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![Page 1: Dynamic Origin-Destination Trip Table Estimation for Transportation Planning Ramachandran Balakrishna Caliper Corporation 11 th TRB National Transportation.](https://reader035.fdocuments.in/reader035/viewer/2022081519/56649ef45503460f94c08160/html5/thumbnails/1.jpg)
Dynamic Origin-Destination Trip Table Estimation for
Transportation Planning
Ramachandran BalakrishnaCaliper Corporation
11th TRB National Transportation Planning Applications Conference, Daytona Beach,
Florida9th May, 2007
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Outline
• Introduction• Within-day traffic dynamics• Limitations of static methods• Short-term planning methods• Obtaining dynamic OD flows• Case studies
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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
0
2000
4000
6000
8000
10000
12000
14000
0 50 100 150 200 250 300 350
Time of Day
Flo
w (
veh
/ho
ur)
Hourly Flows
5-Min Flows
• Temporal dynamics– Complex interactions of network demand– Aggregation error
[Source: PeMS on-line database]
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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
![Page 9: Dynamic Origin-Destination Trip Table Estimation for Transportation Planning Ramachandran Balakrishna Caliper Corporation 11 th TRB National Transportation.](https://reader035.fdocuments.in/reader035/viewer/2022081519/56649ef45503460f94c08160/html5/thumbnails/9.jpg)
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. 16 th 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