Efficient Traffic Monitoring Using Vehicular Sensor Networks

1. Vehicular Sensor Networks for Traffic Monitoring Xu Li, Minglu Li and Min-You Wu Shanghai Jiao Tong University, China In proceedings of 17th International Conference on Computer Communications and Networks (ICCCN 2008)

2. Outline • Introduction • Motivation and Problem • Metric Definition • Traffic Status Estimation • Performance Evaluation • Future Work and Conclusion

3. Introduction • Traffic monitoring in city urban area • Traditional approach: loop detector, camera,etc • infrastructure cost • maintenance cost • communication cost • not scalable

4. Another way? The existing vehicular sensor networks of taxi companies • vehicle dispatching • security purposes • not special for traffic monitoring Whether it can be used for traffic monitoring? If “yes”, Advantage: • Low infrastructure cost • Low maintenance cost • Cover the entire road network, scalable

5. What we have… • Data basis and features: • Long sampling interval due to communication cost • Sparse and incomplete information • Error, etc.

6. Outline • Introduction • Motivation and Problem • Metric Definition • Traffic Status Estimation • Performance Evaluation • Future Work and Conclusion

7. Motivation • What sort of performance for traffic monitoring we might expect from such vehicular sensor networks providing sparse and incomplete information Now in Shanghai, we utilize a test bed with mobile sensors installed in about 4000 taxis

8. Problem • Whether we can demonstrate the feasibility of taxi-based sensor networks for traffic monitoring? • Whether the tradeoff between the accuracy of traffic status estimation and low communication cost can be well handled?

9. Outline • Introduction • Motivation and Problem • Metric Definition • Traffic Status Estimation • Performance Evaluation • Future Work and Conclusion

10. Metric definition • Three key characteristics in macroscopic traffic-flow model: • flow rate • mean traffic speed • density • Public tends to consider more in terms of mean speed rather than flow rate or density in evaluating the quality of their trips

11. Definitions of mean traffic speed • freeway VS roads in urban area

12. Whole time cost ∆t to pass a link =traveling time ∆t1+ intersection delay ∆t2 • For a given link Liwith length li, the mean traffic speed at time tk is defined as:

13. Outline • Introduction • Motivation and Problem • Metric Definition • Traffic Status Estimation • Performance Evaluation • Future Work and Conclusion

14. A sample data from a sensor is defined by a 4-tuple D(SID, T, , ), and two consecutive data samples can construct a data pair. A data pair from sensor s can be defined as: p(s, t1, t2) = {s, t1, 1, t2, 2} 1 and 2 are the geographic coordinates from the consecutive data samples at t1 and t2, respectively

15. The link-based algorithm (LBA) • LBA only aggregates data pairs of sensing data from link Li as well as links adjacent to either of intersection nodes of Li.

16. The vehicle-based algorithm (VBA) • VBA utilizes every available data pairs and disseminates them back to all links traveled to estimate mean traffic speed.

17. A vehicular mobile sensor system: Intelligent Traffic Information Service (ITIS)

18. Outline • Introduction • Motivation and Problem • Metric Definition • Traffic Status Estimation • Performance Evaluation • Future Work and Conclusion

19. Performance Evaluation • Large-scale field testing on arterial and inferior roads

20. The testing results showed VBA-based is better than LBA-based algorithms due to the data feature. More specially, the average error of VBA-Avg can be within only 17.3% The testing results showed VBA-based is better than LBA-based algorithms. More specially, the average error of VBA-Avg can be within only 17.3%, which demonstrates the feasibility of such application in most of cities and the tradeoff between the accuracy of traffic status estimation and low communication cost .

21. Lessons Learned • Map-matching Poor map-matching performance degrades the accuracy of traffic status estimation

22. Traffic light The mean speed of whole trip of 56 km is 21.1 km/h. • traffic light delays: 82 minutes • total time cost: 159 minutes

23. Outline • Introduction • Motivation and Problem • Metric Definition • Traffic Status Estimation • Performance Evaluation • Future Work and Conclusion

24. Conclusion • A performance evaluation study has been carried out in Shanghai by utilizing the sensors installed on 4000 taxis for traffic monitoring • Two types of traffic status estimation algorithms, the link-based and the vehicle-based, are introduced based on such data basis. • The results from large-scale testing cases demonstrate the feasibility of such an application in most of cities

25. thanks!