1. 11October 19, 2011 Comparison of Queue Estimation Models at Traffic Signals Jingcheng Wu October 19, 2011 Presented at the 18th World Congress on ITS

2. 22October 19, 2011 Queueing at Traffic Signals Traffic flow on urban arterials is periodically interrupted by traffic signals. –Free flow –Decelerate to join the queue –Stop and wait in the queue –Accelerate to leave the queue –Free flow

3. 33October 19, 2011 How long is the queue? Queue Length, number of feet Queue Size, number of vehicles Conversion from one to the other requires assuming –Average physical vehicle length –Average space headway between vehicles

4. 44October 19, 2011 What are the benefits? Queue lengths in real-time operations –20 seconds vs. 15 minutes Traffic signal control –Adaptive traffic signal control Traffic management Traveler information Incident management

5. 55October 19, 2011 State of the Practice Physically directly measure queues through detection technologies Estimate queues through various models based on detector data

6. 66October 19, 2011 Measure Queues Video image detection –What you see is what you get. –High cost Install many detectors at close spacings –Higher cost –More does not mean better.

7. 77October 19, 2011 Queue Estimation Models Simple input-output model Kalman filter model Shock wave model Probabilistic model Other models

8. 88October 19, 2011 Simple Input-Output Model Difference between volumes entering and exiting Assuming –Vehicles do not change lanes. –First-in-first-out principle applies. Cannot handle long queues Accumulate errors over time

9. 99October 19, 2011 Kalman Filter Model Time update term and measurement update term Covert time occupancy to space occupancy Kalman filter gain parameter Simplified exponential smoothing model Volume balancing ratio

10. 10 October 19, 2011 Shock Wave Model

11. 11 October 19, 2011 An Example of Probabilistic Model

12. 12 October 19, 2011 Other Models Cell Transmission Model Use data collected by probe-based monitoring techniques to estimate queue length Implement fuzzy logic based models Base on a Gaussian process model

13. 13 October 19, 2011 Field Data New York City Department of Transportation urban arterial performance measurement, proof of concept Midblock RTMS volume and occupancy Stop bar Citilog Video Image Detector (VID) volume Queue length manually collected at stop bar

14. 14 October 19, 2011

15. 15 October 19, 2011 Kalman Filter Model

16. 16 October 19, 2011 Input-Output Model

17. 17 October 19, 2011 Conclusion Kalman filter tends to provide better results when volumes are unbalanced. The simple input-out model is easier to implement with a little sacrifice in accuracy. Very few studies targeting real-time queuing at traffic signals Very few queue models suitable for real- time operations Improved real-time queue models are needed.