1. Princeton University Prospect Eleven Dec. 6, 2005 Towards Real-Time MinETA Routing Tuesday, December 6, 2005 RPI, Troy, NY Alain L. Kornhauser Professor, Operations Research & Financial Engineering

2. Princeton University Prospect Eleven Dec. 6, 2005 Finally… Convergence of GPS Real-Time Decisions that substantially improve the Bottom line of Carriers & Shippers & Robust OS & Memory Real-Time Mobile Information & Wireless & & Processing Optimal Real-Time Management & Control of Mobile Assets

3. Princeton University Prospect Eleven Dec. 6, 2005 16 1 1 1 2 2 2 3 3 3 4 3 55 6 6 8 8 9 6 PROBLEM: How to get from A to B Many Paths, Each with a Different Value to the Decision Maker Company Fundamental 4

4. Princeton University Prospect Eleven Dec. 6, 2005 Including Real-Time Information: Concepts Peak Hour Characteristics & Return to Normalcy During Peak Hours, Traffic Patterns Remain at a relatively constant distance to Historical Estimate There will be a time at which traffic patterns will return to free flow conditions Moorland - Downtown Burleigh - Zoo

5. Princeton University Prospect Eleven Dec. 6, 2005 Method of “smoothing” a time series of observations Most recent observations are given a high weight and previous observations are given lower weights that decrease exponentially with the age of the observation Including Real-Time Information: Concepts Exponential Smoothing Single Double Triple

6. Princeton University Prospect Eleven Dec. 6, 2005 Including Real-Time Information: Solution During Peak Periods: Adaptation of Double Exponential Smoothing Trend is the Trend of the Historical Estimate Observation weighted with Most Recent Estimate + Slope for Smoothed Estimate Forecast done by adding trend to most recent estimate

7. Princeton University Prospect Eleven Dec. 6, 2005 Including Real-Time Information: Solution During Non-Peak Periods Adaptation of Double Exponential Smoothing Trend is decay to free flow Conditions

8. Princeton University Prospect Eleven Dec. 6, 2005 (c) (b) (a) Figure: Empirical testing of forecasting algorithm. (a) Forecast from most recent observation. (b) Weighting on most recent observations. (c) Realization of travel time data.

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23. Princeton University Prospect Eleven Dec. 6, 2005 Real-Time Congestion Dependent Sat/Nav 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 “Advance” project Illinois Universities Transportation Research Consortium The late 90s Conducted it’s version of the abandoned “Advance” project Link &

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26. Princeton University Prospect Eleven Dec. 6, 2005 Record & Send: Mi, Mj, Tj-Ti

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28. Princeton University Prospect Eleven Dec. 6, 2005 Cell Phones as Traffic Probes: A Way to Get Started?

29. Princeton University Prospect Eleven Dec. 6, 2005 Cell Probe Technology Part of general trend away from fixed sensors toward vehicle- based information Reflects frustration with high costs and slow pace of deployment for traditional sensors Characteristics: –Low cost –full regional coverage –performance-based, and –self sufficient business model

30. Princeton University Prospect Eleven Dec. 6, 2005 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

31. Princeton University Prospect Eleven Dec. 6, 2005 Cell Probe Technology

32. Princeton University Prospect Eleven Dec. 6, 2005 Cell Probe Privacy

33. Princeton University Prospect Eleven Dec. 6, 2005 Handset 49, part 1

34. Princeton University Prospect Eleven Dec. 6, 2005 Handset 49, part 2

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36. Princeton University Prospect Eleven Dec. 6, 2005 Handset 49, part 4

37. Princeton University Prospect Eleven Dec. 6, 2005 Handset 49, part 5

38. Princeton University Prospect Eleven Dec. 6, 2005 Handset 49, part6

39. Princeton University Prospect Eleven Dec. 6, 2005 Handset 49, full trip

40. Princeton University Prospect Eleven Dec. 6, 2005 Handset 49, full trip

41. Princeton University Prospect Eleven Dec. 6, 2005 Path-Finding Drive Tests

42. Princeton University Prospect Eleven Dec. 6, 2005 Possible Applications Travel Times –For message signs; web sites Performance measures –Include arterial network –“Top 10” routes –TTI-type reports Operations planning –Special events –Work zone management –Evaluation of actions Safety –Focus on problem areas and assessments Port/intermodal access Local/regional web sites Statewide coverage

43. Princeton University Prospect Eleven Dec. 6, 2005 Applications General Planning and Management –Regional congestion management –Archived data supports system analysis, ”average day” information, long-range planning –Integrated regional or corridor management –Plan for “extreme” or special events –Homeland security applications – no-notice evacuations –Rapid evaluation of alternatives –Work zone management –Rural planning and operations –Traffic volume estimates -- future

44. Princeton University Prospect Eleven Dec. 6, 2005 Applications (2) Performance Measurement –System performance in near real time –Reliability measures – critical from user’s perspective (travel time index, planning time index, etc.) –Performance-based systems – information for operators, users, and the public –Congestion management – support for HOT lanes and other finance alternatives –Economic value from partnerships with business – the DOT a part of just in time delivery

45. Princeton University Prospect Eleven Dec. 6, 2005 Applications (3) Travel Demand and Air Quality Modeling –Today – Validate travel demand and Mobile6 models –Tomorrow – origin/destination data –Tomorrow – New model development: activity-based and beyond Safety –Analysis and prediction –Targeted deployment of safety personnel Communication –Public participation – real data on congestion –Near real-time data – web, PDA, 511 –Premium 511 service

46. Princeton University Prospect Eleven Dec. 6, 2005 Applications (4) Freight Operations –Web- or cell-based distribution of roadway information –Individual dynamic routing recommendations based on congestion –Travel time prediction to improve asset utilization Freight Analytics –Strategic analysis of freight movement for congestion mitigation –Origin/destination data to examine flows and set priorities –Support for cost/benefit and alternatives analysis

47. Princeton University Prospect Eleven Dec. 6, 2005 Safety Example Operational tool –Assign patrol cars to road segments based on: Average speed – Z percent above speed limit Trigger points – X percent of traffic more than Y percent above speed limit –Identify trends and historical patterns –Short-term forecasts Evaluation tool – near real-time –Assess what worked and how well –Statistical analysis of patterns

48. Princeton University Prospect Eleven Dec. 6, 2005 Safety Example Speed Limit