Dynamic Origin-Destination Trip Table Estimation for Transportation Planning Ramachandran Balakrishna Caliper Corporation 11 th TRB National Transportation Planning Applications Conference, Daytona Beach, Florida 9 th May, 2007

Outline Introduction Within-day traffic dynamics Limitations of static methods Short-term planning methods Obtaining dynamic OD flows Case studies

Introduction Long-term planning –Land use, residential location choice –Infrastructure development Short-term planning –Congestion and incident management –Work zone scheduling –Special events preparation –Evacuation planning

Within-Day Traffic Dynamics I-405, Orange County, CA Temporal dynamics –Complex interactions of network demand –Aggregation error [Source: PeMS on-line database]

Limitations of Static Methods Temporal patterns “averaged out” –Average trip rates over long periods –Daily, Peak (AM, PM), Off-Peak (MD, NT) Boundary conditions inconsistent –Trips assumed to finish within single period –Departure time effects ignored Capacity, dynamic traffic evolution ignored –Volume/capacity ratios can exceed unity –No queue formation and dissipation, spillbacks

Short-Term Planning Methods Growing popularity of dynamic models –Microscopic simulation –Dynamic Traffic Assignment (DTA) Key input: origin-destination (OD) matrices –OD departure rates by time interval –Interval width: 5 min, 15 min, 1 hour

Obtaining Dynamic OD Flows OD surveys –OD information collected directly –Costly, difficult to repeat / update Profiling of static matrices –Not based on real measurements –Can be counter-intuitive (e.g. negative flows) OD Estimation –Match actual traffic data (e.g. detector counts) –Data are up-to-date, easy to collect –OD information is indirect (requires modeling)

Dynamic OD Estimation Steps Start with initial OD flow estimates −e.g. Derived from static matrix Assign them to the network −Dynamic network loading model Compare assigned output to data −Goodness of fit statistics Adjust OD flows, iterate to convergence −Optimization algorithms

Challenges OD departures appear in future intervals Data collection: –Loop detector counts are widespread –Richer data are becoming available Easy to match counts –Harder to match speeds, travel times, queue lengths Most methods are tailored for counts –Recent methods include other data

Case Studies Irvine, CA South Park, Los Angeles, CA Lower Westchester County, NY

Irvine, CA 1 1 Balakrishna, R., H.N. Koutsopoulos and M. Ben-Akiva (2005) Calibration and Validation of Dynamic Traffic Assignment Systems. Mahmassani, H.S. (ed.) Proc. 16 th International Symposium on Transportation and Traffic Theory, pp

South Park, Los Angeles, CA 1 1 Balakrishna, R., M. Ben-Akiva and H.N. Koutsopoulos (2007) Off-line Calibration of Dynamic Traffic Assignment: Simultaneous Demand-Supply Estimation. Transportation Research Record (forthcoming).

Lower Westchester County, NY 1 1 Balakrishna, R., C. Antoniou, M. Ben-Akiva, H.N. Koutsopoulos and Y. Wen (2007) Calibration of Microscopic Traffic Simulation Models: Methods and Application. Transportation Research Record (forthcoming).

Conclusion Time-dependent OD flows –Critical for short-term planning, simulation Dynamic OD estimation –Practical for real networks and data –Several approaches using counts –Recent advances allow general traffic data Thrust areas –Collecting richer data for large networks