How many probe vehicles are enough for identifying traffic congestion?—a study from a streaming data perspective

Abstract

Many studies have been carried out using vehicle trajectory to analyze traffic conditions, for instance, identifying traffic congestion. However, there is a lack of a systematic study on the appropriate number of probe vehicles and their sampling interval in order to identify traffic congestion accurately. Moreover, most of related studies ignore the streaming feature of trajectory data. This paper first represents a novel method of identifying traffic congestion considering the stream feature of vehicle trajectories. Instead of processing the whole data stream, a series of snapshots are extracted. Congested road segments can be identified by analyzing the clusters’ evolution among a series of adjacent snapshots. We then calculated a series of parameters and their corresponding congestion identification accuracy. The results have implications for related probe vehicle deployment and traffic analysis; for example, when 5% of probe vehicles are available, 85% identification accuracy can be reached if the sampling time interval is 10 s.

Keywords

vehicle trajectory data / floating car data / streaming data / traffic congestion

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 41171348, 40830530 and 60872132).

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg