1. For Model Users Group June 10, 2011 Kyeil Kim, Ph.D., PTP Atlanta Regional Commission

2. Today’s Menu Overall features of ARC’s Activity-Based Model (ABM) ABM Visualization Software, ABMVIZ Quality Assurance/Quality Control of ABM

3. Daily Travel Trip-Based Model - Home-Work: 2 trips - Work-Eat: 2 trips - Home-Gym: 2 trips Activity-Based Model - Follows daily activity patterns (departure time, duration, location, frequency, mode)

4. What is Activity-Based Model? ABM aims at predicting which activities are conducted where, when, for how long, with whom, the transportation mode involved and ideally also the implied route decisions Disaggregate, Micro-simulation, Behavioral, Tour- based ABM reflects the scheduling of activities in time and space

5. Structure of ARC Models: TBM vs. ABM Trip GenerationTrip DistributionMode Choice Route Choice Long-Term Choices Daily Activity Patterns Tour Mode Choice/Stop Trip Mode Choice Synthetic Population Trip-Based Model Activity-Based Model DemandSupply

6. Population Synthesizer Generates synthetic population to represent actual households and population Base year Input: Census data (marginal distributions of various household control variables), PUMS Control variables: householder age, HH size, HH income, presence of children in HH, number of workers in HH Joint distribution through Frata PUMS 5% sample as seed matrix, control totals from Census Draws from PUMS households from the joint distribution 1 record/hh and 1 record/person

7. Population Synthesizer (cont’d) Forecast year Input: ARC lane-use forecast, PUMS Control variables: HH size, HH income, householder’ age, number of workers in HH Joint distribution through Frata Base year distribution as seed matrix, control totals from land-use forecasts Draws from PUMS households from the joint distribution 1 record/hh and 1 record/person

8. Structure of ARC Models: TBM vs. ABM Trip GenerationTrip DistributionMode Choice Route Choice Long-Term Choices Daily Activity Patterns Tour Mode Choice/Stop Trip Mode Choice Synthetic Population Trip-Based Model Activity-Based Model DemandSupply

9. Long-Term Choices Mandatory activity location choice Work/school/university locations for the synthesized population Work location choice for workers Grade school for persons age 5-12 University for university students Multinomial logit: [subzones]=[person characteristics, size terms, mc logsums, distance, etc.] Car ownership model Number of vehicles owned by each household Multinomial logit: [# cars]=[hh size, income, parking cost, mc logsums, etc.]

10. Structure of ARC Models: TBM vs. ABM Trip GenerationTrip DistributionMode Choice Route Choice Long-Term Choices Daily Activity Patterns Tour Mode Choice/Stop Trip Mode Choice Synthetic Population Trip-Based Model Activity-Based Model DemandSupply

11. Coordinated Daily Activity Pattern Generates personal DAPs and individual tours by purpose for all synthesized population DAPs Mandatory, Non-mandatory & At-home patterns Decision-making unit: Households Multinomial logit: [# DAPs]=[person/hh characteristics, accessibility measures, intra-household interaction terms, etc.] 363 alternatives: 3 (1-p hh), 9 (2-p hh), 27 (3-p hh), 81 (4-p hh), 243 (5-p hh)

12. Tour Models Predicts the number and purpose of tours for each person, destinations, and time-of-day choices Four different tours Individual Mandatory Joint Non-Mandatory Individual Non-Mandatory At-Work Sub-Tours Residual Time

13. Individual Mandatory Tour Tour Frequency Number and purpose of tours for each person Multinomial logit: [# of work/school tour]=[hh composition, income, car ownership, location of work/school activities, accessibility, etc.] Tour Time-of-Day Select the combinations of tour departure/arrival time Multinomial logit: [combination of tour departure/arrival hours]=[household and personal characteristics, network LOS variables, etc.] Alternatives: 190 combinations of tour departure hour and arrival hour back at home

14. Joint Non-Mandatory Tour Joint tours by household members after mandatory tours have been generated and scheduled Joint Tour Frequency Generates the number/purposes of joint tours Multinomial logit: [0, 1 or 2 tours by purpose]=[household variables, accessibility, overlapping time windows, etc.] Joint Tour Composition Determines the person types participating in the tour Multinomial logit: [combination of adults & children]=[household characteristics, purpose of joint tour, overlapping time windows]

15. Joint Non-Mandatory Tour (cont’d) Joint Tour Primary Destination Choice Location of the tour primary destination Multinomial logit: [subzones]=[household/person characteristics, tour purpose, size variables, mc logsum, distance, etc.] Joint Tour Time-of-Day Choice Tour departure time from home and arrival time back at home Multinomial logit: [combination of tour departure/arrival hours]=[household and personal characteristics, network LOS variables, etc.] Alternatives: 190 combinations of tour departure hour and arrival hour back at home

16. Other Tours Individual Non-Mandatory Tour Tour Frequency Tour Primary Destination Choice Tour Time-of-Day Choice At-Work Sub-Tour Tour Frequency Tour Primary Destination Choice Tour Time-of-Day Choice

17. Structure of ARC Models: TBM vs. ABM Trip GenerationTrip DistributionMode Choice Route Choice Long-Term Choices Daily Activity Patterns Tour Mode Choice/Stop Trip Mode Choice Synthetic Population Trip-Based Model Activity-Based Model DemandSupply

18. Tour Mode Choice Tour mode choice: main tour mode used from origin to primary destination and back Two-level mode choice in ARC ABM Tour mode level (upper-level choice) Trip mode level (lower-level choice conditional on the upper-level)

19. Tour Mode Choice (cont’d) Tour MC models Work, University, K-12, Non-mandatory, At-work 12 Alternatives Nested logit: [tour mode]=[household and personal characteristics, urban form variables, network LOS variables, etc.] Use the round-trip LOS between the tour anchor location and the primary destination

20. Intermediate Stop Models Stop Frequency Model Number of intermediate stops on the way to/from the primary destination by tour purpose Multinomial logit: [# of stops]=[household and personal characteristics, tour duration, tour distance, accessibility, etc.] Stop Location Choice Model Location of stops along the tour other than the primary destination Multinomial logit: [Subzones]=[mc logsum, distance, size variables, etc.]

21. Structure of ARC Models: TBM vs. ABM Trip GenerationTrip DistributionMode Choice Route Choice Long-Term Choices Daily Activity Patterns Tour Mode Choice/Stop Trip Mode Choice Synthetic Population Trip-Based Model Activity-Based Model DemandSupply

22. Trip Mode Choice Determines the mode for each trip along the tour Constrained by the main tour mode Correspondence rules to determine which trip modes are available for which tour modes E.g., drive-alone pay trip is only available for drive-alone pay tour E.g., transit tours can include auto shared-ride trips for particular legs

23. Structure of ARC Models: TBM vs. ABM Trip GenerationTrip DistributionMode Choice Route Choice Long-Term Choices Daily Activity Patterns Tour Mode Choice/Stop Trip Mode Choice Synthetic Population Trip-Based Model Activity-Based Model DemandSupply

24. Route Choice Same routine as the trip-based model Multimodal User Equilibrium Time-of-Day Assignment Bi-Conjugate Frank-Wolfe for both TBM and ABM, departing from the traditional Frank-Wolfe

25. Run Environment Java-Package Cube/TP+ Three 64-bit Windows machines Each machine with 32GB of RAM Base year run: approx. 30 hours 2040 run: approx. 50 hours

26. Visualization Model generates huge database Model visualization system, ABMVIZ Primary starting point for most model analysis questions Interactive/dynamic visualization of model estimates/results Some unique visualization types Tables, Bar Charts/Maps Time Use Tour Tracing Tree Map Radar Charts

27. Quality Assurance/Quality Control Quality Assurance (QA): a systematic review process by personnel not directly involved in model development Quality Control (QC): a technical routine to control quality of the model performed in model development Full understanding of the models’ capabilities/limits ARC initiated internal a year-long QA/QC process for both ABM and TBM New QA/QC guidelines

28. QA/QC Overall processes Reasonableness checking for EVERY modeling step Temporal validation between base and forecast years Comparability between ABM and TBM Components Modeling flows/Scripts Socioeconomic data Transportation network data External trips Trip generation, Trip distribution, Mode choice, & Traffic assignment

29. QA/QC (cont’d) Tools SQL Express Management Studio STATA ABMVIZ Custom scripts Voyager/TP+ R Our brain

30. Thank You!