9. TRAVEL FORECAST MODEL DEVELOPMENT

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1 9. TRAVEL FORECAST MODEL DEVELOPMENT To examine the existing transportation system and accurately predict impacts of future growth, a travel demand model is necessary. A travel demand model is a computer based mathematical model that simulates existing roadway conditions, vehicle demand on roadways and use of public transit. With a model that accurately replicates existing travel conditions, future conditions and impacts of alternatives can be evaluated. Travel demand modeling for the Tri-County region was performed using the TransCAD transportation planning computer software package. Steps in developing the travel forecast model for the Regional 2035 Transportation Plan are displayed in Figure 9-1. This chapter describes model development and how it was applied in preparing the plan. Highway Network Development In order to utilize a travel model, the existing roadway system must be represented by a TRANSCAD highway network. Translating the highway system into a computer representation is known as network coding. Basic elements of a network are nodes and links. Links represent actual roadways (excluding most minor local roads) found in the highway system. Nodes are end-points of links and connect links together. Nodes are usually located where a change in travel direction is allowed, such as intersections. Since links are representations of existing roadways, they are coded with their actual characteristics, speeds, distances, capacities, roadway types, functional classification and similar information. This information was coded using TransCAD. Once the network is created, it was reviewed for accuracy. This was done by reviewing highway paths or minimum time routes between areas of the region. An extensive review of highway paths was done to ensure the network accurately represents routes people choose in daily travel. Capacities As part of model development, an updated methodology was used to create revised roadway capacities based on 2000 Highway Capacity Model. For interrupted flow facilities, base capacities are calculated as the product of ideal capacity at Level of Service (LOS) D and percent green time expected on that type of facility. Ideal capacity is defined as 1,700 passenger cars per hour per lane (pcphpl) on one-way streets and 1,600 pcphpl on all other signalized highways subject to delays from turning movements. Percent green times are estimated as a function of relative priority of the link s facility type and priority of conflicting facility types most likely encountered in the next mile of travel. If network link coding is changed, default green time may require adjustment to reflect dominant downstream facility types. Percent green time by facility type is defined as:

2 Figure 9-1: Travel Demand Modeling Forecast Process Network Development Socio-Economic Data Development Trip Generation Trip Distribution Mode Choice Traffic Assignment Model Calibration

3 Divided highways 0.70; One-way streets 0.60; Ramps (controlled) 0.60; Principal arterials 0.50; Minor arterials 0.40; Collectors 0.30; and Locals To adjust for number of lanes, free flow facilities (freeways) are multiplied by number of through lanes. Base capacities for rural freeways are 2150 pcplph and 2090 for urban/suburban freeways. Multilane highways use 1850 pcplph and two lane highways use On controlled facilities, capacity of the first travel lane is adjusted downward by five percent and additional lanes are assigned full capacity. If a median or center left turn lane is available on a two-way facility, capacity of the center lane is adjusted upward by a factor that varies by area type 20 percent in the CBD, 15 percent in the rest of the urban area and 10 percent in suburban areas. Finally, capacities for facilities without left turn bays are adjusted downward 20 percent. Adjustments are also made for lane width, heavy vehicles, on street parking and transit vehicle usage. Transit Network Development As part of model development, a transit network was also coded. Existing Capital Area Transportation Authority bus routes were coded to operate on the Tri-County highway network. Socioeconomic Data Development Statistical studies have shown a significant correlation between an area s demographic/ economic characteristics and trip making. The travel forecast model for the Regional 2035 Transportation Plan utilizes socioeconomic information on population, housing, auto-ownership, retail employment and non-retail employment to estimate the amount of regional vehicle travel. Initially, socioecomonic data was developed by County. For purposes of transportation modeling, the region was divided into 1082 internal traffic analysis zones (TAZs) and 62 external stations. TAZs are small geographic areas bounded by roadways, waterways or other physical features. Land use in each zone is generally homogeneous and may be all residential, commercial or industrial (or a mix). Socioeconomic data by County was disaggregated to TAZ and then summed to MCD as discussed in Chapter 6. Data for these zones was thoroughly reviewed and updated for the 2005 base year. Information was gathered from federal, state, local and university resources as well as from community leaders. Once 2005 base year socioeconomic data was finalized, projections of the 2035 socioeconomic data were developed for four different land use alternatives. Projecting

4 future year socioeconomic data allows for estimation of future year vehicle travel. A more detailed description of socioeconomic data development is in Chapter 6. Table 9-1 provides a summary of socioeconomic data used in the model. Table 9-1: Socioeconomic Data Summary Regional Data Adopted Trend Forecast 2035 Adopted Trend Forecast 2035 Wise Growth Build Out Wise Growth Build Out Population 454, , , ,808 1,163,800 1,076,300 Retail Employment 49,431 49,319 50,764 50, , ,600 Non-Retail Employment 231, , , , , ,400 Households 181, , , , , ,200 Vehicles 331, , , , , ,100 Trip Generation Trip generation estimates trips to and from each TAZ in the region. In this step, socioeconomic data is used to estimate number of daily person-trips in the study area and those with origins or destinations outside the area. The trip generation submodel uses TAZ level data and estimates trip attractions and trip productions in each traffic zone. For transportation planning purposes, a trip production is the origin point of a particular trip while a trip attraction is the destination. The amount of trips generated by a zone is assumed to be a function of the number of people, households and automobiles in the zone. A zone attracts trips if it contains retail or non-retail employment. Since people make trips for various reasons, it is useful to create different categories of trip productions and attractions. In the Tri-county model, trips were estimated for the following purposes: (1) Home based Work (HBW) trips; (2) Home based Other (HBO) trips; (3) Non-home Based (NBH) trips; (4) Four tire commercial vehicles; (5) Single unit trucks with six or more tires; and (6) Combination trucks with 9 tractor and one or more trailer units. Additionally, due to its size, a separate trip purpose was developed to account for the unique trip making characteristics of Michigan State University. Trip generation, productions, attraction rates and equations used in trip generation analysis were originally taken from the National Cooperative Highway Research Program (NCHRP) Report 365, Travel Estimation Techniques for Urban Planning the replacement report for NCHRP 187 were adjusted as appropriate based on consultants review of Michigan Travel Counts data and experience elsewhere. Truck trip data was taken from the United States Department of Transportation Quick Response Freight

5 Manual (QRFM). Trip generation rates were applied to socioeconomic data for each TAZ. A few sites in the region require special consideration when developing trip generation estimates. As previously mentioned, MSU required a slightly different approach to trip generation. Lansing Community College and Cooley Law School also required an adjustment to trip generation rates due to unique trip making characteristics of those commuter colleges. Vehicle trips to and from areas outside the Tri-County region are based on a review of traffic counts at various external stations, data gathered by MDOT in roadside origindestination interviews and the Michigan Travel Counts data collection program. Table 9-2 summarizes trip generation analysis. Regional trip productions and attractions are presented by purpose for the year Table 9-2: 2005 Trip Generation Trip End Summary Trip Distribution Trip Purpose 2005 Total Trip Ends HBW 309,381 14% HBO 933,954 41% NHB 521,119 23% MSU 70,890 3% TRUCKS 133,723 6% INTERNAL/EXTERNAL 233,648 10% THROUGH 82,579 4% TOTALS 2,285, % Purpose % of Total Trip Ends The next step in the modeling process distributes productions and attractions developed in the trip generation model. In trip distribution, productions in each zone are allocated to attractions found in other zones. Trips are distributed from their production zone to their attraction zone based on a gravity model. The gravity model, based on Newton s law of gravity, distributes trips between zones based on size of zones and relative distance between zones. Trip distribution is performed for each trip purpose. Reasonableness of a trip distribution model can be determined by reviewing average trip lengths by purpose. Table 9-3 shows year 2005 average trip lengths by purpose for the Tri-County trip distribution model. These trip lengths are reasonable in comparison to areas of similar size and make-up as the Tri-County region. Trip lengths are lengthened somewhat by the rural nature of counties which surround the Lansing urban core and the large size of the region. Rural trips to the city tend to be much longer than urban trips, but the relatively lower number of rural trips tends to minimize effects of longer trips.

6 Table 9-3: 2005 Average Trip Length Summary Trip Purpose Number of Trips Average Trip Length HBW 309, minutes HBO 933, minutes NHB 521, minutes MSU 70, minutes TRUCKS 133, minutes TOTAL1 1,969, minutes After person-trips are distributed between all different zones, person-trips were converted into vehicle trips. This is done by applying vehicle occupancy factors to person-trip numbers to develop vehicle trip numbers. Separate vehicle occupancy factors are used for different trip purposes which reflect local operating conditions, but were based on factors derived from studies in the region or borrowed from other studies. Table 9-4 displays vehicle occupancy factors used in the Regional 2035 Transportation Plan. Table 9-4: Vehicle Occupancy Factors Trip Purpose Auto Occupancy Factor HBW 1.05 HBO 1.54 NHB 1.43 MSU 1.54 Similarly, to account for the relatively greater impact that trucks and commercial vehicles have on traffic flow, they are converted to Passenger Car Equivalents for distribution, as shown in Table 9-5. Table 9-5: Freight to Passenger Car Conversion Freight Passenger Car Unit Conversion 4-Tire Truck 1.5 Singe Unit Truck 2 Combined Truck 4 Time-of-Day Analysis A time of day model was developed and offers several advantages over a simple analysis of daily volumes and capacities. Analysis of distinct AM and PM periods can reveal separate worst case conditions. Deficiencies can be identified in either the AM or PM peaks, as well as during both periods. This sensitivity helps explain the nature of network deficiencies and better defines their extent and severity. Peak period modeling

7 greatly enhances performance and reliability of the transit/mode choice model and mobile source emissions estimation. For areas such as the Tri-County region, three time periods are sufficient to provide reasonable assignments AM peak, PM peak and off-peak. All trips are assigned to one of these three time periods. Implementing time of day modeling uses the following steps: Identify peak periods; Estimate share of trips in each period (demand); and Estimate capacity of highway facilities in each period (supply). For the Tri-County region, traffic count data indicates peak periods of travel generally range between 7:00 and 9:00 AM and 3:00 and 6:00 PM. Since travel demand varies through the day (with peaks occurring even in peak periods) calculating capacity of highway facilities during travel periods is not as simple as multiplying hourly capacity by duration (in hours) of the time period. Simplification of multiplying capacities by duration of time period would underestimate congestion: the worst congestion would not be evident. To adjust for this problem, a peak period capacity factor is applied to hourly capacity so it more accurately reflects these more congested conditions. The methodology employed in the updated model assumes peak period congestion is equivalent to peak hour congestion and that congestion for the off peak period is equivalent to congestion in the median off peak hour. This assumption and knowledge of different periods shares of daily trips allows us to calculate capacity factors used to determine peak period capacity. Walk and Parking Analysis Model An additional procedure was developed that reconciled automobile traffic projections for downtown Lansing and the MSU campus to supply of parking facilities to accommodate projected traffic. Regional travel forecasting procedures, for the most part, do not consider parking available in traffic analysis zones. Auto travel projections for a particular traffic analysis zone were compared to parking supply of that zone to ensure parking demand did not exceed supply. Accomplishing this task required: coding a walk and park network; estimating parking supply and demand; calibrating the peak period demand; and introducing this model into the four-step modeling process. If travel demand in excess of existing (or proposed) parking supply is projected, the excess demand is reallocated to adjacent or nearby traffic analysis zones with excess supply (where demand is less than supply). The walk time required for auto users to access their destination is determined. The amount of excess supply in other traffic zones, parking cost in these zones and local traffic circulation patterns used to access

8 these zones are all considered in reallocation of auto trips from zones with excess parking demand. This iterative process is repeated until a pattern of parking demand is achieved in downtown Lansing and MSU that is in consonance with, or does not exceed, the distribution of parking. Various commuter lots at MSU are a special case since essentially a vehicle trip is needed to access the commuter lot, after which the driver either walks, bikes or takes the bus to their destination. A special procedure was developed to account for those trips which drive to an MSU commuter lot and then access a CATA bus or other mode to their final destinations. Mode Choice A mode choice model was developed that allows analysis of public transportation alternatives. It has a logit-based structure, preferred for urban transit forecasting. It determines proportions of projected total person travel between two travel analysis zones which are allocated between public transportation and private automobile. Factors considered in this allocation include trip purpose, highway and transit travel times, routing, frequency of transit service, transit fares, parking costs and other factors. Traffic Assignment After the mode choice model is executed, two matrices of values are produced (one for highways and one for transit) representing traffic volumes (highways) or trips (transit) between all traffic analysis zones. While this indicates amounts of travel between zones, it is still unknown what routes those traffic volumes or trips take to their destinations. In the traffic assignment submodel, those interzonal volumes/trips are assigned to the highway network. The basic premise is that traffic will follow the route with the least impedance or, in other words, the fastest travel time. However, for highway trips, when volumes on a route or link approach capacity of a link, speed is reduced and trips begin to divert to other routes which are now faster. This method of assigning traffic to a network is called capacity restraint and represents choices drivers make when driving on the actual highway network. The Lansing area travel demand model utilizes an equilibrium assignment algorithm as part of the capacity restraint program. The equilibrium algorithm allows for assignment of interzonal traffic to multiple paths based on interzonal travel times. Model Calibration Highway Assignment Calibration ensures accuracy of the travel forecast model. The basic goal in calibrating the model is to verify assigned traffic volumes simulate counted volumes on the actual roadway network for the same year. This is an iterative process in which assigned volumes are compared to actual ground counts. If results do not correspond to counted volumes, adjustments are made to the model in a number of ways. A region-wide problem might indicate need for adjusting trip rates in the trip generation model. Over

9 or under-assignments in specific locations might require reviewing and adjusting socioeconomic data at the TAZ level or including a special generator in the trip generation process. Problems at the link level might indicate need to adjust capacity, speed or other attributes of links with problems. This iterative process is repeated until assigned volumes accurately simulate ground counts for the region. The FHWA has developed statistical targets for measuring the level of calibration of a travel demand model. The Tri-County model was refined until it met FHWA targets for acceptability. Tables 9-6 and 9-7 outline FHWA calibration targets and how the model measures up to those targets. Table 9-8 shows the model also meets more restrictive MDOT volume group targets. Table 9-6: Calibration Targets Summary Facility Type Count VMT 1 Assigned VMT 2 A versus C 3 TARGET 4 TARGET MET? Interstate 2,527,969 2,715,099 7% ±7% Yes Other Freeways 728, ,814 12% ±7% No Principal Arterials 549, ,356 10% ±10% Yes Minor Arterials 510, ,309 8% ±15% Yes Collector 279, ,382-9% ±25% Yes Local 24,672 25,595 4% ±25% Yes Tri-County Total 4,620,122 4,985,554 7% ±5% No 1 Vehicle Miles of Travel (VMT) where counted vehicles are multiplied by distance traveled. 2 Assigned vehicles multiplied by distance traveled. 3. Assigned versus count. 4 FHWA target. Table 9-7: Calibration Targets by Volume Facility Type Count Volume Assigned Volume A versus C 1 TARGET 2 TARGET MET? Count Locations Interstate 1,879,418 2,004,100 6% ±7% Yes 111 Other Freeways 400, ,346 12% ±7% No 27 Principal Arterials 2,992,479 3,378,945 11% ±10% No 170 Minor Arterials 1,419,929 1,467,099 3% ±15% Yes 150 Collector 473, ,519-2% ±25% Yes 127 Local 66,959 74,535 10% ±25% Yes 22 Tri-County Total 7,232,002 7,844,543 8% ±5% No Assigned versus counts. FHWA target

10 Table 9-8: Calibration Targets Summary, Assigned Volumes by Volume Group Volume Group Count Volume Assigned Volume A versus C 1 TARGET 2 TARGET MET 1-1,000 25,530 19,648-30% ±200% Yes 1,001-2, , ,868-29% ±100% Yes 2,501-5, , ,736-19% ±50% Yes 5,001-10, , ,411-1% ±25% Yes 10,001-25,000 3,373,201 3,660,121 8% ±20% Yes 25,001-50,000 2,290,978 2,659,724 14% ±15% Yes All Count 7,131,826 7,681,508 7% - - Groups 1 Assigned versus counts. MDOT target. Transit Assignment Aggregate measures (such as number of trips by mode and by mode of access) are used to calibrate and validate the transit model. Routes with the highest number of boardings have good validation. It is also important to look at total number of boardings by service type. The CATA local service ridership estimate was 42,927. This compared favorably with counted ridership of 43,000. CATA MSU transit service was estimated to have a ridership of 2,161 which compared favorably with actual ridership of 2,160. Screenline/Cutline Analysis An additional measure of model accuracy is reviewing screenlines and cutlines in the study area model. A screenline is an imaginary line drawn through the region which divides the study area into two parts. The volume of traffic crossing that line in the model is then compared to actual ground counts. This gives a broad indication of whether overall volumes in the model are approaching correct values for the region. Six screenlines were developed for the Tri-County region and assigned values on four were well within MDOT calibration targets. Two were only slightly higher (three percent) than the target, which resulted from limitations in count data. Additionally, cutlines were developed for major corridors in the region. Cutlines are short screenlines which give a more accurate indication of whether volumes in a specific corridor are within acceptable parameters. Twenty-one corridor cutlines were developed. Of the 21 cutlines, two were within 3% of the standard (cutlines 13 and 25) and one (cutline 17) was within one percent. Two cutlines with larger errors (+6 percent and +9 percent, respectively, for cutlines 15 and 19) also may reflect potentially faulty traffic counts. All others were acceptable. Locations for cutlines and screenlines are shown in Maps 9-2 and 9-3. Table 9-9 shows screenline/cutline analysis results

11 Summary FHWA and MDOT urban model calibration targets are reasonably consistent with performance of this model. The trip generation model produced reasonable trip end volumes The trip distribution model resulted in average trip lengths comparable to other travel studies. Traffic assignment produced volumes close to or within FHWA and MDOT targets. Therefore, the model can be used in forecasting 2035 traffic to examine future year capacity deficiencies and as a reliable tool to perform future year plan development and analysis. Future year analyses require substitution of base year socioeconomic data with 2035 future year data and the entire modeling process is then rerun to develop future year values

12 Map 9-1: Map of Screenline Locations

13 Map 9-2: Map of Cutline Locations

14 Table 9-9: Screenline/Cutline Analysis Assigned Screenline Count Volume A Versus C* Target 1 Target Met Volume Screen 1 187, ,661 8% ±5% No Screen 2 253, ,966 5% ±5% Yes Screen 3 40,098 40,477 1% ±5% Yes Screen 4 68,050 68,042 0% ±5% Yes Screen 5 73,341 79,824 8% ±5% No Screen 6 59,225 61,874 4% ±5% Yes Cut 11 55,570 60,612 8% ±10% Yes Cut 12 48,820 46,449-5% ±10% Yes Cut 13 45,900 52,548 13% ±10% No Cut 14 29,400 28,078-5% ±10% Yes Cut 15 45,160 53,991 16% ±10% No Cut 16 32,401 33,812 4% ±10% Yes Cut 17 18,567 20,801 11% ±10% No Cut 18 39,325 43,124 9% ±10% Yes Cut 19 51,755 57,200 10% ±10% Yes Cut 20 30,730 33,890 9% ±10% Yes Cut 21 51,365 63,061 19% ±10% No Cut 22 13,385 13,218-1% ±10% Yes Cut 23 41,672 49,791 16% ±10% No Cut 24 80,198 78,589-2% ±10% Yes Cut , ,128 13% ±10% No Cut , ,910 3% ±10% Yes Cut 27 71,678 74,402 4% ±10% Yes Cut , ,640 9% ±10% Yes Cut 29 39,006 43,567 10% ±10% Yes Cut 30 26,798 29,694 10% ±10% Yes Cut 31 43,249 42,973-1% ±10% Yes Total 1,848,448 1,986,322 7% * Assigned versus counts. 1 MDOT targets

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