1. © 2015 Noblis, Inc. Measure Estimation Using Connected Vehicle Data Meenakshy Vasudevan James O’Hara TRB HCQS Committee Mid Year Meeting Connected Vehicle Data Workshop 3 June 2015 Study performed for James McCarthy and Gene McHale under the BSM Emulator Task, sponsored by the Data Capture and Management Program, ITS Joint Program Office

2. 22 © 2015 Noblis, Inc. Agenda  Two Fundamental Connected Vehicle Messages SAE J2735 Basic Safety Message (BSM) and Probe Data Message (PDM)  Study Goals and Objectives  Experimental Plan Overall Approach Measures Definitions and Algorithms – Queues at Known Bottleneck Locations, Route Travel Times  Results  Next Steps

3. 33 © 2015 Noblis, Inc. SAE J2735 Basic Safety Message (BSM) Part 1  SAE J2735 provides a set of rules for broadcasting BSM Messages of time stamp, vehicle position, speed, acceleration, vehicle system status, are broadcast every 10th of a second Protects vehicle anonymity (no vehicle ID) No messages are stored on the Onboard Unit (OBU) Broadcast messages are received via Dedicated Short Range Communications (DSRC) by connected vehicles and Roadside Units (RSU) in range DESIGNED TO SUPPORT V2V (Vehicle to Vehicle) SAFETY APPLICATIONS

4. 44 © 2015 Noblis, Inc. SAE J2735 Probe Data Message (PDM)  SAE J2735 provides a set of rules for storing and sharing PDM Snapshots of time stamp, vehicle position, speed, acceleration, vehicle system status, temporary vehicle identifier (Probe Segment Number) are generated: – Periodic (every 4-20 seconds depending on vehicle speed) – Start/Stop (Stop: no movement for 5 seconds and no other stops for past 15 seconds; Start: speed exceeds 10 mph after a stop) – Event Triggered (change in vehicle status) Protects vehicle anonymity (privacy gaps; rollover of PSNs) Limited number of snapshots saved on the Onboard Unit (OBU) Snapshots packaged and transmitted to a Roadside Unit (RSU) when in range DESIGNED TO SUPPORT GENERAL V2I (Vehicle to Infrastructure) APPLICATIONS

5. 55 © 2015 Noblis, Inc. SAE J2735 Probe Data Message (PDM) (cont.)  PDM as a concept provides more network coverage, however… Re-identification is a privacy risk associated with tracking vehicles in the network  Privacy protocols for PDM First snapshot generated only after 500 m or 120 seconds, whichever occurs first, from origin PSN changes every 120 seconds or 1 km, whichever comes later After PSN changes, no snapshots are generated for 3 to 13 seconds, or 50 to 250 meters, whichever comes first After a vehicle sends snapshots to an RSU, all remaining snapshots with the PSN are purged and the vehicle does not communicate with that RSU for 6 minutes or 4 km, whichever comes first

6. 66 © 2015 Noblis, Inc. Study Goals  Develop tools for emulating mobile wireless messaging protocols, including BSM, PDM, and their variants  Examine effectiveness of BSM, PDM, and their variants transmitted via DSRC and non-DSRC technologies (e.g., cellular) in estimating key transportation measures under various scenarios  Compare the characteristics and attributes of the US, Japanese, and European messaging protocols  Explore the concept of Dynamic Interrogative Data Capture (DIDC) to curtail bandwidth overload

7. 77 © 2015 Noblis, Inc. Objectives  Assess effectiveness of BSM, PDM, and variants in estimating key measures  Assess impact of the following on the accuracy of measures estimation: Market penetration of connected vehicles Roadside Unit (RSU) densities (or deployment rates) Traffic conditions (e.g., high congestion, moderate congestion) Roadway Types (e.g., freeway, arterial) Communication technology (e.g., DSRC only, cellular only, DSRC+cellular) Communication frequency (e.g., every 10 th of a second, every 2 minutes)

8. 88 © 2015 Noblis, Inc. From Trajectories to Measures: An Overview Time Position Vehicle Trajectories from Microsimulation Tool Extract Sample Depending on Market Penetration 1 2 3 4 Emulated BSM (PDM) Process Messages To Estimate Measures TCA Travel Time Queue Length Other BSM (PDM)

9. 9 © 2015 Noblis, Inc. 9 Ground Truth versus 100% BSM/PDM 100% market penetration is NOT Ground Truth Ground Truth (VISSIM)BSMPDM All vehicles reportOnly connected vehicles report Reports are every 0.1 seconds Messages broadcast every 0.1 seconds ~1 snapshot every 4 to 20 seconds Vehicle IDs are persistentNo vehicle IDs Temporary vehicle IDs (PSN) timeout All data are retainedMessages are lost if not heardMessages in buffer are purged if transmission occurs Precise lane-specific trajectories Lane-specific precision unavailable

10. 10 © 2015 Noblis, Inc. Hypotheses and Assumptions Hypotheses  BSM and PDM, when used in combination will be more effective in estimating key measures than when used individually, when everything else is held a constant  As market penetration of connected vehicles increases, accuracy of measures estimation will increase, when everything else is held a constant  As the RSU density increases, accuracy of measures estimation will increase, when everything else is held a constant  As congestion or traffic demand increases, accuracy of measures estimation will increase, when everything else is held a constant  Accuracy of measures estimation will be higher with availability of both DSRC and cellular communication technologies than just DSRC or cellular technology, when everything else is held a constant Assumptions  Cost of RSU deployment and cellular coverage is not part of assessment

11. 11 © 2015 Noblis, Inc. Transportation Measures Examined  Prioritized List: 1.Queues at known bottlenecks 2.Cycle failures 3.Shockwaves 4.Queues at variable locations 5.Travel time 6.Delay 7.Speed 8.Throughput 9.Travel time reliability

12. 12 © 2015 Noblis, Inc. Measures Definitions and Algorithms

13. 13 © 2015 Noblis, Inc. Queues at Known Bottleneck Locations (#1): Definition  Vehicle is in queue when it is either [Source: TAT Vol. VI]: stopped OR traveling at a speed less than 10 fps (3 m/s) and is approaching another queued vehicle with gap of less than 20 ft (6 m)  By definition, first vehicle in a queue has to be stopped  #1: Queues at Known Bottleneck Locations (e.g., signalized intersections, stop and yield signs, ramp meters, bus stops, lane drops, work zones, etc.) Queue length: Measured in feet as the distance from the known bottleneck location (e.g., position of stop bar) to the rear bumper of the last vehicle in the queue Queue count: Number of vehicles in queue

14. 14 © 2015 Noblis, Inc. Queue at Known Bottleneck Locations on Arterials (#1): Measure Estimation For 100% market penetration of connected vehicles broadcasting BSMs (100% BSMs): Step 1: Sort BSMs every 10 th of a second based on distance from bottleneck Step 2: Check for start of queue (BSM speed = 0 fps, distance to bottleneck <= 20 feet) Step 3: Check if additional vehicles in queue (BSM speed <= 10 fps, gap to queued lead “BSM” <= 20 feet) Step 4: Calculate Queue Length = Distance of last BSM in queue from bottleneck + 20 feet Step 5: Calculate Queue Count = Queue Length /20 feet Step 6: Calculate Maximum Queue Length, Queue Count over 2 minutes

15. 15 © 2015 Noblis, Inc. Queue at Known Bottleneck Locations on Arterials (#1): Measure Estimation (cont.) For PDMs and less than 100% BSMs:  For PDMs: For each unique PSN, first interpolate between snapshots to get a snapshot every second  For PDMs and BSMs: Follow same process as 100% BSMs, with the following change: Speed of first vehicle in queue <= 5 fps (vs. 0 fps) Distance to stop bar and between vehicles is 100 ft (vs. 20 ft) Queue Length = 120+20 Queue Count = 7 (140/20) Legend 0 fps <= Speed <= 5 fps Legend Scale 20’ Speed > 10 fps 5 fps < Speed <= 10 fps Queue Length = 80’+20’ Queue Count = 5 (100/20) 80’ 0 0 3 3 4 4 5 5 9 9 Speed = 0 fps Speed > 10 fps 0 fps < Speed <= 10 fps 10 15 0 0 5 5 9 9 Example Illustration: 100% BSMs Example Illustration: PDMs or <100% BSMs 10 12 8 2013 9 12 2013 10 120’ 10

16. 16 © 2015 Noblis, Inc. Searches for up to 8 BSMs within a search window from start of route: 100 ft and 20 seconds if freeway 20 ft and 5 seconds if arterial Expand search window by (100 ft, 20s) OR (20 ft, 5s) if: 0 BSMs for freeway Less than 4 BSMs for arterial Travel Time (#5): Definition and Measure Estimation Using BSM Definition  This is defined as the average travel time on route segments experienced by all vehicles that begin travel in a specific time interval Measure Estimation Using BSM  Travel times calculated for hypothetical trips executed at 5-minute intervals Trips constructed using Nearest Neighbor search technique End of Rt. Start of Rt. Move hypothetical vehicle along route to a location that it can travel to in 4s at speed v h Distance traveled in 4 s at speed v h Repeat process until vehicle reaches end of route Travel Time = 4650-3150 seconds (25 min) Travel time = Time vehicle reaches end of route – Time vehicle started on route

17. 17 © 2015 Noblis, Inc. Travel Time (# 5): Measure Estimation Using PDM  Process uses similar Nearest Neighbor Search technique as BSM-Travel Time, but synthesizes missing portions of trip for each unique PSN End of Rt. Start of Rt. Unassociated PDMs PDMs from same PSN Distance traveled in 4 s Travel time = 4.6 min

18. 18 © 2015 Noblis, Inc. Scenarios

19. 19 © 2015 Noblis, Inc. Scenarios Examined ParametersRange Market Penetration of Connected Vehicles 2%, 20%, 50%, 100% Message Variants1. All connected vehicles transmit BSM via DSRC every 10th of a second 2. All connected vehicles transmit PDM via DSRC when 16 snapshots generated 3. All connected vehicles generate and transmit BSM via cellular every 2 min 4. All connected vehicles transmit PDM via cellular when 16 snapshots generated 5. All connected vehicles transmit BSM via DSRC every 10th of a second, and via cellular every 2 minutes when not in range of an RSU DSRC Communication Range 820 feet (~250 meters)

20. 20 © 2015 Noblis, Inc. Urban Arterial: Van Ness Blvd., CA Network: 1.75 miles (l) x.80 miles (w) 12 Signalized intersections Average block length: 430 feet Traffic Characteristics: Moderate to severe congestion at southern end of corridor Average Arterial Speed of 11.7 mph, max of 27.5 mph Simulation Model: Calibrated model in VISSIM (Source: PTV) for PM Peak Simulation time: 90 minutes N RSU Deployment

21. 21 © 2015 Noblis, Inc. Results

22. 22 © 2015 Noblis, Inc. Full Coverage Without Overlap of RSU Communication Ranges: Accuracy in Queue Estimation (#1)  Errors were high at lower market penetrations  Accuracies of more than 70% can be achieved even at 50% market penetration of connected vehicles for 4 of the 5 message protocols examined  BSM outperformed PDM in queue estimation, except when a BSM was transmitted every 2 minutes via cellular

23. 23 © 2015 Noblis, Inc.  Route travel time errors were less than 20% even at 2% market penetration, except when a BSM was transmitted every 2 minutes via cellular  PDM resulted in more accurate travel time estimates than BSM since PDM-based algorithm exploited association between snapshots  Neither DSRC nor cellular outperformed the other; benefits were due to the increased frequency of message transmission Full Coverage Without Overlap of RSU Communication Ranges: Accuracy in Travel Time Estimation (#5)

24. 24 © 2015 Noblis, Inc. Interim Key Findings Hypothesis: As market penetration of connected vehicles increases, accuracy of measures estimation will increase, when everything else is held a constant  Both queue and travel time estimation accuracies increased with increase in market penetration  Segment-specific queue estimation accuracies were lower than that for travel times  Travel time estimation accuracies of more than 90% were achieved even at lower market penetrations (20% and above) for 4 of the 5 message protocols examined Hypothesis: Accuracy of measures estimation will be higher with availability of both DSRC and cellular communication technologies than just DSRC or cellular technology, when everything else is held a constant  Message variant that transmitted BSM via both DSRC and cellular produced nearly the same results as the variant that transmitted BSM via DSRC – this may be attributed to the network type, where no portion of the roadway section was outside the range of an RSU even with limited RSU deployment

25. 25 © 2015 Noblis, Inc. Next Steps  Complete remaining scenarios to test the hypotheses  Assess capability of international messages (Japanese ITS Spot, European Union Cooperative Awareness Message) in estimating the key measures  Identify potential alternatives that combine the best ideas from all message protocols to inform an update of the PDM that generates accurate measures, limits redundant message generation and transmission, and mitigates re-identification risk  Study to be completed by Fall  Experimental plan, algorithms, code, and report will be made available via Open Source Application Development Portal (OSADP) at www.itsforge.netwww.itsforge.net