Mid-South Regional Travel Surveys & Model Update October 16, 2014 Presented to the Mid-South Travel Survey and Model Update Steering Committee By: Thomas Rossi and Anurag Komanduri, Cambridge Systematics Subconsultants: Abt SRBI, Dikita Engineering and Neel-Schaffer
2 Introductions Meeting Agenda Model development update Trip generation Mode choice Truck model Project schedule/status
3 Introductions Introductions of Steering Committee Consultant Team Cambridge Systematics prime, modeling lead & survey analysis Dikita Engineering transit onboard survey lead Abt SRBI household survey lead Neel-Schaffer support on freight and non-motorized surveys
4 Modeling Data Base Year data submitted to TDOT for approval TAZ system Highway and transit networks Socio-economic data Population Density To be completed Forecast year networks Forecast year socioeconomic data (from land use model) Worker Density
5 Model Development Plan Model Component Description Data Source Internal Trip Productions Cross-classification by trip purpose Household survey Internal Trip Attractions Linear regression by trip purpose Household survey Special Generators Asserted totals Outside data Journey to Work Stops Multinomial logit model Household survey EE and EI Trip Generation Linear regression model Household survey Destination Choice Multinomial logit model Household survey Intermediate Stop Destination Choice Multinomial logit model Household survey Time of day Fixed factors by trip purpose Household survey Mode choice Truck trip generation Nested logit model Linear regression model Household survey Transit survey TRANSEARCH + ATRI Freight survey Truck trip distribution Gravity model TRANSEARCH + ATRI
6 Trip Generation Initial Results Models segmented by income About 8.5 person trips per household (existing model: 8.2) Work journeys are modeled, including stops Percentages of trips by purpose: 2.00 1.80 JTW (journey to work) 23% HB Non-Work 51% Non-Home Based 25% Some trip rate differences from existing model by trip purpose 1.60 1.40 1.20 1.00 0.80 0.60 0.40 0.20 0.00 Income 1 Income 2 Income 3 Income 4 4+ Person 3 Person 2 Person 1 Person
7 Mode Choice Initial Results Modes modeled: Drive alone Shared ride 2 Shared ride 3+ School bus (HBSc) Transit walk access Transit auto access Walk Bicycle Variables include IVT, OVT, cost, income level, stops on JTW, population/employment density Generally, results as expected: Increased level of service makes modes more desirable Stops on JTW decrease likelihood of transit/non-motorized modes Lower income travelers more sensitive to cost Higher income travelers more likely to drive alone
8 Freight Data Model-related freight data provided by TDOT Preliminary TRANSEARCH data from IHS/Global Insight Processed Truck GPS data from ATRI Finalizing employment database by TAZ Model development will commence once emp data are approved Travel Market I-I I-E E-I Type of Truck Service Freight Freight Dataset Used ATRI (All trucks included) TRANSEARCH (Only freight trucks included) E-E Freight TRANSEARCH (Only freight trucks included)
9 Truck Model Initial Results Estimation process Two models: External (E-E, E-I, I-E) Internal Estimation databases External dominated by commodity truck flows, uses commodity flow database (TRANSEARCH) Internal dominated by service trucks, uses database that contains both freight and service trucks (ATRI GPS)
10 External Model Trip Generation Internal (TAZ) Truck Trips ends Develop NAICS3 employment by TAZ as explanatory variables. For each CG, for Internal Counties, identify best explanatory variable and rates for production and attraction equations External (Station) Truck Trip Ends Estimate from windowed TRANSEARCH FRATAR to observed truck counts
11 External Truck Model Trip Distribution Gravity Model Friction Factor is a negative exponential function of distance Coefficient is inverse of average trip length Distance within region from network skim Distance outside of region as reported in windowed TRANSEARCH T ij = k ijp i A j F ij k ij A j F ij n j=1 F ij = e c d ij d ij = di ij + do ij
12 Internal Truck Model Trip Generation Use ATRI truck table as observed variable QRFM employment by TAZ from SE data are explanatory variables TDOT SWM TAZs are used as geographic Trucks/day per employee/household level of detail For heavy trucks, develop rates for TG equations For TDOT SWM TAZ in Tennessee Variable Light Medium Heavy Agriculture/Mining/ Construction 3.22 0.84 0.504 Manufacturing/ TCU/Wholesale 8.50 2.19 0.942 Retail 10.71 3.05 0.784 Office/Service 0.0049 0.0008 0.0001 Households 0.0007 0.0003 0.0001
13 Internal Truck Model Trip Distribution Gravity Model Friction Factors use negative exponential function of distance Coefficient computed by TransCAD AM skim times from Memphis MPO model Observed ATRI truck table T ij = k ijp i A j F ij k ij A j F ij n j=1 F ij = e c d ij
14 Model Development Status Model Component Status Internal Trip Productions Draft complete Internal Trip Attractions Draft complete Special Generators Data obtained Journey to Work Stops Not started EE and EI Trip Generation Not started Destination Choice Underway Intermediate Stop Destination Choice Not started Time of day Underway Mode choice Draft complete Truck trip generation Draft complete Truck trip distribution Draft complete
15 Task Jun Approximate Month (revised) 2013 2014 Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1. Project Management 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2. Travel Demand Model Consultation 3. Research Design, Pretest, and Refinement 4. Household Travel Survey Data Collection 5. Freight Survey 6. Transit Onboard Survey 7. Bicycle and Pedestrian Survey 8. Data Weighting and Expansion 9. Travel Surveys Final Report/ Data Delivery 10.Travel Demand Model Update Interim Findings Deliverable Project Oversight Steering Committee Meetings 15
16 Questions/Comments and Discussion