % 19.4% 20.2% 26.9% 22.1% Shared Ride % 19.9% 23.1% 23.1% 22.3% Shared Ride % 34.2% 38.7% 30.7% 35.2% Shared Ride % 39.4% 34.9% 35.9% 35.3% Walk to Transit 0.5% 0.1% 0.0% 0.0% 0.1% Walk to Transit 0.5% 0.1% 0.1% 0.0% 0.1% Drive to Transit 0.2% 0.1% 0.0% 0.0% 0.1% Drive to Transit 0.3% 0.1% 0.0% 0.0% 0.1% Bike 0.9% 2.1% 1.0% 0.8% 1.1% Bike 1.8% 1.4% 1.0% 1.0% 1.2% Walk 18.3% 11.7% 10.7% 6.1% 10.6% Walk 22.3% 9.7% 8.1% 7.4% 9.9% School-Bus 0.0% 0.0% 0.0% 0.0% 0.0% School-Bus 0.0% 0.0% 0.0% 0.0% 0.0% Total 11.7% 14.7% 49.6% 24.0% 100.0% Total 12.1% 15.2% 49.7% 23.0% 100.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% Drive AloneShared RideShared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Survey Bike Walk School-Bus Figure 4-19 Mode Choice Calibration Sheet for Home Based Escort Purpose 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% Drive Alone Shared RideShared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Model Bike Walk School-Bus Memphis MPO Model Update

128 4-44 Cambridge Systematics, Inc. Mode- Survey < 20k 20k - 35k 35k - 100k > 100k Total Mode- Model < 20k 20k - 35k 35k - 100k > 100k Total Drive Alone 28.4% 29.9% 28.9% 29.7% 29.2% Drive Alone 28.7% 30.5% 29.0% 29.6% 29.3% Shared Ride % 38.2% 26.7% 33.9% 29.7% Shared Ride % 29.4% 29.9% 31.3% 29.6% Shared Ride % 31.9% 41.2% 36.3% 36.6% Shared Ride % 35.6% 37.7% 38.8% 36.7% Walk to Transit 0.0% 0.0% 0.0% 0.0% 0.0% Walk to Transit 0.0% 0.0% 0.0% 0.0% 0.0% Drive to Transit 0.0% 0.0% 0.0% 0.0% 0.0% Drive to Transit 0.0% 0.0% 0.0% 0.0% 0.0% Bike 0.0% 0.0% 0.0% 0.0% 0.0% Bike 0.0% 0.0% 0.0% 0.0% 0.0% Walk 18.5% 0.0% 3.2% 0.2% 4.5% Walk 10.2% 4.4% 3.4% 0.4% 4.4% School-Bus School-Bus 0.0% 0.0% 0.0% 0.0% 0.0% Total 16.7% 15.9% 43.7% 23.7% 100.0% Total 20.4% 16.7% 44.2% 18.8% 100.0% 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% Drive Alone Shared RideShared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Survey Bike Walk School-Bus 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% Drive Alone Shared RideShared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Model Bike Walk School-Bus Memphis MPO Model Update

129 Cambridge Systematics, Inc Figure 4-20 Mode Choice Calibration Sheet for All Purposes Mode- Survey < 20k 20k - 35k 35k - 100k > 100k Total Mode- Model < 20k 20k - 35k 35k - 100k > 100k Total Drive Alone 31.9% 42.8% 47.4% 51.6% 45.3% Drive Alone 32.2% 38.5% 45.9% 49.3% 43.4% Shared Ride % 28.3% 23.9% 25.0% 24.7% Shared Ride % 24.9% 23.8% 23.8% 24.2% Shared Ride % 22.4% 25.3% 20.6% 24.2% Shared Ride % 24.9% 24.3% 23.0% 23.7% Walk to Transit 1.4% 0.5% 0.1% 0.0% 0.4% Walk to Transit 1.4% 0.4% 0.1% 0.0% 0.3% Drive to Transit 0.6% 0.2% 0.1% 0.0% 0.2% Drive to Transit 0.7% 0.2% 0.1% 0.0% 0.2% Bike 0.6% 0.5% 0.4% 0.2% 0.4% Bike 0.4% 0.3% 0.3% 0.2% 0.3% Walk 14.3% 5.4% 2.9% 2.4% 4.9% Walk 11.9% 5.5% 2.8% 2.3% 4.5% School-Bus School-Bus 5.7% 5.2% 2.9% 1.3% 3.3% Total 15.6% 14.0% 46.5% 24.0% 100.0% Total 15.7% 14.8% 47.3% 22.1% 100.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Drive AloneShared RideShared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Survey Bike Walk School-Bus 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% Drive Alone Shared Ride Shared Ride 2 3+ Walk to Transit Drive to Transit < 20k 20k - 35k 35k - 100k > 100k Model Bike Walk School-Bus Memphis MPO Model Update

130 4.6 HIGHWAY ASSIGNMENT CALIBRATION Highway traffic assignment validation was a critical element of the whole validation process. Since a majority of travel in the region happens using auto, it is essential the model matches observed traffic patterns and counts within the parameters established for highway validation. The highway validation consisted of three key steps: Incorporate traffic counts from Tennessee DOT, Mississippi DOT, and Arkansas DOT into the model network. Compare the model reported volumes against those reported in the traffic counts database. Code screenlines and bridge crossings in the model network and compare metrics such as vehicle-miles traveled (VMT) on these major crossings. Traffic Count Locations Count data were obtained using web-based portal data from TDOT and using GIS-data sharing from other DOTs. The count data were snapped to the model network and exhaustive manual checks were conducted to ensure that the counts were snapped to the appropriate link in the model network. After the manual checks, traffic counts from nearly 1,600 locations were found to have been snapped to the correct location. The traffic count locations were well distributed by roadway facility and area type. Figure 4-21 shows the traffic count locations on the highway network. It is apparent that the count locations are pretty diverse and are distributed throughout the region. In addition, the traffic counts coded into the network were also well distributed across the screenlines identified for calibration. Figure 4-22 shows the screenlines on the highway network Cambridge Systematics, Inc.

131 Figure 4.21 Traffic Count Locations Table 4-23 Distribution of Traffic Counts by Facility Type Facility Type Urban Locations Rural Locations Interstate Major Arterial Minor Arterial Collector Other Total 520 1,048 Cambridge Systematics, Inc. 4-47

132 Figure 4-22 Screenlines Model vs. Count Location Comparisons The model results were compared against the counts across two dimensions traffic volume and traffic VMT. Both passenger and traffic volumes were included in the model results since the traffic counts also include all types of vehicular traffic. Key findings include: Total observed volume on the count locations is 14.5 million while the model volumes on the same locations equal 13.1 million (9 percent difference). The coefficient of determination (R 2 ) comparing the modeled volumes to traffic counts is Cambridge Systematics, Inc.

133 Table 4.24 outlines the volume and VMT comparisons between observed and model data by facility type. On the major interstate and expressway facilities, the model volumes are within 5 percent of observed count volumes in urban areas and 2 percent in rural areas and the VMT difference is only 3 percent in urban areas and almost 0 percent in rural areas. The FHWA Target for VMT difference for such facilities is 7 percent. On the other major facilities such as major arterials, the VMT difference is only 5 percent in urban areas and 9 percent in rural areas compared to an FHWA target of 10 percent. No adjustments were made to the volume delay functions since the model replicates regional behavior reasonably well. Total VMT in the region is about 33 million vehicle-miles. Based on information provided by Memphis MPO, this number is slightly lower than the VMT observed in the HPMS. Table 4-24 Model vs. Observed Count Data Facility Type URBAN Observed Data Model Data Comparison Volume VMT Volume VMT % Diff Volume % Diff VMT Interstate 224, , , ,422 5% -3% Major Arterial 1,040, ,887 1,163, ,397 12% 5% Minor Arterial 318, , , ,761 16% 20% Collector 328, , , ,244 6% 11% Other 326, , , ,976 6% 9% Total 2,238,864 2,126,799 2,463,194 1,983,489 10% 7% RURAL Interstate 2,990,420 2,175,252 2,940,010 2,176,690-2% 0% Major Arterial 4,004, ,893 3,700, ,421-8% -9% Minor Arterial 1,123, , , ,300-28% -20% Collector 3,897, ,014 3,072, ,252-21% -19% Other 254,075 86, ,416 65,179-26% -24% Total 12,269,683 4,256,550 10,707,763 3,935,842-13% -8% Table 4.25 outlines the model performance at the screenline locations. As the description of the screenlines suggests, the screenlines tested for the model were pretty broad in coverage and include some of the most important crossings, including the river crossing between Tennessee and Arkansas. Further, the I-40 Cambridge Systematics, Inc. 4-49

134 cutline captures movements across the most important feeder roadway in the urban portion of the model. In addition to the results described in Table 4.25 a few smaller screenlines were also evaluated as part of the model testing for subareas, but not included in the final report. At the Mississippi river crossing that separates Arkansas from Tennessee, the model does a very good job in replicating traffic counts with the model vs. observed difference fewer than 1,500 vehicles. On the high volume cutline for the I-40/I-240 loop, the model again does a great job in replicating observed volumes and counts. Observed values are about 1.23 million while the observed values are 1.18 million (difference of only 4 percent). For the Mississippi-Tennessee cutline, also known as the State Line Cutline, the model predicts about 40,000 fewer vehicle trips than observed counts. However, the percent difference is only 12 percent, which is within the boundaries of reasonableness that we have observed in work in other areas. Similar observations were made in the East-West Screenline. Table 4-25 Model vs. Observed Count Data at Screenlines Screenline/Cutline Location Modeled Volumes Observed Counts Percentage Difference North-South Screenline 564, ,669 2% East-West Screenline 497, ,278-14% I-40/I-240 Cutline 1,182,564 1,225,925-4% State Line Cutline 269, ,539-12% Mississippi River Screenline 56,895 54,743 4% TDOT Guideline Comparisons TDOT guidelines have several recommended comparisons in the highway assignment section. Each of these comparisons is included here in the tables that follow. Across each of the comparisons, the Memphis MPO model falls within each of the guidelines. Some detailed comparisons are included below. Volume-to-Count Ratios. The model ranges are well under the prescribed guidelines for the preferred category. Percentage Differences and Errors for Volume Groupings. The model results are well within the prescribed ranges for the preferred category. This is especially true for the highest volume categories where the model does an exceptional job in replicating count volumes. Urban VMT by Facility Type. Model results are at the higher end of the prescribed ranges, but nothing appears to be deviating from the prescribed ranges Cambridge Systematics, Inc.

135 Modeled vs. Observed VMT. The model does a great job in replicating the VMT results developed by using count data. RMSE Comparisons. For volume groupings, the model does an acceptable job in meeting TDOT guidelines. For RMSE comparisons by functional class, the model does a great job across all facilities except principal arterials. Table 4-26 Volume-to-Count Ratios and Percent Error Statistic Standards Memphis Acceptable Preferable Model Freeway Volume-to-Count +/-7% +/-6% 0% Arterial Volume-to-Count +/-15% +/-10% -5% Collector Volume-to-Count +/-25% +/-20% -4% One way/frontage Road Volume-to-Count +/-25% +/-20% -3% 1 - "Other" Table 4-27 Percent Difference Targets for Daily Volumes Groupings Average Annual Daily Traffic Desirable Percent Deviation Memphis FHWA Michigan Model < 1, % 1,000 2, % 2,500 5, % 5,000 10, % 10,000 25, % 25,000 50, % > 50, % Table 4-28 Percent Error by Volume Group and Roadway Designs Statistic Standards Memphis Acceptable Preferable Model Percent Error: LT 10,000 Volume (2L road) 50% 25% 6% Percent Error: 10,000-30,000 (4L road) 30% 20% 12% Percent Error: 30,000-50,000 (6L road) 25% 15% 6% Percent Error: 50,000-65,000 (4-6L freeway) 20% 10% 4% Percent Error: 65,000-75,000 (6L freeway) 15% 5% 10% Percent Error: GT 75,000 (8+L freeway) 10% 5% 11% 2 - total of 7 links 3 - total of 2 links Cambridge Systematics, Inc. 4-51

136 Table 4-29 Urban Area VMT by Facility Type Facility Type Urban Area Population Memphis Small Medium Large Model (50-200K) (200K-1M) (>1M) Freeways/Expressways 18-23% 33-38% 40% 41% Principal Arterials 37-43% 27-33% 27% 28% Minor Arterials 25-28% 18-22% 18-22% 19% Collectors 12-15% 8-12% 8-12% 8% Table 4-30 Modeled Versus Observed VMT Stratification Memphis Functional Class Acceptable Preferable Model Freeways/Expressways ±7% ±6% 0% Principal Arterials ±15% ±10% -1% Minor Arterials ±15% ±10% -9% Collectors ±25% ±20% -4% All Links ±5% ±2% -3% Table 4-31 Root Mean Square Error (RMSE) By Volume Group Statistic Standards Memphis Acceptable Preferable Model RMSE: LT 5,000 VPD 100% 45% 85% RMSE: 5,000-9,999 VPD 45% 35% 47% RMSE: 10,000-14,999 VPD 35% 27% 35% RMSE: 15,000-19,999 VPD 30% 25% 37% RMSE: 20,000-29,999 VPD 27% 15% 27% RMSE: 30,000-49,999 VPD 25% 15% 17% RMSE: 50,000-59,999 VPD 20% 10% 12% RMSE: 60,000+ VPD 19% 10% 16% RMSE Areawide 45% 35% 43% Table 4-32 Root Mean Square Error (RMSE) By Functional Class Functional Type Small Large Memphis Regions Regions Model Freeways 20% 20% 18% Principal Arterials 30% 35% 38% Minor Arterials 40% 50% 45% Collectors 70% 90% 49% 4-52 Cambridge Systematics, Inc.

137 4.7 TRANSIT ASSIGNMENT CALIBRATION Transit market shares in the region are very low and constitute less than 0.75 percent of total travel in the region. Therefore, while modeling transit is important from an equity and policy analysis standpoint, it is difficult to capture a lot of details in the model given the current trip-based format. Further, transit assignment validation was not straightforward because of substantial changes in the MATA route definitions since the model base year of The bus routes in the 2010 model are different than the current routes for which ridership counts were available. Some routes were discontinued since 2010, others were added since then, some routes were extended or truncated, and others were rerouted. Owing to this reason, the on-board survey was not assigned to the transit network. Since modeled to observed ridership comparisons could not be done on a route by route basis, ridership was compared on a route group basis, with route groups defined by geography. Four segments were defined: downtown, crosstowns, trolley, and West Memphis. Table 4.33 shows the groups for the routes for which ridership totals were available. Table 4.34 shows the comparison between observed and modeled boardings by route group. The modeled boardings were adjusted to reflect 2013 conditions, the year of the observed ridership numbers. Cambridge Systematics, Inc. 4-53

138 Table 4-32 Route Groups Route Name Route group 2 Madison Downtown 4 Walker Downtown 5 Central Downtown 6 Northaven Downtown 7 Air Park Downtown 8 Chelsea Downtown 9 Highland Crosstown 11 Thomas Downtown 12 Florida Downtown 13 Lauderdale Downtown 15 President's Island Downtown 17 McLemore Downtown 19 Vollintine Downtown 20 Bellevue / Winchester Downtown 30 Brooks Crosstown 32 East Parkway Crosstown 34 Walnut Grove Downtown 35 South Parkway Crosstown 36 Hacks Cross Downtown 37 Perkins Downtown 38 Boxtown / Westwood Downtown 39 S. Third Downtown 40 Wolchase Downtown 42 Crosstown Crosstown 46 Whithaven Flyer Downtown 50 Poplar Downtown 52 Jackson Downtown 53 Summer Downtown 56 Lamar Downtown 57 Park Downtown 69 Winchester Crosstown 75 Broadway / Ingram West Memphis 77 West Memphis West Memphis 78 West Memphis Express West Memphis 82 Germantown Crosstown 100 Trolley Main Line Trolley 101 Trolley Riverfront Trolley 102 Trolley Madison Line Trolley 4-54 Cambridge Systematics, Inc.

139 Table 4-33 Comparison of Modeled and Observed Ridership by Route Group Route Group Observed Model Downtown 20,125 15,041 Crosstown 4,997 5,347 Trolley 3,041 4,254 West Memphis Total 28,781 24,945 Cambridge Systematics, Inc. 4-55

140

141 5.0 Future Year Forecasting One of the key advantages of a travel demand model is that it can be used to forecast future year traffic conditions. For the Memphis MPO study, the calibrated travel demand model was used to run three future year scenarios for the year Existing highway and transit networks with future year socio-demographic data. Existing and committed projects with future year socio-demographic data. Existing, committed, and regional transportation plan (RTP) projects with future year socio-demographic data. In addition to the three model runs, the model team also developed all the inputs necessary for several other model years. This section outlines the key steps undertaken to conduct future year scenario testing and also presents some of the key model outputs. 5.1 FUTURE YEAR DEMAND DATA The Memphis MPO model has been streamlined for future year model runs. Only two input files must be updated in order to set up future year model runs. These include: Socio-demographic data that serve as inputs to the TAZ file; and External count data that serve as key inputs to the external model and the freight model. Socio-Demographic Data Forecasts A stand-alone land use model was also developed by Memphis MPO. This model produced TAZ-level forecasts for the following variables: Total number of households; Total population; and Seven employment categories Industrial/Manufacturing Wholesale/Transportation Office Service Retail Cambridge Systematics, Inc. 5-1

142 Government Other These data serve as control totals for future year socio-economic data. However, the data produced by these forecasts do not provide all the details necessary for the travel demand model. So, CS staff developed spreadsheet-based routines that took the control totals developed by the land use model, and utilized demographic and employment distributions from the 2010 TAZ file to develop all socio-demographic cross-tabulations and all employment summaries required for both the freight and passenger models. Socio-demographic data forecasts were developed for the following model years: 2017, 2020, 2023, 2025, 2030, 2035, 2040, 2045, and If more updated forecasts are available from the land-use model in the future, then the spreadsheet tabulations may be used to update the socio-demographic inputs for the travel demand model. External Data Forecasts Traffic counts at external stations are required for the freight and for the external model. The following methodology was employed to calculate the external counts for each of the future year model runs. The previous MPO travel demand model update had projected growth rates between 2-4 percent per year for auto counts at external stations. Given that we have expanded the model boundary, the study team anticipated that the growth rates at external stations will be slightly lower than before. An annual growth rate of 1.5 percent for auto was incorporated at all external stations. For truck counts, TRANSEARCH data provides estimates of future year traffic movements. This information is available only for the largest traffic stations. The growth varies between 2-5 percent. For smaller stations, the growth rate was assumed to be 5 percent. Figure 5.1 highlights the external stations in the region. Table 5.1 outlines the growth rates for some of the key locations. Growth rates for all other locations was fixed at 1.5 percent for auto and 5 percent for truck. 5-2 Cambridge Systematics, Inc.

143 Figure 5.1 External Station Locations Table 5.1 External Station Growth Rates for Key Stations Station ID Truck Growth Rate Auto Growth Rate Station ID Truck Growth Rate Auto Growth Rate % 1.5% % 1.5% % 1.5% % 1.5% % 1.5% % 1.5% % 1.5% % 1.5% % 1.5% % 1.5% % 1.5% % 1.5% % 1.5% % 1.5% % 1.5% % 1.5% % 1.5% All Other 5% 1.5% Cambridge Systematics, Inc. 5-3

144 5.2 FUTURE YEAR TRANSPORTATION NETWORKS The study team also streamlined the process of updating the transportation networks within the model framework. First, detailed descriptions of the projects were obtained from the Memphis MPO and included in a stand-alone file. Second, the opening (and closing) year of each of these projects were also included in this file. Third, the network attributes that must be modified from a modeling perspective were identified. Changes were made to the appropriate attributes for each project to reflect reality. Fourth, a unique project identifier was included in the stand-alone file. Finally, the network links that are affected by the projects were identified and the appropriate project identifier is assigned to each of the links. The model script is designed to: Look for the model year; Identify the projects that become active for the selected model year using the starting date field; and Use the project identifier to update the link attributes in the model network. By using this methodology, only one model network may be used for all future year model runs, including the RTP model runs. In total, nearly 350 projects were coded as part of the future year model network development. 5.3 FUTURE YEAR MODEL RUNS Three model runs were conducted for the 2040 model year. Results from the three model runs were assessed for reasonableness and extensive QA/QC was conducted on the model networks to ensure that the model results were reasonable. In this report, the 2010 model results are compared against the 2040 model results with no committed projects. Detailed comparisons are available in the accompanying spreadsheets titled Comparisons 2010 vs xlsx. In total, the regional population is forecast to increase by 23 percent. Therefore, one of the first checks conducted for the future year model run was to see whether the growth in total trips was comparable to the growth in socio-demographics. As expected, person trips grew by about 24 percent (Table 5.2). Since the demographic growth is not uniform across the region, the study team also checked to see the growth in O-D patterns across different regions 5-4 Cambridge Systematics, Inc.

145 to ensure that the growth in trips was consistent to the growth in sociodemographics in that region (Table 5.3). DeSoto County is the fastest growing region in the study area. This region also has the highest growth in trip rates. Similarly, Crittenden County that has limited population growth forecast is observed to have minimal changes in trips produced. It is also observed that the growth rate in trips is slightly higher for higher income households than lower income households. This suggests that the growth of population in the region is slightly skewed towards areas that have higher income (Table 5.4). Table 5-2 Growth in Socio-Demographics by County 2010 vs County 2010 Household 2010 Employment 2040 Household 2040 Employment Household Growth Employment Growth Crittenden 19,026 19,799 19,069 29,780 0% 50% DeSoto 57,734 54, , ,355 81% 109% Fayette 14,505 8,727 19,995 19,629 38% 125% Marshall 13,369 7,700 15,990 13,281 20% 72% Shelby 350, , , ,777 12% 44% Tate 10,035 6,771 14,809 11,659 48% 72% Tipton 21,629 12,129 31,566 16,247 46% 34% Tunica 3,927 14,354 6,264 24,907 60% 74% Total 491, , , ,635 23% 52% Table 5-3 Growth in Passenger Trips by Region 2010 vs County CBD Rest of Memphis Rest of TN DeSoto Rest of MS Crittenden CBD 2.8% 26.4% 5.3% 36.2% 0.1% 15.6% 12.4% Rest of Memphis 6.3% 23.3% 1.4% 22.3% -8.5% 16.8% 16.7% Rest of TN 7.9% 26.1% 11.4% 27.2% 18.8% 23.6% 14.4% DeSoto 64.4% 92.9% 49.5% 83.5% 81.2% 84.3% 80.3% Rest of MS 25.4% 39.1% 8.6% 66.1% 34.3% 30.3% 36.1% Crittenden -3.7% 15.9% -9.9% 9.6% -27.5% 2.8% 3.1% Total 7.8% 27.5% 11.2% 73.0% 41.1% 8.9% 23.9% Total Cambridge Systematics, Inc. 5-5

146 Table 5-4 Growth in Trip Rates by Category Trip Purpose Under $20,000 $20,000 - $35,000 $35,000 - $100,000 Over $100,000 Total Journey to Work 18% 19% 23% 25% 22% Home Based School 21% 23% 28% 30% 26% Home Based University 17% 18% 21% 22% 20% Home Based Shopping 20% 21% 25% 27% 24% Home Based Social-Recreation 20% 21% 26% 28% 25% Home Based Escort 19% 20% 25% 27% 25% Home Based Other 20% 21% 25% 27% 24% Non-Home Based Work 20% 21% 25% 27% 24% Non-Home Based Non-Work 19% 19% 23% 25% 22% Total 20% 21% 25% 27% 24% Table 5.5 highlights the increase in average impedance between 2010 and Trip durations increase anywhere between 1 percent for Home-based Shopping to nearly 16 percent for Non-home based work trips. Given that a majority of increases in trip making happen outside the congested Memphis region, this increase in trip length appears reasonable. Overall, transit has a smaller growth in trips than the overall growth of trips in the region. Therefore, transit market share reduces slightly in the future (Table 5.6). Similar patterns hold true for walk and bike. This is primarily due to the fact that a majority of increases in trip-making happen in areas where there is neither good transit service nor are there good bike and pedestrian facilities. Table 5-5 Change in Average Impedance 2010 vs Trip Purpose Under $20,000 $20,000 - $35,000 $35,000 - $100,000 Over $100,000 Total Journey to Work 5.3% 7.3% 8.5% 8.5% 8.4% Home Based School 5.1% 6.5% 9.7% 5.4% 7.9% Home Based University 13.3% 0.1% 1.4% 4.0% 3.8% Home Based Shopping -12.3% -3.7% 1.4% 10.1% 0.5% Home Based Social-Recreation 3.8% 5.5% 4.8% 3.5% 4.4% Home Based Escort 0.5% 2.3% 6.2% 4.0% 4.7% Home Based Other 2.2% 4.2% 5.2% 5.0% 4.6% Non-Home Based Work 18.2% 14.5% 15.8% 15.5% 15.7% Non-Home Based Non-Work 7.4% 7.8% 7.3% 7.9% 7.6% 5-6 Cambridge Systematics, Inc.

147 Table 5-6 Change in Trips by Mode 2010 vs Trip Purpose Under $20,000 $20,000 - $35,000 $35,000 - $100,000 Over $100,000 Drive Alone 21.0% 20.7% 24.3% 25.8% 23.8% Shared Ride % 21.2% 25.9% 27.7% 24.7% Shared Ride % 23.6% 27.9% 29.5% 26.8% Walk to Transit 14.7% 13.0% 9.6% 10.8% 13.7% Drive to Transit 15.5% 12.8% 12.9% 15.6% 14.6% Bike 22.9% 16.1% 16.6% 14.8% 17.9% Walk 8.3% 4.3% 0.8% 8.8% 5.5% School Bus 20.9% 22.8% 24.2% 27.0% 23.2% Total Total 19.7% 20.7% 24.9% 26.7% 23.8% Overall, regionwide VMT increases by 42 percent whereas vehicle hours traveled (VHT) increase by 61 percent as compared to a regionwide increase in trips of only 24 percent (Table 5.7). This suggests that travelers in the region are, on average, traveling further, and for longer durations, thereby increasing congestion on roadways. This average regional speed drops from 37.5 mph to 33.2 mph, further indicating the increase in congestion in the region and the use of low speed roadways to travel between origins and destinations. Figure 5-2 and Figure 5-3 show the volume to capacity maps during AM peak periods of year 2010 and year 2040 respectively. The comparison between the two maps indicates more congestion in year Table 5-7 Growth in VMT and VHT 2010 vs Roadway Type 2010 VMT 2010 VHT 2040 VMT 2040 VHT VMT Growth VHT Growth Interstate 10,037, ,044 13,525, ,351 35% 67% Principal Arterial 9,918, ,462 14,360, ,855 45% 61% Minor Arterial 2,110,592 64,300 3,369, ,783 60% 71% Collector 6,197, ,468 8,830, ,046 42% 58% Other 4,325, ,198 6,328, ,471 46% 52% Total 32,589, ,472 46,414,218 1,399,505 42% 61% Cambridge Systematics, Inc. 5-7

148 Figure 5-2 Volume to Capacity Ratio during AM period in Year Cambridge Systematics, Inc.

149 Figure 5-3 Volume to Capacity Ratio during AM period in Year 2040 Cambridge Systematics, Inc. 5-9