Integrated Dynamic/AB Models: Getting Real Discussion Integrated ABM-DTA Model: First Application Experience and Lessons Learned Peter Vovsha, Jim Hicks, Matt Stratton, Chrissy Bernardo, Rosella Picado (WSP) Rebekah Anderson, Greg Giaimo (ODOT) Guy Rousseau (ARC) Robert Tung, Vassilis Papayannoulis (Metropia) Integrated Dynamic/AB Models: Getting Real Discussion May 15, 2017

Complete regional microsimulation model (ABM+DTA) Major Strands of Evolution of Urban Travel Models & Network Simulations Aggregate 4-Step Trip-Based models (since 1980th) Disaggregate Tour-Based models (since 2000th) Activity- Based Models (ABMs) (since 2010th) Complete regional microsimulation model (ABM+DTA) Aggregate Static Assignments (since 1980th) Sub-area and corridor Traffic Simulations (since 1990th) Regional Dynamic Traffic Assignment (DTA) (since 2000th) Generations of Travel Demand Models Generations of Network Simulation Models

Construction of ABM-DTA integrated system

Construction of ABM-DTA integrated system: Step 1/Q Fixed transit and NM LOS skims or static assignments or DPTA in future? Transit, NM LOS ABM Initial highway LOS Start with static LOS skims or preliminary DTA with initial trip roster? List of person tours & trips Car occupancy? Randomized departure time? Individual VOT or classes? External trips, trucks? Roster of vehicle trips DTA

Construction of ABM-DTA integrated system: Step 1/A Fixed transit and NM LOS skims Transit, NM LOS ABM Initial highway LOS Start with static LOS skims CT-RAMP2 design includes consistent translation of person trips into vehicle trips w/ continuous departure time Simpler ABMs require post processing External trips, trucks require one-time processing List of person tours & trips Roster of vehicle trips DTA

Construction of ABM-DTA integrated system: Step 2/Q Initial highway LOS Transit, NM LOS ABM How to feed back LOS for potential trips that have not been simulated? List of person tours & trips How to resolve schedule inconsistencies? Roster of vehicle trips Individual trajectories DTA

Construction of ABM-DTA integrated system: Step 2/A Initial highway LOS Transit, NM LOS ABM How to feed back LOS for potential trips that have not been simulated? List of person tours & trips iSAM Internal loop Roster of vehicle trips Individual trajectories DTA

Construction of ABM-DTA integrated system: Step 3/Q Initial highway LOS Transit, NM LOS ABM LOS manager ADIT List of person tours & trips External loop iSAM Internal loop Roster of vehicle trips Individual trajectories DTA

Integration Layer Components External Loop 1 w/mining individual trajectories LOS Internal Loop 2 w/individual Schedule Adjustment Module (iSAM)

Essence of each Loop External Loop 1: Internal Loop 2: Generates activity patterns & schedules Uses individualized LOS through trajectory mining Internal Loop 2: Simulates activity patterns Adjusts schedules for realistic trip chain loading Uses individual trajectories Evaluates “stress” measures

Data Input and Output for iSAM Planned Individual Schedule Input: Simulated Travel Time Output: Adjusted Individual Schedule Planned departure time Planned departure time Adjusted departure time Expected travel time Simulated travel time Simulated travel time Trip 1 Preferred arrival time Adjusted arrival time Planned activity duration Adjusted activity duration Planned departure time Planned departure time Adjusted departure time Expected travel time Simulated travel time Simulated travel time Trip 2 Preferred arrival time Adjusted arrival time Planned activity duration Individual schedule adjustment and stress evaluation Adjusted activity duration Schedule adjustment parameters

Schedule adjustment example (ARC)

Schedule adjustment example (ARC) SOV/free SOV/paid

Schedule adjustment example (ARC) HOV2/free HOV3/free

Schedule adjustment example (ARC) Walk Walk to local transit

Schedule adjustment example (ARC) Destination-work Origin-work

Schedule adjustment example (ARC) Destination-school Origin-school

Schedule adjustment example (ARC) Destination-discretionary Origin-discretionary

Daily Volume Differences

1x30 iters without iSAM

2x15 iters with iSAM

Learning about Space from Individual Trajectories (Dynamic Choice Set) One implemented trip provides individual learning experience w.r.t. multiple destinations [Tian & Chiu, 2014] Destination Origin 3 2 1 4 Moreover, people use trips to certain destination to learn more about the space. Each simulated trajectory provides learning experience with respect to multiple OD pairs that greatly improves the coverage. Efficient on-the-fly mining of the bank of individual trajectories and dissecting them into sub-trajectories is one of the essential components of the new integrated model. Instead of storing infinite OD skims we store individual trajectories and mine them efficiently. OD pairs covered: OD, O1, O2, O3, O4, 1D, 2D, 3D, 4D 12, 13, 14, 23, 24, 34

Indexing Schema and Search Trip Node 1 (loading point 1): Trajectory ID=1, nodePlacement=5 Trajectory ID=2, nodePlacement=3 …. Node 2 (loading point 2): Trajectory ID=2, nodePlacement=8 Trajectory ID=3, nodePlacement=10 Origin MAZ Destination MAZ Origin loading points (nodes) Destination loading points Trajectories containing origin node Trajectories containing dest. node Trajectories containing origin node and destination node in the right order: origNodePlacement<destNodePlacement Extract sub-trajectories meeting matching criteria Best representative sub-trajectory

Matching Levels (Excluding Intra-MAZ trips) TOD Temporal Spatial Occupancy VOT 1 AM, PM 5 min OMAZ, DMAZ All MD EA, NT 2 15 min EA, MD, NT 3 OTAZ, DTAZ 4 60 min 9 No match found / skims used

If multiple trajectories found: Chose the min discrepancy function Find the closest simulated individual trajectory (sub-trajectory) by the penalty function: Occupancy discrepancy × Weight=0.5 (if relevant) VOT discrepancy, $/h × Weight=0.1 (if relevant) Departure time discrepancy, min × Weight=0.05 Same origin TAZ, different MAZ × Weight=0.1 Same destination TAZ, different MAZ × Weight=0.1 Trajectory age penalty = (iter_ABM-iter_DTA-1) × 0.1

Trajectory Coverage Stats TOD Aggregation level 1 2 3 4 9 Total Before 6 83.0% 12.5% 0.2% 0.1% 4.2% 100.0% 6-10 61.3% 5.6% 19.5% 7.6% 5.9% 10-15 93.4% 5.7% 0.7% 15-19 66.6% 17.2% 6.2% After 19 92.0% 6.9% 0.0% 0.9% 77.7% 6.1% 9.6% 3.6% 3.0%

Travel Time Differences by aggLevel: Trajectory-Skim, min

Travel Time Differences by TOD: Trajectory-Skim, min

Example of Analysis of Time Budgets

Mode Choice Switch between Skim-Based run and Trajectory-Based Run (MORPC) mode/skims Mode/trajectories 1=SOV 2=HOV2 driver 3=HOV3 driver 4=HOV passenger 5=Walk to local bus 6=PNR local bus 7=KNR Local Bus 8=Walk to express bus 9=PBR express bus 10=KNR express bus 14=Walk 15=Bike 16=Taxi 17=School bus 153,858 - 93 99 16 13 2 9 200 11 39,470 22 4 5 1 58 6 33,943 3 21 7 84,368 119 17 15 136 23 88 10 20 4,783 535 222 211 69 78 21,642 968 1,676 8 208 53 12,482 C10 FHWA Webinar, November 1, 2016

Conclusions Deep integration of ABM and DTA is feasible: Already practical for regions under 1M Many additional new avenues Moving towards AgBM Runtime is an issue: Integration layer adds only a little DTA and ABM constitute major time-taking components, especially DTA

Contact(s) Peter Vovsha, PhD Jim Hicks, PhD Assistant Vice President, WSP Systems Analysis Group Peter.Vovsha@wsp.com Jim Hicks, PhD Principal Software Architect James.Hicks@wsp.com