1 / 73

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

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

rosina
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related