1. Real-time Microscopic Estimation of Freeway Vehicle Positions from the Behaviors of Probe Vehicles
Noah J. Goodall, P.E.
Research Scientist, Virginia Center for Transportation Innovation and Research
Ph.D. Candidate, University of Virginia
April 20, 2012
10/27/2019

2. Smartphones Very sophisticated computer Sensors
GPS
3-axis accelerometer
Camera
Magnetometer
Carried with you all day

3. Modern Vehicles – Very Sophisticated
How many lines of code in a:
F-22 Raptor:
Average new Ford:
1.7 million
10 million

4. 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

5. Vehicle-to-Vehicle Communication: Not Sophisticated
Hi-tech vehicles
Low-tech communication with other vehicles
Brake lights
Turn signals
Horn
Flash headlights

6. Vehicle-to-Infrastructure Communication: Not Much Better
We want to know where vehicles are, what they’re doing
Lot’s of sensors already in the field to detect this

7. Field Sensor Shortcomings
Limited 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

8. Solution: Connected Vehicles

9. Wireless Vehicle Communication
Significant movement towards wireless communication between vehicles and infrastructure

10. Connected Vehicle Applications
Lots of connected vehicle mobility applications in development
Most of these applications need at least 20-30% of vehicles to be “connected” to see benefits
Application
% Connected Vehicles Needed for Benefits
Traffic signal control
20-30%
Freeway incident detection
20%
Lane-level speed estimation
Arterial performance measurement
10-50%

11. 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,
October 2009.

12. Background Rollout of connected vehicles will not be instantaneous
16 years between kickoff and 80% penetration
Projected rollout of on-board equipment in US Fleet (Volpe, 2008)

13. Assumed Location of Unequipped Vehicle
What it Means
Problem – Not able to get the full benefit of connected vehicle applications at low market penetrations.
Solution – “Location Estimation”
Behavior of equipped vehicles may suggest location of unequipped vehicles.
Assumed Location of Unequipped Vehicle
Equipped Vehicles

14. Methodology How to estimate vehicle locations
Depends on unexpected behavior of equipped vehicles – indicates an unequipped vehicle ahead
What is “unexpected”?
Car-following model

15. 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/s2 actual
Estimate properties from model or history
Wiedemann is widely accepted
3 ft/s2 expected
30 mph
45 mph
Headway = 97 feet
Speed = 29 mph

16. 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/s2 actual
Estimate properties from model or history
Wiedemann is widely accepted
3 ft/s2 expected
30 mph
45 mph
Vehicle continues to drive
according to model, until overtaken
10/27/2019

17. Testing Using NGSIM datasets as ground truth
Two short freeway segments
Artificial VISSIM model to supplement
One direction, 4 lanes, 7 miles, 2 interchanges

18. Results
Equipped
Observed
Predicted

19. I-80 at 25% Market Penetration
Ground Truth Densities (Equipped and Observed Vehicles)
Distance
(1/8 mile)
Estimated Densities (Equipped and Predicted Vehicles)
Distance
(1/8 mile)
Time (s)

20. Artificial Network at 25% Market Penetration
Ground Truth Densities (Equipped and Observed Vehicles)
Distance
(7 miles)
Estimated Densities (Equipped and Predicted Vehicles)
Distance
(7 miles)
Time (s)

21. Performance Effective Market Penetration =
Accurate Predictions – False Predictions + Equipped Vehicles
Total Actual Vehicle-Seconds
The blue line comes in early.

23. Conclusions
The algorithm can predict the locations of many unequipped vehicles at various levels of accuracy, particularly during and after congestion

24. Next Steps Apply on surface streets
Vehicles decelerate for many reasons
signals, pedestrians, stop signs, turning, etc.
Lots of congestion
Almost every vehicle queues at some point

25. Special Thanks Advisors
Brian L. Smith, P.E., Ph.D., University of Virginia
Byungkyu (Brian) Park, Ph.D., University of Virginia

26. For more information: Noah Goodall noah.goodall@vdot.virginia.gov