1. 1 Vehicular Sensor Networks for Traffic Monitoring In proceedings of 17th International Conference on Computer Communications and Networks (ICCCN 2008)

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

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

4. 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. 5 What we have… Data basis and features:  Long sampling interval due to communication cost  S parse and incomplete information  Error, etc.

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

7. 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. 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. 9 Outline Introduction Motivation and Problem Metric Definition Traffic Status Estimation Performance Evaluation Future Work and Conclusion

10. 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. 11 Definitions of mean traffic speed  freeway VS roads in urban area

12. 12 Whole time cost ∆ t to pass a link =traveling time ∆ t 1 + intersection delay ∆ t 2 For a given link L i with length l i, the mean traffic speed at time t k is defined as:

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

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

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

16. 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. 17 A vehicular mobile sensor system: Intelligent Traffic Information Service (ITIS)

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

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

20. 20 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. 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%

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

22. 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. 23 Outline Introduction Motivation and Problem Metric Definition Traffic Status Estimation Performance Evaluation Future Work and Conclusion

24. 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. 25 thanks!