Memphis and Shelby County Metropolitan Planning Organization (MPO) Travel Demand Model Tennessee-Kentucky Model User Group Meeting October 26, 2006
Model Development Team Kimley-Horn and Associates, Inc. Cambridge Systematics HNTB NuStats
Model History in Memphis Originally Developed in Late 60s Last Major Model Update was in 1995 Existing Model Parameters Model Boundary Demographic Variables Submodels 1998 Household Travel Survey
Review Process Client Review Steering Committee Peer Review Committee
Documentation and Meetings 12 Documents 2 Face-to-Face Peer Review Meetings 2 Peer Review Conference Calls 3 Steering Committee Meetings 2 Expert Panel Meetings for Land Use and Demographics Update Meetings with Engineering and Technical Committee monthly
New Travel Demand Model Decision to Change Platform GIS Based Program (TransCad) Flexibility in Model Applications Ease of Database Manipulation Reporting Features and Tools Consistency with State and other MPOs
Land Use Coverage Traffic Analysis Zones
Development of TAZ Structure Expansion of Prior Zonal Coverage In the North (Tipton County) In the East (Fayette County) In the South (DeSoto and Marshall Counties) Census TIGER Line Files Geographic Features Transportation Facilities
Model Area Boundary
Traffic Analysis Zones
Development of TAZ Structure Special Generators Census Boundaries Tracts (Suburban/Rural) Block Groups (Urban/Suburban) Blocks (Urban) Centroid Connectors and the Network TAZs in Previous Model: 800 (app) TAZs in Current Model: 1,237
Development of Baseline Data Population and Household Variables Used Data from Census 2000 (SF1, SF3) Matched Census Geography to TAZs Employment Variables Used 2000 At-Place Employment Data Reconciled using BLS Grouped into Generalized Industry Categories (NAICS)
Development of Baseline Data 2004 Estimation Consultation with Planning Staff Comparison with Available Data (E.G., Building Permits)
Typical Model Processes (How the model works) 4-Step Travel Demand Modeling Process Trip Generation (How Many Trips?) Trip Distribution (Where Do You Want to Go?) Mode Choice (How Do You Want to Get There?) Trip Assignment (Which Route?) Data Requirements Base, Future, and Interim Year Models
Household Survey Key Features MPO Region Shelby County and Part of Fayette County, TN Part of DeSoto County, MS Memphis MSA (Census 2000 Figures) Ranked 44 Out of All the Other MSAs in Country by Population Population = 1,135,614 :: Households = 424,202 Type of Survey Travel Diaries with Detailed Activities Description One Day, 24-Hour Travel Record for Every HH Member Computer-Aided Interviewing (CATI) Procedures Conducted in 1998 (September-November)
Survey Sample # Households = 2,526 # Persons = 6,438 # Trips = 198,519
Trip Generation Trip Generation Submodels Internal Person Trip Productions and Attractions (P s and A s) External/Internal Vehicle Trips Special Generators Vehicle Availability Model
Trip Generation Trip Purposes 9 Internal Trip Purposes Journey to Work Home Based School Home Based University Home Based Shopping Home Based Social-Recreational Home Based Pick-up and Drop-off Home Based Other Non-Home Based Work Non-Home Based Non-Work
Vehicle Availability Models Models Multinomial Logit (MNL) Model Ordered Response Logit Model (ORL) Model Inputs Tested HH Characteristics Accessibility, Socio- Demographics (# Persons, # Workers, Income Level Dummies) Validation Validated Census Data Selected Model with Best Performance
Multinomial Logit (MNL) Model All Households (HHs) 0 Vehicle HHs Probability 1 Vehicle HHs Prob(n th alt) = Utility Equations: n e max i= 0 U n e U U i n 2 Vehicle HHs = b n0 + nv j= 1 b nj X 3+ Vehicle HHs nj
Vehicle Availability Model Coefficients of ordered response logit model Variable 0/1+ 1/2+ 2/3+ Constant 1.12-2.41-1.62 2 Person HH 2.20 3+ Person HH 2.25 0.59 1 Worker 0.90 2+ Workers 1.48 1.09 3+ Workers 1.76 LMed Income 1.42 1.19 HMed Income 1.86 2.40 0.58 High Income 3.10 2.88 0.98 % Emp w/in 15 min -0.05-0.05-0.04
Trip Generation Application Results Trip Purpose Productions Attractions % Diff JTW 783,436 706,159-10% HBSc 343,361 372,993 9% HBU 56,147 46,202-18% HBSh 223,496 232,395 4% HBSR 238,801 254,492 7% HBPD 207,017 201,188-3% HBO 612,326 608,710-1% NHBW 138,182 142,692 3% NHBNW 512,547 573,675 12% Total 3,115,313 3,138,506-1%
External Trip Generation External-External Trips from Statewide Models External-Internal Trip Generation: E j = AT j D j B where: E j = Number of EI Trips Generated in Internal Zone j T j = Total Internal Trip Attractions Generated in Internal Zone j D j = Distance from Zone j to the Nearest External Station A, B = Model Parameters
External Station Classification Expressway Arterial Near Expressway Arterial Not Near Expressway Collector/Local
Trip Generation Special Generators Memphis International Airport Graceland Federal Express (Airport Hub)
25.0 20.0 15.0 10.0 5.0 0.0 Time of Day Model Figure 1. Percent of Trips by Time and Purpose Journey-to-Work Home-Based School/University Home-Based Other Non Home-Based All Trip Purposes 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22 22-23 23-24 Time Period 1-2 0-1 Percent of Trips
Time of Day Model Percent of Trips by Purpose Time Period Journey-to- Work HBSchool/ HBUniversity Other Home- Based Purposes Non-Home- Based All Purposes 0:00-1:00 0.8 0.0 0.4 0.1 0.40 1:00-2:00 0.2 0.0 0.1 0.1 0.15 2:00-3:00 0.3 0.0 0.2 0.1 0.15 3:00-4:00 0.4 0.0 0.1 0.0 0.17 4:00-5:00 0.7 0.0 0.2 0.0 0.30 5:00-6:00 2.9 0.2 0.5 0.2 1.16 6:00-7:00 9.3 7.8 2.5 0.8 5.46 7:00-8:00 16.7 23.6 7.0 3.8 12.52 8:00-9:00 7.8 11.7 5.8 3.4 6.79 9:00-10:00 3.1 3.1 5.1 3.8 3.90 10:00-11:00 1.3 2.6 4.4 5.4 3.27 11:00-12:00 1.8 3.3 4.7 13.2 4.42 12:00-13:00 2.2 3.7 4.8 19.1 5.17 13:00-14:00 2.4 2.1 4.7 12.2 4.41 14:00-15:00 4.0 13.8 7.0 11.4 8.54 15:00-16:00 7.1 12.3 8.4 9.0 9.40 16:00-17:00 10.1 3.6 7.3 5.2 7.39 17:00-18:00 12.3 4.4 8.6 3.7 8.56 18:00-19:00 6.4 1.9 8.9 3.1 6.22 19:00-20:00 3.1 1.6 7.4 2.3 4.20 20:00-21:00 2.0 2.3 5.1 1.4 2.95 21:00-22:00 1.9 0.9 3.7 1.0 2.24 22:00-23:00 1.7 1.0 2.1 0.4 1.32 23:00-24:00 1.4 0.2 1.2 0.2 0.90 Total 100.0% 100.0% 100.0% 100.0% 100.0%
Time of Day Model Time of Day Directional Trip Factors (Post-Mode Choice) Trip Purpose Direction AM Peak Midday Peak PM Peak Off-Peak 1 JTW % From Home 95.59 64.42 10.81 26.93 % To Home 4.41 35.58 89.19 73.07 2 HBSchool % From Home 99.69 42.46 1.45 13.49 % To Home 0.31 57.54 98.55 86.51 3 HBUniv % From Home 96.60 40.13 25.78 9.19 % To Home 3.40 59.87 74.22 90.81 4 HBShop % From Home 63.65 53.43 38.01 37.57 % To Home 36.35 46.57 61.99 62.43 5 HBPUDO % From Home 64.42 63.00 43.36 38.75 % To Home 35.58 37.00 56.64 61.25 6 HBSR % From Home 85.22 58.93 58.41 38.58 % To Home 14.78 41.07 41.59 61.42 7 HBO % From Home 88.58 54.90 35.46 37.65 % To Home 11.42 45.10 64.54 62.35 8 NHBW N/A 50.0 50.0 50.0 50.0 9 NHBNW N/A 50.0 50.0 50.0 50.0
Time of Day Model Time of Day External Trip Factors Facility Type Direction AM Peak 1 Interstate 2 Other Principal Arterial Midday Peak PM Peak Off-Peak % of Daily 16.4 30.3 24.3 29.0 % Inbound 70 51 39 43 % Outbound 30 49 61 57 % of Daily 16.9 30.7 28.7 23.7 % Inbound 62 51 48 42 % Outbound 38 49 52 58 6 7/8/9 Minor Arterial Collector/ Local % of Daily 19.6 26.6 29.0 25.1 % Inbound 63 49 49 40 % Outbound 37 51 51 60 % of Daily 18.0 27.5 29.2 25.3 % Inbound 63 49 49 40 % Outbound 37 51 51 60
Trip Distribution Trip Distribution Model Components Intrazonal Travel Times Terminal Times Primary Destination Choice Intermediate Travel Times
Trip Distribution Primary Destination Choice Gravity Model T ij A F = P j ij i n ( A F ) j = 1 j ij Destination Choice Model P i = exp( U j exp( U i ) j )
Logit Destination Choice Model Utility of Choosing Destination Zone j = B 1 (impedance ij ) + B 2 ln (size variable) + B 3 (prod or attr zone dummy variable 1) + + B n (prod or attr zone dummy variable n-2)
Destination Choice Model for JTW Trips Variable Parameter Estimate Mode Choice Logsum 0.057 Production-Attraction Dummies Production and Attraction Ends in CBD Production End is Urban Zone and Attraction End is in CBD Production End is Suburban/Rural and Attraction End is in CBD Attraction End Dummies Attraction End is an Urban Zone Attraction End is a Suburban Zone Attraction End is a Rural Zone Production-Attraction Highway Distance Power Series Distance Square of Distance Cube of Distance 1.584 0.280 0.579 0.106 0.150 0.00 (Base) -0.261 0.009-0.00018 Multiplier for Size Variables 0.723 Size Variables (Coefficients Shown Are Exponents of Estimates) Service Retail Industrial/Manufacturing Wholesale Office Government 1.000 (Base) 0.438 0.533 0.597 0.394 0.386 Attraction Zone Area in Square Miles 0.0487
Destination Choice Model for NHBW Trips Variable Parameter Estimate Mode Choice Logsum 0.32 Attraction End Dummies Attraction End is a CBD/Urban Zone Attraction End is a Suburban Zone Production-Attraction Highway Distance Power Series Distance Square of Distance Cube of Distance -0.45-0.35-0.62 0.03-0.0004 Natural Log (Non-Home Based Work Modeled Attractions) 0.71
Journey to Work Stops Model Number of Stops Variable 0 1 2+ Constant -1.55-2.73 Home-to-work chain -0.31-0.75 1-vehicle household 0.58 1.24 2-vehicle household 0.58 1.21 3+ vehicle household 0.56 0.92 Presence of kids in household 0.76 0.98
Avg. Travel Time Comparison Purpose Model (min) Observed (min) JTW 17.07 17.08 HBO 12.22 12.21 HBPUDO 10.90 11.34 HBSc 9.61 9.63 HBSh 11.26 11.18 HBSR 12.71 12.70 HBU 19.25 16.31 NHBW 12.05 11.99 NHBNW 12.41 12.22
Mode Choice Multinomial Logit Model (Like Destination Choice Model) On-Board Transit and Household Survey Travel Modes Included
Mode Choice Modes included: Transit with Auto Access (Includes Bus and Trolley) Bus with Walk Access Trolley with Walk Access Non-Motorized (Including Walk/Wheelchair and Bicycle) Shared-Ride Drive Alone Spare Mode for Future Use
Survey Data Set Summary Mode JTW HBSc HBU HBSh HBPD HBSR HBO NHBW NHBNW All Bus - Auto 225 18 63 31-17 88 36 47 525 Bus - Walk 1,238 121 293 158-89 394 107 131 2,531 Trolley - Auto Trolley - Walk 11 1 4 6 0 5 25 6 20 78 51 0 6 17 0 6 52 38 34 204 Walk 124 469 4 94 38 137 154 52 83 1,155 Bicycle 11 20 - - - 5 5 2 4 47 School Bus 4 554 2-5 - 48-107 720 Shared Ride 793 1,010 45 599 889 603 2,068 225 1,792 8,024 Drive Alone 3,292 46 257 598 465 421 1,182 498 917 7,676 All 5,749 2,239 674 1,503 1,397 1,283 4,016 964 3,135 20,960
Mode Choice On-Board Transit Survey Results Vehicle Ownership 100% 80% 60% 60% 40% 20% 0% 25% 11% 3% 1% None One Two Three Four or more
Mode Choice On-Board Transit Employment Status Survey Results Employed, Full time 42.6% Employed, Part tim e 18.4% Unemployed, Looking 16.2% Student 15.2% Retired 3.3% Unemployed, Not Looking 2.4% Homemaker 1.9% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Mode Choice On-Board Transit Income Level Survey Results Less than $6,000 $6,000 - $18,000 34% 31.8% $18,001 - $30,000 18% $30,001 - $42,000 11.2% $42,001 - $60,000 $60,001 - $90,000 More than $90,000 3.2% 1.3% 0.5% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Mode Choice On-Board Transit Age Survey Results 100% 80% 60% 40% 20% 0% 35.6% 19.5% 19.0% 15.6% 7.8% 0.9% 1.7% Under 16 16-18 19-24 25-34 35-49 50-64 65 or older
Mode Choice On-Board Transit Ethnicity Survey Results 100% 88.9% 80% 60% 40% 20% 0% Black/American American 8.2% 1.2% 0.7% 0.6% 0.4% White Other Native American Hispanic Asian American
Freight Model Trip Gen Quick Response Freight (QRF) Manual Generator (Unit) Agriculture, Mining, and Construction Manufacturing, Transportation, Communications, Utilities, and Wholesale Trade Commercial Vehicle Trip Destinations (or Origins) per Unit per Day Four-Tire Trucks Single Unit Trucks Combination Trucks 1.110 0.289 0.174 1.573 0.938 0.242 0.104 1.284 Retail Trade 0.888 0.253 0.065 1.206 Office and Services 0.437 0.068 0.009 0.514 Total Trucks Households 0.251 0.099 0.038 0.388
Freight Model (cont.) Trip Distribution - Gravity Model Calibrated to Classification Counts
Roadway Network Development Used network provided by MPO (with some cleaning) Developed data collection tool in TransCAD to enter in network attribute data Collected street data for all streets in network (through TRIMS and windshield data)
Network Collection Tool Allowed for data entry in the field by a two person team Could copy and paste data from one link to another Helped to minimize coding errors
TRIMS Image Data
Roadway Network Development Coordinated with TAZ development to ensure appropriate level of detail for both Developed centroid connectors in coordination with local staff Centroid connectors indicated auto/non-auto access Aerial photography and measurement data used to clean interchanges in network
Roadway Network Development One TransCAD file contained all years of development baseline, existing plus committed, long range plan, etc., by year Network contained link-dating that indicated when a particular section will open (or close) Changes in network carried over to all potential scenarios and years
Roadway Network Quality Control TransCAD tools such as Check Line Layer Connectivity were used Trip path tests and test loadings also were used to identify network issues Plots with network attributes (lanes, speeds, median type, etc.) were submitted for review Checks against available aerial photography
Capacity Equations Based on HCM and TDOT Data Doesn t use standard lookup tables completely based on attributes Live update lanes on a link, capacity updates Calculated hourly and daily capacity Calculated LOS A through LOS E
Capacity Equations The general form of the equation was: SF = c * N * fw * fhv * Fp * FE * fd * FSD* FCLT * FPark * (v/c)i Where the variables were: SF = Maximum service flow for desired level-of-service c = Capacity under ideal conditions (vehicles per hr per lane) N = Number of lanes fw = Factor due to lane and shoulder width fhv = Factor due to percent heavy vehicles Fp = Factor due to driver population FE = Factor due to driving environment fd = Factor due to directional distribution FSD = Factor due to signal density FCLT = Factor for continuous left-turn lane (for undivided sections) FPark = Factor for on-street parking (v/c)i = Rate of service flow for levels-of-service A through E
Capacity Equations
Lookup Tables No hardcoding of values Separates interface development from model development More efficient model adjustments/calibration Subsequent model updates don t necessarily need new code Data is more transparent and accessible
Lookup Tables
Network Development Highway Network Network
Network Development Highway Network Screenlines and Cutlines
Network Development Highway Network Area Types
Network Development Highway Network Code Facility Type Centerline-miles Daily Counts TOD Counts Class Counts Supplementary Counts 1 Rural Interstate 51 11 7 0 2 2 Rural Principal Arterial 137 42 25 1 1 3 Rural Freeway Ramp 11 6 Rural Minor Arterial 78 76 42 4 7 Rural Major Collector 263 166 93 7 3 8 Rural Minor Collector 261 250 152 15 9 Rural Local Access 374 32 18 1 4 11 Urban Interstate 143 105 61 0 9 12 Urban Freeways/Expressways 59 47 31 0 1 13 Urban Freeway Ramp 103 14 Urban Principal Arterial 276 443 289 4 6 16 Urban Minor Arterial 556 894 578 22 22 17 Urban Collector 332 499 326 9 9 19 Urban Local Access 113 36 22 3 4 Total Rural Roads 1175 577 337 28 10 Total Urban Roads 1582 2024 1307 38 51 Total - All Roads 2757 2601 1644 66 61
Network Development Transit Network 129 1-way routes
Network Development Transit Network
Network Development Transit Network
4 Park and Ride Lots
Assignment Roadway and Transit Networks Level of Detail Data Collection Effort Quality Control All or Nothing Preload Heavy Commercial Vehicles External-External Trips Equilibrium Multi-Class Assignment Pathfinder Transit Assignment
Highway Assignment Validation Targets Table 1. Percent Difference Targets for VMT by Functional Classification Facility Type Target Freeways 8-12% Principal Arterials 18-22% Minor Arterials 27% Collectors 33% Table 2. Percent Difference Volume Targets by Functional Classification Facility Type Target (+/-) Freeway 7% Major Arterial 10% Minor Arterial 15% Collector 25% Local 25% Table 3. Percent Difference Volume Targets by Daily Volume Groupings (totaled over entire group) Volume Group Target (+/-) <1,000 200% 1,000-2,500 100% 2,500-5,000 50% 5,000-10,000 25% 10,000-25,000 20% 25,000-50,000 15% >50,000 10%
Highway Assignment Validation Targets Table 4. Percent of Links within a Specified Percent of Count by Facility Type Facility Type Target within Count Range Compared to Counts Freeway 75% 20% Freeway 50% 10% Major Arterial Major Arterial Minor Arterial Minor Arterial 75% 30% 50% 15% 75% 40% 50% 20% Note: Table 4 can be read as 75% of the freeway links need to be within 20% of counts, 50% of the freeway links need to be within 10% of counts.
Highway Assignment Global Results
Highway Assignment Global Results Screenlines and Cutlines
Highway Assignment Calibration/Trouble Shooting Globally Low Modeled Volumes versus Observed Volumes Assignment Bias Toward Interstate versus Non-Interstate Facilities (Particularly Pronounced in Urban Area)
Future Year Model Demographics Area Type Signals Highway and Transit Network
What s New About this Forecasting Process? Employed a Rigorous Analytical Model Integrated this Economic Model with the Benefits of Local Planning Knowledge Review by Expert Panel Review by Local Planners
45 Sub-County Areas (SCAs)
Memphis Forecasting Sequence NATIONAL FORECAST Integration of federal data REGIONAL FORECAST Industry linkages to U.S. SCA FORECASTS Allocation of regional forecasts To 45 sub-county areas (SCAs) Expert Panel Review/Revision TAZ FORECASTS Allocation of SCA forecasts to 1,237 traffic analysis zones (TAZs) Local Planners Review/Revision
Study Area Forecast 900000 800000 700000 600000 500000 400000 Households Jobs 300000 200000 100000 0 2004 2040
Forecast by Counties 700000 Shelby County 2040 600000 500000 2004 2040 400000 2004 300000 200000 100000 0 Jobs Households
Forecast by Counties 140000 120000 100000 DeSoto County 2040 2040 80000 60000 40000 2004 2004 20000 0 Jobs Households
Study Area Portion of Other Counties 35000 Portion of Fayette County 35000 Portion of Tipton County 30000 2040 30000 2040 25000 2040 25000 20000 20000 2040 15000 15000 2004 10000 5000 2004 2004 10000 5000 2004 0 Jobs Households 0 Jobs Households
Study Area Portion of Other Counties 14000 Portion of Marshall County 12000 2040 10000 2040 8000 6000 4000 2000 2004 2004 0 Jobs Households
Households
Households
Employment
Employment
MSA Forecast Results 2,000,000 Regional Population Forecast 1,000,000 Regional Employment Forecast 1,800,000 900,000 1,600,000 800,000 1,400,000 700,000 1,200,000 600,000 1,000,000 500,000 800,000 400,000 600,000 300,000 400,000 200,000 200,000 100,000 0 1980 1990 2000 2004 2010 2020 2030 2040 0 1980 1990 2000 2004 2010 2020 2030 2040 Between 2004 and 2040 54% Gain in Population 56% Gain in Total Employment
Future Year Signal Identification Capacity equations used intersection penalties, signal density, and signal coordination If you use signals, you have to forecast them.. somehow Basic warrant analysis flagged potential new signals User then used tool to accept/reject pending flags
Future Year Signal Identification Flags potential signals using basic warrant analysis on AM peak (using TDOT standards) Corridors are grouped into sections for capacity equations (Signal density and signal coordination)
Area Type Model Forecasted by 6 Categories CBD, CBD Fringe, Urban, Suburban, Rural and OBD (Outlying Business District) Forecasting Methodology No downgrade is possible New CBD zone must be adjacent to existing CBD zones New urban zone must be adjacent to the existing urban cluster New urban cluster will be created if the area is >10 square miles Existing or new OBD zones will become urban if they become adjacent to any urban zone No neighboring constraints on OBD, suburban, and rural zones Unique Algorithmic Features The forecasting process is conducted inside out, similar to an urban sprawl process, to avoid invalid neighbors If one zone can upgrade to more types, they are evaluated in priority order, so all possibilities will be considered CBD,CBD-fringe and urban zones are evaluated by recursively finding the fringe zones and finalize it step by step All future area types are decided based on final decisions already made e.g., not dependent on the particular evaluation order
Memphis Model Demo Introduction Installation Scenario Management Model Run Control Export Results Future Year Network Structure and Project Management Query Project Modify/Redefine Existing Project Add/Delete project Future Year Signal Forecasting Forecasting Signal Locations Signal Density Signal Coordination Reports Highway Transit Maps Highway Transit
Questions?
Contacts Mark Dunzo Email: mark.dunzo@kimley-horn.com Phone: 919.677.2075 Kenny Monroe Email: kenny.monroe@kimley-horn.com Phone: 901.374.9109 Zhiyong Guo Email: zhiyong.guo@kimley-horn.com Phone: 901.374.9109 Pramoda Gode Email: pramoda.gode@kimley-horn.com Phone: 919.653.2949
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