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PROBLEM: How to get from A to B Many Paths Each with a Different Value to the Decision Maker PowerPoint Presentation
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PROBLEM: How to get from A to B Many Paths Each with a Different Value to the Decision Maker

PROBLEM: How to get from A to B Many Paths Each with a Different Value to the Decision Maker

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PROBLEM: How to get from A to B Many Paths Each with a Different Value to the Decision Maker

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  1. 3 2 6 1 6 1 3 2 A 9 B 8 2 5 5 6 8 3 3 1 6 1 4 A B 4 Addressing the Real-time Aspects In Turn-by-turn Navigation • PROBLEM: How to get from A to B • Many Paths • Each with a Different Value to the Decision Maker • Each Segment Changing with Uncertainty over Time Week 8

  2. Link Travel Times Historic, Actual & Forecast During Day One week-day on one link Things change! Week 8

  3. The Measurement Problem • How to collect the real time Speed Data? • Incremental Infrastructure • In pavement loop detectors (single point) • radar/laser/video signpost systems (single point) • EZ Pass readers (2 point span measurement, Excellent) • Processing “Existing” Data • Wireless Location Technology (Cellular Probes, see Fontaine, et al) • Cell-tower trilateration • Yet to demonstrate sufficient accuracy • Cell-handoff processing • maybe OK for simple networks • Floating Car (Vehicle Probe) data processing (see Demers et al) Week 8

  4. Cell Probe Technology • Practical success requires more than cell phones • Cell phone movement based on cell location and “hand-offs” from one cell to another • Pattern recognition techniques filter out data from those not on the highway • Then traffic algorithms generate travel times and speeds on roadway links • Cell phones need to be turned on, but not necessarily in use • Full regional systems in place in Baltimore, Antwerp, and Tel Aviv = 4,600 miles, Shanghai Week 8

  5. Cell Probe Technology Week 8

  6. Cell Probe Privacy Week 8

  7. Handset 49, part 1 Week 8

  8. Handset 49, part 2 Week 8

  9. Handset 49, part 3 Week 8

  10. Handset 49, part 4 Week 8

  11. Handset 49, part 5 Week 8

  12. Handset 49, part6 Week 8

  13. Handset 49, full trip Week 8

  14. Handset 49, full trip Week 8

  15. Path-Finding Drive Tests Week 8

  16. Baltimore MMTIS • Provides first regional deployment of commercial-quality cellular traffic probes in North America • Mutually profitable public-private partnership • Test commercial markets during project • Integrate with existing public data – including transit and E-911 • Encourage public applications beyond traditional ITS • Contract signed September 2004; data flow to Maryland DOT began April 2005 Week 8

  17. Baltimore MMTIS – Private Firms • Delcan-NET • Transportation and technology consultants • Fifty plus years in business • Profitable every year; staff = 500 plus • ITIS Holdings • Leader in traffic probes; staff = 100 • Commercial customers – 16 automobile firms, for-profit 511 • Profitable! • Publicly traded on London exchange • National cellular firms Week 8

  18. Week 8

  19. MARYLAND DOT CAMERAS SHOW ACCURACY OF TRAFFIC INFORMATION BEING CAPTURED USING CELL PROBES I-695 at HARTFORD ROAD Monday, June 6th 2005 9:02:18 am Week 8

  20. CELL PROBES ACCURATELY UPDATE TRAFFIC CONDITIONS AS CHANGES OCCUR I-695 at HARTFORD ROAD Monday, June 6th 2005 9:33:06 am Week 8

  21. Produced by Dr Hillel Bar Gerd, Associate Professor, Ben Gurion Negev University, Israel Week 8

  22. Baltimore Comparison with RTMS Data Week 8

  23. Analysis Route Overview Week 8

  24. Performance data I-695 – July 2005 Week 8

  25. Baltimore I-695 Weekday Patterns Week 8

  26. Baltimore I-695 Saturday Patterns Week 8

  27. Baltimore I-695 Route Travel Time Week 8

  28. Week 8

  29. Week 8

  30. Week 8

  31. Vehicle Probes • Assign Speed data to network segments of Digital Map database, or • Maintain travel times between strategically located virtual monuments Week 8

  32. (mi, mj) near Troy (mi, mj) larger area North American Monument Network • ~125,000 North American “Monuments” • ~106 (mi, mj) • Can create Median travel Tims by Time-of-Day • For Example: AM Peak, Midday, PM Peak, Night, Weekend day Week 8

  33. > 40 mph < 40 mph 1:30pm 11/14/07 Median Speed (by direction) on National Highway Network 1:30pm 11/14/07 height ~ speed Week 8

  34. Average Speed (by direction) on National Highway Network 1:30pm 11/14/07 > 40 mph < 40 mph height ~ speed 1:30pm 11/14/07 Week 8

  35. “Advance” project The late 90s & Illinois Universities Transportation Research Consortium Real-Time Dynamic Minimum ETA Sat/Nav Conducted its version of the abandoned “ADVANCE” (Advanced Driver and Vehicle Advisory Navigation ConcEpt)project • 250 Volunteers using CoPilot|Live commuting to/from RPI • CoPilot continuously shares real-time probe-based traffic data • CoPilot continuously seeks a minimum ETA route Won ITS America’s 2007 “Best Innovative Research” Award Week 8

  36. Project Objectives • Create: real-time data collectionfrom vehiclesanddisseminationto vehiclesof congestion avoidance information which is used to automatically reroute drivers onto the fastest paths to their destinations • Target locations: small to medium-sized urban areas • Aspects: operations, observability, controllability, users, information transfer to travelers Week 8

  37. Experiment Details 3-month field test Capital District (Albany), NY, USA Journey-to-work 200 participants 80 Tech Park employees 120 HVCC staff & students “Techy” travelers Network: Freeways & signalized arterials Congested links Path choices exist Week 8

  38. 6 2, 4 Customized Live|Server at ALK Customized CoPilot|Live In vehicles 7 3 1 5 8 Destination Basic Operational Architecture Two-way cellular data communications between Week 8

  39. If Momument, mj , is passed Send mi, mj, ttk(mi, mj)= t(mi) - t(mi) (52 bytes) ALK Server Updates: TT(mi, mj ) CoPilot|Live “Where Am I”, Then… Every Second CoPilot|Live Determines “Where am I”, Then… Set i=j Week 8

  40. CoPilot|Live … Send… Current Location & Destination, Last update time (42 bytes) CoPilot|Live Sends: “Where am I”, Dest., Last update Receives/Posts: updates Computes: MinETA Updates route, if better ALK Server Builds: setUk Sends: TT(mi, mj ) for every (i,j) in Uk Every “n” Minutes ALK Server … Determines Uk : set of TT(mi, mj ) within “bounding polygon” of (Location;Destination)k that have changed more than “y%” since last update. ALK Server … Send… New TT(mi, mj ) for every (i,j) in Uk (280 bytes/100arcs) CoPilot|Live … Updates TT(mi, mj ) in Uk , ETA on current route, Finds new MinETA route, if MinETA “substantially” better then… Adopt new route Week 8

  41. ALK Server Updates: TT(mi, mj ) When Available ALK Server … Receives: Other congestion information from various source, blends them in TT(mi, mj ) Week 8

  42. What We Heard I'm very impressed with the CoPilot program thus far. The directions are accurate and it adapts quickly to route changes. I find it interesting how willing I am to listen to a machine tell me which route to take I like using it for when I have no idea on how to get somewhere, and it is good for my normal route because it keeps me out of traffic on route 4. This thing is awesome. I was a little skeptical at first but once i got the hang of it I don’t know how I went along without it. I think any student commuting to school will benefit from this. It is great, it took a while to trust it telling me where to go, but i like it because i cant get lost! Thanks. Week 8

  43. also Can Watch Vehicles 1 2 3 Week 8

  44. Forecasting Travel Times Using Exponential Smoothig Week 8

  45. Historical Expectation: Concepts • Patterns Differ over Days & Time of Day • Most Significant Difference is Between Weekdays and Weekends Zoo Interchange – Hale Interchange (All Days) Week 8

  46. Historical Expectation: Concepts • Two Peak Periods • Each appears to be Bell Shaped • Afternoon Peak Period Appears to have “Extra Hump” Week 8

  47. Historical Expectation: Solution Week 8

  48. Historical Expectation: Application Downtown – Zoo Interchange Minimize the SSE between Historical Estimation Function and actual data points Week 8

  49. Historical Expectation: Application Week 8

  50. Using Real-Time Information to Improve our Estimate Week 8