1 Vehicular Sensor Networks for Traffic Monitoring 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  S parse 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 ∆ 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 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(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 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 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. 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 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

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