Microscopic Estimation of Freeway Vehicle Positions Using Mobile - PowerPoint PPT Presentation

Microscopic Estimation of Freeway Vehicle Positions Using Mobile Sensors Noah J. Goodall, P.E. Research Scientist, Virginia Center for Transportation Innovation and Research Ph.D. Candidate, University of Virginia Brian L. Smith, P.E., Ph.D., University of Virginia Byungkyu (Brian) Park, Ph.D., University of Virginia January 20, 2011 9/29/2020 1

Computerized Measurement • Speed • Heading • Acceleration (lateral, longitudinal, vertical) • Position (from GPS) • Other diagnostics – Wipers on/off – Headlights on/off – Braking status – Turn signals on/off – Tire pressure – Rain sensors – Steering wheel angle – Stability control 9/29/2020 2

Vehicle-to-Vehicle Communication: Not Sophisticated • Hi-tech vehicles • Low-tech communication with other vehicles – Brake lights – Turn signals – Horn 9/29/2020 3

Vehicle-to-Infrastructure Communication: Not Much Better • Important to know where vehicles are and what they’re doing • Lot’s of sensors already in the field to detect this 9/29/2020 4

Field Detection 10/20/2011 5

Field Sensor Shortcomings • Poor data quality • Point detection, not continuous coverage • Difficult/expensive to repair = frequent downtime • Limited types of data – Aggregated speed, density, and volumes at a single point 9/29/2020 6

Solution: Connected Vehicles 10/20/2011 7

Wireless Vehicle Communication • Significant movement towards wireless communication between vehicles and infrastructure 9/29/2020 8

Connected Vehicle Applications • Lots of connected vehicle mobility applications in development % Connected Vehicles Application Needed for Benefits Traffic signal control 20-30% Incident detection 20% 2% (supplemented by loop Freeway monitoring detectors) • Most of these applications need at least 25% of vehicles to be “connected” to see benefits • These use data from individual vehicles, NOT aggregated data like speed/density/flow 9/29/2020 9

Better Performance with Higher Market Penetration Premier and Friedrich, “A Decentralized Adaptive Traffic Signal Control Using V2I Communication Data ,” Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems , 9/29/2020 10 October 2009.

Background • Rollout of connected vehicles will not be instantaneous 16 years between kickoff and 80% 9/29/2020 11 Projected rollout of on-board equipment in US Fleet (Volpe, 2008)

What it Means • Problem – Mobile sensors and connected vehicle data are not constant or ubiquitous. Leads to poor performance of connected vehicle applications. • Solution – “Location Estimation” – Behavior of equipped vehicles may suggest location of unequipped vehicles. – Can artificially augment real penetration rates. Assumed Location of Equipped Vehicles Unequipped Vehicle 9/29/2020 12

Methodology • How to estimate vehicle locations – Depends on unexpected behavior of equipped vehicles – indicates an unequipped vehicle ahead – What is “unexpected”? – Car-following model 9/29/2020 13

Algorithm • Vehicles assumed to follow Wiedemann car- following model – Widely accepted, basis for VISSIM • A deviation from expected acceleration indicates an unequipped vehicle ahead -4 ft/s 2 actual Estimate 3 ft/s 2 expected properties from model or history 45 mph 30 mph Vehicle continues to drive 9/29/2020 14 according to model, until overtaken

Algorithm Details • Acceleration threshold: 0.2g less than expected • Estimate of lead vehicle’s speed obtained from empirical observation 𝑤 𝑜−1 = 𝑤 𝑜 + .162𝑏 𝑜 – 𝑤 𝑜−1 = speed of estimated leading vehicle (m/s) – 𝑤 𝑜 = speed of equipped trailing vehicle (m/s) – 𝑏 𝑜 = acceleration of equipped trailing vehicle (m/s 2 ) 15

Algorithm Details • If equipped, trailing vehicle is accelerating – Assume trailing vehicle is in “following” regime • If equipped, trailing vehicle is decelerating – Assume trailing vehicle is in “closing” regime 9/29/2020 16

Testing • Using NGSIM datasets as ground truth – Two freeway segments – One arterial • Calibrated VISSIM model to supplement – Rt 50 in Chantilly 17

Results Sampled Observed Predicted 9/29/2020 18

Densities Along I-80 at 25% Market Penetration Actual Densities (Sampled and Observed Vehicles) 0 Distance (1/4 mile total) 2 4 6 8 10 12 0 200 400 600 800 1000 1200 1400 1600 1800 Estimated Densities (Sampled and Predicted Vehicles) 0 Distance (1/4 mile total) 2 4 6 8 10 12 0 200 400 600 800 1000 1200 1400 1600 1800 9/29/2020 Time (s) 19

Absolute Difference between Observed and Predicted Densities Along I-80 at 25% Market Penetration Absolute Difference in Densities (Scaled to Enhance Differences) 0 2 Distance (1/4 mile total) 4 6 8 10 12 0 200 400 600 800 1000 1200 1400 1600 1800 Time (s) Estimates improve downstream, as the model populates itself 9/29/2020 20

Metrics • Not a one-to-one correlation between estimates and observed • Need to determine which estimate belongs to which observation 9/29/2020 21

My Approach For all vehicles on a single lane at a single second, calculate distances Estimated Vehicles Errors Distances A3/E5: 1 meter E1 E2 E3 E4 E5 E6 A1/E6: 2 meters A1 67 46 93 11 23 2 Observed A2/E3: 6 meters Vehicles A2 20 41 6 76 64 89 A4/E2: 16 meters E1: infinite A3 45 24 71 11 1 24 E4: infinite A4 37 16 63 19 7 32 Effective Market Penetration = Accurate Estimates – False Estimates + Sampled (Known) Vehicles Total Actual Vehicle-Seconds 9/29/2020 22

Original 100% Effective Market Penetration, I-80 Market Penetration 0 80% 5 Effective Market Penetration, 10 Improvement (%) 60% 15 20 25 40% 30 40 50 20% 60 70 0% 80 90 100 -20% 0 5 10 15 20 25 30 9/29/2020 Desired Accuracy of Estimate (m) 23

50 Improvement in Effective Market Penetration, I-80 Market Penetration 40 0 5 30 Percentage Point Improvement (%) Effective Market Penetration, 10 20 15 20 10 25 30 0 40 50 -10 60 -20 70 80 -30 90 100 -40 0 5 10 15 20 25 30 9/29/2020 Desired Accuracy of Estimate (m) 24

Challenges • Not all estimations are of the same quality – More confidence in a gap in a queue than unexpected behavior in free flow traffic • Arterials provide another challenge - vehicle not always reacting to another vehicle – Driveways – Turning movements – Pedestrians 25

Conclusions • The algorithm can predict the locations of some unequipped vehicles at various levels of accuracy, especially during and after congestion • Reliance on a car-following model limits the algorithm to freeways • More sophisticated techniques needed for surface streets 9/29/2020 26

For more information: Noah Goodall noah.goodall@vdot.virginia.gov 9/29/2020 27

Preliminary Results: Predicting Locations with 25% Market Penetration (a) (b) (c) Distance (ft) Distance (ft) Distance (ft) 8:05 8:20 8:35 8:05 8:20 8:35 8:05 8:20 8:35 (d) (e) 20 Distance (ft) Distance (ft) 15 10 5 8:05 8:20 8:35 8:05 8:20 8:35 Number of vehicles in each of 120-foot long cells during each second of the NGSIM data set for (a) ground truth, (b) mobile sensors only averaged over twenty repetitions, (c) mobile sensors only for a single repetition, (d), detector- supplemented averaged over twenty repetitions, and (e) detector-supplemented for a single repetition. In each scenario, 25% of vehicles were able to transmit their locations and speeds once per second. 9/29/2020 28