Routenwahl im IV - Teil 2

Transcription

1 Routenwahl im IV - Teil 2 Klaus Nökel TRB Innovations in Travel Modeling Conference 2014 AGGREGATE TOUR BASED MODELING, A PRAGMATIC STEP-UP FROM THE 4-STEP MODEL Chetan Joshi Klaus Noekel

2 OVERVIEW 1. Background and Objective 2. Methodology 3. Example Implementation 4. Remarks 5. Future Chetan Joshi Klaus Noekel

3 BACKGROUND Activity and tour based modeling has moved from research to real world application in the past few years Many larger agencies now have fully functional activity based models (NYMTC, SACOG, ARC, SFCTA etc.) Most of the activity based models implemented in the US are disaggregate simulation based models Implementation is typically based on custom code with many moving parts I Seite 3

4 BACKGROUND There are still challenges in widespread adoption of activity or tour based models, according to the Transportation Research Board Special Report 288 Cost typical model costs ~ $1 million to $1.4 million Technical issues consistency in simulation outputs, greater data requirement, high run times Staffing and Training Most small and medium MPOs have few staff members assigned to travel forecasting and inadequate knowledge base for model development Institutional Issues Aversion to changing status quo I Seite 4

5 MOTIVATION Previous work done by Bernardin and Conger (Knoxville), Vovsha (Ottawa- Gatineau), Allen (Glynn County, GA) indicates that an aggregate or hybrid approach to tour based modeling is worth exploring So we attempt to provide a standardized platform and methodology for tour-based modeling in an attempt to aid its assimilation into the state of practice. I Seite 5

6 METHODOLOGY Aggregate Tour Based Modeling Approach (VISEM) VISEM is based on explicit modeling of activities of homogeneously divided behavioral groups/ socio-economic groups aggregated at a zonal level. I Seite 6

7 METHODOLOGY Aggregate Tour Based Modeling Approach (VISEM) Travel decisions are modeled as three integrated processes: Tour Generation Destination Choice Mode Choice Simultaneously computed Logsums go up from mode choice to destination choice I Seite 7

8 TOUR BASED DATA MODEL STRUCTURE Objects used in the VISEM data model include Person groups (behaviorally homogeneous demographic) Structural properties (land use/employment data) Activities Activity Pairs Demand Time Series Activity Chains Demand Strata I Seite 8

9 TOUR GENERATION Tours are generated based on mobility rates derived from survey data Mobility rate is the frequency/probability of executing a given home based tour E+c E-c NE+c NE-c Appren Stud SPup PPup Child HWH 74% 63% 8% 3% 33% 11% 2% 0% 0% HSH 0% 0% 0% 0% 48% 0% 0% 0% 0% HPH 17% 26% 61% 63% 12% 24% 13% 9% 0% HRH 27% 25% 53% 40% 38% 37% 40% 39% 0% HPH 0% 0% 0% 0% 0% 0% 0% 75% 0% HSH 0% 0% 0% 0% 0% 45% 0% 0% 0% HOH 1% 2% 1% 0% 0% 0% 80% 0% 0% HWWH 3% 1% 0% 0% 1% 0% 0% 0% 0% HWOH 5% 7% 1% 0% 2% 1% 0% 0% 0% HWRH 2% 1% 0% 0% 1% 2% 0% 0% 0% HWOH 0% 0% 0% 0% 0% 0% 0% 0% 0% I Seite 9

10 DESTINATION CHOICE May adopt user defined form, i.e. Gravity or discrete choice model. Calculated as a series of matrix operations with each matrix representing an activity leg/pair. For example: (HWOH) includes activity pairs: HW, WO, OH Logit form transforms to => P ij = eu ij +ln Z j B k=1 e U ik +ln Z k Trips between an OD pair I Seite 10

11 MODE CHOICE Choice Probability of a mode m between an origin i and destination j May be applied based on person type or person type + destination activity I Seite 11

12 MATRIX OPERATIONS Destination / mode choice calculation for a tour (HWOH) HW Z(W) Q Auto NOT exchangeable along tour Bike & transit exchangeable along tour WO Z(O) Q 1 2 HW(auto) HW(bike) HW(transit) OH Z(H->Anchor) WO(auto) WO(bike) WO(transit) Q *(8.1375) 2*(3.4875) 2 0 2*(8.1375) 2*(3.4875) OH(auto) OH(bike) OH(transit) I Seite 12

13 TIME OF DAY CALCULATION Temporal profile applied at the activity pair level Origin activity Destination activity Temporal profiles H HOME W Work HW_E+c, HW_E-c O Recreation Location H HOME OH_E+c, OH_E-c W Work H HOME WH_E+c, WH_E-c I Seite 13

14 EXAMPLE IMPLEMENTATION Tour based model implemented for City of Winnipeg, Canada 8 th Largest city in Canada CMA Population ~ 730,018 (2011) sq miles (464 km²) Existing 4-step model for AM I Seite 14

15 TRANSITION FROM FOUR STEP MODEL Features/characteristics adopted from the 4-step model Person groups (12 primary person types) Travel modes (sov, hov, transit, bike/walk, school bus) Auto network with capacities and road type classifications Coefficients for destination/mode choice Destination parking costs etc. I Seite 15

16 TRANSITION FROM FOUR STEP MODEL Enhancements added for tour based model TAZ system expanded from ~550 zones to 1136 zones Transit network replaced by enhanced high resolution GTFS based network Trip generation replaced by Tour generation Better representation of discretionary purposes (now modeled as tours) K-factors were eliminated Basic time of day calculation was added Added delay functions for turns and nodes Airport trips handled using a trip based submodel I Seite 16

17 TRANSITION FROM FOUR STEP MODEL Flexible output specification I Seite 17

18 PERFORMANCE, SYSTEM REQUIREMENTS AND BUDGET Requirements for a single pass (laptop with 16GB of RAM and 4 cores): Procedure Time taken RAM needed Transit skimming (timetable based) => Only required at 1 st iteration 58min ~12GB Auto/Bike/Walk skimming 1min ~4GB Tour frequency generation on fixed mobility rates Tour-based destination/mode choice/time of day 12min ~4GB AM Assignment (gap: 10^-5) 3min15s ~4GB PM Assignment (gap: 10^-5) 3min53s ~4GB Budget of $90K with almost a third of the cost being used for network development, zone structure refinement and recompilation of land use data I Seite 18

19 REMARKS Does not require custom scripting or programming for data management or implementation of model procedures/calculations Produces point estimates that agencies are accustomed to seeing Runtime and hardware requirements are modest compared to a full disaggregate activity based model - Cost effective Can be incrementally blended/enhanced with simulation based methods or other methods to fill shortcomings Addresses some key issues related to better representation of destination choice and mode choice but not all issues (eg. time of day, intra-household interactions etc. which microsim ABMs do) I Seite 19

20 FUTURE Prototype with half-tours and rubberbanding has been developed and currently under testing Closer integration with the built in macroscopic dynamic assignment skim feedback by TI Refined time of day model More flexible expression evaluation in the embeded matrix calculator More charting and graphics specific to presentation of tour based modeling results I Seite 20

21 CREDITS Klaus Nökel (PTV AG) Arne Schneck (PTV AG) Doug Hurl (City of Winnipeg) David Tang (City of Winnipeg) I Seite 21

22 THANK YOU! I Seite 22