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Assessment and Refinement of Real-Time Travel Time Algorithms for Use in Practice

Assessment and Refinement of Real-Time Travel Time Algorithms for Use in Practice. Project Goals. Determine best approach for travel time estimation for real-time applications Recommend algorithm Midpoint Coifman

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Assessment and Refinement of Real-Time Travel Time Algorithms for Use in Practice

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  1. Assessment and Refinement of Real-Time Travel Time Algorithms for Use in Practice

  2. Project Goals • Determine best approach for travel time estimation for real-time applications • Recommend algorithm • Midpoint • Coifman • Provide statistical analysis so performance of algorithm is understood under different conditions (free-flow, congestion, incidents(?)) • Provide confidence in travel time estimations

  3. Task 1: Impact of Various Factors on Travel Time Estimation • Investigate impact of several factors on travel time estimation • Detector Spacing • Algorithm • Data Quality • Highway geometry • Today: • Initial results on Detector Spacing and Algorithm • Very preliminary results on Data Quality • Deliverable: Full results at next meeting (Nov) • Note: Expansion and extension of Task 1 in work order

  4. Task 2: Ground Truth Data Collection • Ground Truth Collection to be done by consulting company • $5000 budget for data collection • Initial set of runs in October/early November • Select corridors and try to finalize plan today • Analyze data from runs by early January • Second set of runs Jan/Feb 2007 • Deliverable: Initial Collection done by Nov 10, 2006

  5. Task 3: Sensitivity Analysis • What input parameters are algorithms sensitive to? • Reveal biases the algorithms may have to different parameters • Include study of work using Kalman filters (most recent ITS seminar) • Real-time and deals well with dirty data • Survey other algorithms proposed and in use • Deliverable: Presentation/Memorandum Nov 10, 2006

  6. Future Tasks • Task 4: Algorithm Refinement • Technical Memorandum due Dec 1, 2006 • Task 5: Detailed Comparative Study of Algorithms • Technical Memorandum due March 23, 2007 • Task 6: Draft Final Report • Due May 18, 2007 • Task 7: Final Report • Due June 15, 2007

  7. Current Work • Travel Time Estimation Algorithm Comparisons • Coifman Algorithm • Midpoint Algorithm (ODOT algorithm) • Quantification of Travel Time Estimation Error • Detector Spacing • Data Quality • Road Geometry • Algorithm

  8. Algorithm Comparisons • Travel time estimates from archived loop data • Coifman algorithm • Four different scenarios • Midpoint algorithm • Two different scenarios • Probe vehicle data • Probe cars • TriMet bus data • Variety of traffic conditions • Congested vs. Free Flow • Incidents

  9. Free Flow Conditions

  10. Incident Conditions (Congestion)

  11. Large Detector Spacing

  12. Travel Time Estimation Errors

  13. Error vs. Detector Spacing

  14. Error Vs. Spacing contd….

  15. Data Quality

  16. Loop Detectors On I-84 33rd Ave (mp 2.1) Indicates WB detectors

  17. Detector Locations on US 26 EB detectors 26 @ 405, mp 73.62 Skyline, mp 71.37

  18. Data Quality Flags • Data is flagged as invalid if it meets any of the following criteria (adapted from TTI criteria) • 20 second count > 17 • Occupancy > 95% • Speed > 100 MPH • Speed < 5 MPH (probably being removed) • Speed = 0 and Volume > 0 • Speed > 0 and Volume = 0 • Occupancy > 0 and Volume = 0 • Data quality is determined (in part) by percentage of 20-second readings for which a detector fails one of the above tests

  19. Ground Truth Collection • Two Phases (Pilot Phase, Final Phase) • Phase 1: Soon (October/early November) • Phase 2: January/February • Focus on only two corridors in initial phase • Second phase may add additional corridors • Initial Number of Runs (my calculations show ~50 runs for 5% error at 95% confidence) • Start with 20 runs/corridor • Getting quotes from several firms

  20. Ground Truth Data Collection • Corridor Selection Criteria (Adapted from Sue Ahn’s criteria for SWARM project) • Must have moderate level of recurrent congestion • Require reasonable loop detector spacing to ensure good evaluation of algorithms • Ideally detectors have high data quality • Construction Schedule – avoid times/areas when there is construction

  21. Detector Locations I-5 S of Downtown

  22. Detector Locations - 217

  23. I-5 N Wed, Oct 4, 2006 traffic flow

  24. I-5 S, Wed, Oct 4, 2006 traffic flow

  25. 217 N, Wed, May 17, 2006 traffic flow

  26. 217 S, Wed, May 17, 2006 traffic flow

  27. I-205 N, Wed, Oct 4, 2006 traffic flow

  28. I-205 S, Wed, Oct 4, 2006 traffic flow

  29. How Good is Good Enough? > 5% accuracy, limited benefit Below this line, commuter is better off using historical experience (13%-21% accuracy) Data is for Los Angeles Source: Travel Time Data Collection for Measurement of Advanced Traveler Information Systems Accuracy (Toppen, Wunderlich) June 2003, MTS Systems

  30. What do you want? • What are your expectations for the project? • What is a ‘good enough’ estimate? • Maximum allowable error? • Is assumption 8%-10% accuracy ‘good enough’ OK? Should this be investigated more? • Can we prioritize recurring congestion over incidents? • Which corridors are a priority to you? • So we can concentrate on those corridors (probe vehicle data  collection etc.)

  31. I-84 (East and Westbound) • Limited number of loop detectors and poor data quality • I-405 (North) • Relatively short (≈ 3.5 miles) and limited loop detectors • I-405 (South) • This freeway corridor is relatively short (≈ 3.5 miles), lightly congested during peaks • US-26 (East and Westbound) • Was under construction – what is data quality like on 26? • OR217 Northbound • Sue had problems with the queue location – when are we getting detectors again? • OR217 Southbound • Looks pretty good – when are detectors going to be turned on? • I-205 Northbound • Looks pretty good. When are new loop detectors going in? • I-205 Southbound • This corridor is lightly congested during the peak periods. The speed remains above 40 mph throughout the entire corridor. • I-5 Upper-section Northbound • Poor data quality • I-5 Upper-section Southbound • Poor data quality?? • I-5 Lower-section Southbound • A recurrent bottleneck is located near the Wheeler Ave. on-ramp. The resulting queue, however, usually propagates only 2 – 3 miles upstream. • A queue that forms near Wheeler Ave. often overrides the upstream bottleneck near Columbia Blvd (in the upper-section of I-5). In this case, the entire queue propagates upstream of the Interstate bridge, where loop detector data are not available to PSU. • I-5 Lower-section Northbound • There are several of sections along this corridor where the spacing of adjacent loop detectors is very large. 2.5 miles between Terwilliger Blvd. and Macadam Ave., 3 miles between Nyberg Rd. and Stafford Rd.

  32. In terms of loop detector spacing, ORE 217 southbound and I-205 northbound show relatively small average spacing (≈ 0.7 and 1.1 miles respectively) as well as smaller maximum spacing (< 2 miles) compared to the other two candidate corridors. Hence, measurements from the loop detectors on these two corridors will provide better assessment of freeway conditions and their dynamics.

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