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Using Archived ITS Data to Automatically Identify Freeway Bottlenecks in Portland, Oregon

Using Archived ITS Data to Automatically Identify Freeway Bottlenecks in Portland, Oregon

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Using Archived ITS Data to Automatically Identify Freeway Bottlenecks in Portland, Oregon

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  1. Using Archived ITS Data to Automatically Identify Freeway Bottlenecks in Portland, Oregon Robert L. Bertini, Rafael J. Fernández-Moctezuma,Jerzy Wieczorek, Huan Li, Portland State University 15th World Congress on ITS New York City, NY November 17, 2008

  2. PORTAL database Loop Detector Data 20 s count, lane occupancy, speed from 500 detectors (1.2 mi spacing) Bus Data 1 year stop level data 140,000,000 rows Incident Data 140,000 since 1999 Data Archive Days Since July 2004 About 300 GB 4.2 Million Detector Intervals VMS Data 19 VMS since 1999 Weather Data

  3. Objectives • How can we automate bottleneck detection? • How can we analyze the resulting detected bottlenecks?

  4. What is a Bottleneck? Queueing upstream Freely-flowing downstream Temporal and spatial variation Bottleneck Unqueued Queued Detectors

  5. Why study bottlenecks? • Find and rank recurrent bottlenecks(via data archive) Planners know where to focus congestion-reduction efforts • Detect bottlenecks in real time Improve incident detection andtravel time predictions

  6. Research objectives • Refine an algorithm to systematically detect freeway bottlenecks, and quantify and visualize their impacts • Implement this tool in PORTAL, our continuously-updated transportation data archive

  7. Reading a contour plot InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288

  8. Reading a contour plot InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288

  9. Reading a contour plot InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288

  10. Contour plots in real time InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288 ?

  11. InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288 Mockup of desired tool

  12. Data • I-5 Northbound corridor has best loop detector coverage: 23 detectors over 24 miles, giving 1.1 mi average detector spacing • Chose 5 representative days for initial testing • Averaged data across all 3 lanes, removed bad detectors, and imputed missing values MP 308 MP 284

  13. Our starting point • Based on a California field experiment • Using 5-minute aggregated data,declare a bottleneck betweentwo detectors in a given timeperiod if: • Speed difference acrossbottleneck is > 20mph, and • Upstream speed is < 40 mph • “Sustained bottlenecks” filter: • Remove outliers with too few “neighbors” • Fill in any small gaps within bottlenecks

  14. Success and False Alarm Rate Tables

  15. Success rate over all 5 days(using sustained filter)

  16. Success rate over all 5 days(using sustained filter)

  17. Success rate over all 5 days(using sustained filter)

  18. Success rate over all 5 days(using sustained filter)

  19. Success rate over all 5 days(using sustained filter)

  20. False alarm rate over all 5 days(using sustained filter)

  21. False alarm rate over all 5 days(using sustained filter)

  22. False alarm rate over all 5 days(using sustained filter)

  23. False alarm rate over all 5 days(using sustained filter)

  24. False alarm rate over all 5 days(using sustained filter)

  25. Bottleneck detection results • Optimized parameter values for our chosen Portland freeway corridor • Validated this method on Oregon data as a good start: • It successfully finds 75% of bottlenecks • Only 20% of detections are false alarms

  26. Bottleneck analysis tools • Find entire congested area upstream of the bottleneck: • estimate queue propagation speed • calculate costs of delay, emissions, etc • Process historical data and find prior probabilities to improve real-time detection

  27. InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288 Congestion tracking

  28. InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288 Congestion tracking

  29. InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288 Queue propagation speeds 7.66 mph 25.8 mph 14.1 mph

  30. InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288 Delay (in vehicle-hrs) 26403veh-hrs 1622 veh-hrs 1569 veh-hrs

  31. InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288 Congestion tracking

  32. InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288 Congestion tracking

  33. InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288 Congestion tracking

  34. InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288 Congestion tracking

  35. Congestion Tracking: 90% Of Days InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288

  36. Congestion Tracking: 75% InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288

  37. Congestion Tracking: 50% InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288

  38. InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288 Congestion tracking: rarest 10%

  39. InterstatebridgeMP 308 I-405MP 304 I-84MP 302 I-405MP 300 OR-217MP 292 I-205MP 288 Mockup of desired tool

  40. Next steps • Set parameters for remaining corridors; implement into PORTAL; solicit feedback • Improve detection algorithm: incorporate weather conditions, occupancy/flow data, historical knowledge, etc. • Distinguish incidents from recurrent congestion; rank the latter on Portland’s freeways

  41. Acknowledgments • Oregon Department of Transportation • Federal Highway Administration • TriMet • The City of Portland, OR • National Science Foundation • CONACYT (Mexico) • TransPort ITS Committee Visit PORTAL Online: http://portal.its.pdx.edu

  42. Thank You! www.its.pdx.edu