Dynamic OriginDestination Trip Table Estimation for Transportation Planning

1
Dynamic Origin-Destination Trip Table Estimation
for Transportation Planning

• Ramachandran Balakrishna
• Caliper Corporation
• 11th TRB National Transportation Planning
  Applications Conference, Daytona Beach, Florida
• 9th May, 2007

2
Outline

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

3
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

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Within-Day Traffic Dynamics

• I-405, Orange County, CA

Source PeMS on-line database

• Temporal dynamics
• Complex interactions of network demand
• Aggregation error

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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

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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

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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)

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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

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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

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Case Studies

• Irvine, CA
• South Park, Los Angeles, CA
• Lower Westchester County, NY

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Irvine, CA1
1Balakrishna, R., H.N. Koutsopoulos and M.
Ben-Akiva (2005) Calibration and Validation of
Dynamic Traffic Assignment Systems. Mahmassani,
H.S. (ed.) Proc. 16th International Symposium on
Transportation and Traffic Theory, pp. 407-426.
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South Park, Los Angeles, CA1
1Balakrishna, R., M. Ben-Akiva and H.N.
Koutsopoulos (2007) Off-line Calibration of
Dynamic Traffic Assignment Simultaneous
Demand-Supply Estimation. Transportation Research
Record (forthcoming).
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Lower Westchester County, NY1
1Balakrishna, 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).
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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