1. VII Data Characteristics for Traffic Management: Task Overview and Update 21 June 2006 Karl Wunderlich Fellow, Transportation Analysis

2. 2ControlNumber Scope Examine capability of VII probe data to support (specifically): –Signal control –Ramp metering –Traveler information This capability must be examined with respect to key variables: –Facility type (arterial/freeway/rural) and geometry –Congestion levels and road/weather conditions –Market penetration –VII probe message management In-vehicle At the roadside and in backhaul communication Near-term analytical emphasis is on the support of Day 1 applications –For example, off-line periodic signal retiming versus “real-time” adaptive signal control

3. 3ControlNumber Objectives Identify likely content of collected VII probe messages passed to traffic managers or traveler information service providers under realistic conditions Develop (where possible) algorithms that will estimate key measures from the collected probe data, for example: –Vehicle volumes by lane and turning movements –Travel times and intersection delays Estimate the accuracy of these algorithms with respect to the key variables from previous slide (e.g., market penetration) Provide USDOT with an understanding of key tradeoffs along a spectrum of issues/conditions (e.g., privacy)

4. 4ControlNumber Staffing and Coordination Mitretek Systems Team Michael McGurrin Karl Wunderlich Meenakshy Vasudevan Emily Parkany Phil Tarnoff, U-Md. USDOT Task Manager Brian Cronin Use Case Development (BAH) VII Data Elements (PB) Key VII-Related Activities

5. 5ControlNumber Approach Data needs assessment –Define the data required by traffic management and traveler information applications –Qualitative assessment of data produced by VII to meet these identified needs Analytical assessment of VII probe data –Develop an analytical tool that takes… Vehicle trajectory data Specific probe message management strategy Assumed RSE deployment … and produces the associated VII probe data content –Trajectory data will come from a variety of sources: Observed (e.g., NGSIM or floating car data) Simulated (e.g., from a traffic simulation) –Develop algorithms to process this probe data into measures of interest (e.g., link travel time)

6. 6ControlNumber VII Data Characteristics Task Data Needs Assessment 1/1 Data Needs White Paper 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1 Prelim Matrix Analytical Tool Development and Evaluation Kickoff Briefing 1 Briefing 2 Downselect Strategies (I) Draft WP Day 1 Final Report (draft) Acquire/Prep Trajectory Data Build Trajectory Converter Acquire Traffic Simulation and Test Networks Enhance Converter Briefing 3 write-up Multi-RSE Strategy Evaluation Tradeoff Analyses write-up revisions Data Characteristics WP (final) Coordination/ Progress Briefings Downselect Strategies (II) Initial Strategy Development Initial Strategies Assess Needs Observed Data Track Simulation Track Expended Funding Remaining Funding Width indicates relative Mitretek LOE Deliverables Completed Planned (+ internal draft) 1 FTE Der. Algs. (II) 12 June 2006 Planned Coordination Meetings Completed Bi-Weekly Status Updates (Scheduled) Brian/Karl Full Team Completed Validate Sim Trajectories Preliminary Strategy Evaluation Derivation Algs. (I)

7. 7ControlNumber Key Deliverables Data Needs White Paper (completed) –Broad, qualitative assessment of Day 1 and later needs Applications Preliminary Requirements Matrix (completed) –High-level assessment of the capability of VII data to meet the identified short- and long-term needs Data Characteristics White Paper (1 September 2006) –Summary of findings, primarily from observed data analysis –Initial assessment of capability of VII probe data to support Day 1 applications Draft Day 1 Final Report (1 January 2007) –Update and expansion of the September white paper –Results from the analysis of simulated trajectories –More comprehensive assessment of key tradeoffs

8. 8ControlNumber From Trajectories to Measures Time Position Vehicle Trajectories Extract Sample Depending on Market Penetration 1 2 3 4 Populate With Snapshots According to Message Handling Strategy Process Snapshots To Estimate Measures Travel Time Queue Length Other

9. 9ControlNumber Observed Data Sets, Floating Car: Strengths and Weaknesses Floating car trajectory data –Strengths: Trajectories are long (30+ miles in some cases) Arterial, freeway, rural road facilities Light to heavy congestion conditions Some “other data” collected that looks like VII data elements (e.g., weather or turn signal disposition) –Weaknesses: Only one vehicle tracked Ground truth measures can’t be directly observed for aggregate traffic flow – just one vehicle Will be most valuable for looking at travel time derivation issues over longer links, potentially widely dispersed RSEs Road Weather Management

10. 10ControlNumber Observed Data Sets: NGSIM NGSIM data are high-resolution vehicle trajectory data –Processed video images from multiple high-angle cameras –Near 100% of all vehicle positions traced at 0.1 sec intervals –Detailed lane position and disposition to other vehicles –Two freeway data sets, one arterial data set Strengths: 100% vehicle coverage Weaknesses: Short coverage areas (under 1 km)

11. 11ControlNumber Simulated Vehicle Trajectories: Strengths and Weaknesses Simulated trajectory data –Strengths: Most facilities of interest can be modeled 100% tracking of vehicles Ground truth measures can be directly obtained Congestion levels and other elements can be systematically adjusted –Weaknesses: Validity of detailed trajectories under congestion is poorly understood Time and effort to build and calibrate realistic networks Will be most valuable when attempting to deal with incremental tradeoffs for key issues like market penetration and buffer size

12. Sample Trajectory Conversion: Columbus, Ohio: Route 33 and I-270 Run Type : GPS (Floating Car) Distance: 62.0 Miles Travel Time: 93.8 Minutes Average Speed: 39.6 mph RSE Spacing : 2.3 miles between RSEs (on average) Snapshots per Mile: 10.0 Vehicle IDs (Transmit/Produced): 32 / 42 Snapshots per ID (Transmit/Produced): 9.4/13.7 Total number of Snapshots: 618 –Stop Snapshots: 23 –Start Snapshots: 13 –Periodic Snapshots: 582

13. Columbus, Ohio Expected RSE Location, GPS Trace

14. Walk-Through of Default VII Probe Message Process Location: –A congested segment on I-270 What we will examine: –50 Snapshots taken right after vehicle RSE interaction Time –3133 to 3448 seconds (5.25 minutes) Distance: –1.9 Miles

15. I-270 Route

16. Time 3133-3244 (1.85 Min) 0.69 Miles 43 secs (7 SS) Spd 20-28 12 secs (4 SS) Spd 0-9 T 3196 (1 Stop) 48 Secs (1 Start) Spd 10.5 Periodic 11Stop 1Start 1Capacity 13/30 Periodic 0Stop 0Start 0Deleted

17. Time 3244 – 3356 (1.87 mins) 0.69 Miles 12 secs 4 SS Spd 12-19 T 3356 (1 Stop) Periodic 27Stop 2Start 1Capacity 30/30 29 secs 6 SS Spd 22-32 14 secs 2 SS Spd 43 1 secs 1 SS Spd 19 20 secs 7 SS Spd 4-12 Buffer is full 3.25 mins after the last vehicle RSE Interaction Periodic 4Stop 0Start 0Deleted Deleted from SS from Time 3133- 3151 (0.3 mins)

18. Time 3356- 3448 (1.9 mins) 0.54 Miles 4 Secs (1 Start) Spd 11.0 20 Secs (5 SS) Spd 13-19 41 Secs (10 SS) Spd 13-19 Periodic 26Stop 2Start 2Capacity 30/30 Periodic 20Stop 0Start 0 Deleted Deleted from SS from Time 3133- 3276 (2.4 mins) Does not report to a RSE for another 4.7 Mins

19. Deleted SS Time 3133-3244 (1.85 Min) 0.69 Miles

20. Deleted SS Time 3244 – 3356 (1.87 mins) 0.69 Miles 95 additional snapshots are deleted before The vehicle interacts with another RSE

21. Deleted Snapshots by Location First RSE Interaction Last RSE Interaction

22. Estimating Travel Time from Snapshots OVERALL Actual = 94 minutes Calculated = 67 minutes Error = 29% A =321 C = 159 E = 50% A = 574 C = 242 E = 58% A =100 C = 120 E = 20% A =279 C = 220 E = 21% A = 160 C = 195 E = 22% A =363 C = 292 E = 20% A = 151 C = 174 E = 15% A = 120 C = 100 E = 17% A =200 C = 179 E = 11% Actual = 234 sec Calculated = 236 sec Error = 1% A = 260 C = 262 E = 1% A = 460 C = 483 E = 5% A = 154 C = 168 E = 9%

23. Preliminary Observations For uncongested conditions: –the default strategy provides fairly good geographic coverage and accuracy For congested conditions even with relatively closely spaced RSEs: –The default plan results in significant buffer overflow –The deleted snapshots leave significant geographic gaps –Gaps have impact on accuracy of travel time estimation

24. Analysis: Next Steps Evaluate more Data Sources –Columbus, Ohio GPS –Salt Lake City, Utah I-15 GPS runs –Dulles Toll Road GPS runs –I-66/Route 50 GPS runs –NGSIM validation data Evaluate VISSIM simulated runs Test alternative thresholds and strategies for VII probe message process Test sensitivity to a range of RSE locations and densities