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LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data

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As the development of crowdsourcing technique, acquiring amounts of data in urban cities becomes possible and reliable, which makes it possible to mine useful and significant information from data. Traffic anomaly detection is to find the traffic patterns which are not expected and it can be used to explore traffic problems accurately and efficiently. In this paper, we propose LoTAD to explore anomalous regions with long-term poor traffic situations. Specifically, we process crowdsourced bus data into TS-segments (Temporal and Spatial segments) to model the traffic condition. Later, we explore anomalous TS-segments in each bus line by calculating their AI (Anomaly Index). Then, we combine anomalous TS-segments detected in different lines to mine anomalous regions. The information of anomalous regions provides suggestions for future traffic planning. We conduct experiments with real crowdsourced bus trajectory datasets of October in 2014 and March in 2015 in Hangzhou. We analyze the varieties of the results and explain how they are consistent with the real urban traffic planning or social events happened between the time interval of the two datasets. At last we do a contrast experiment with the most ten congested roads in Hangzhou, which verifies the effectiveness of LoTAD.

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The authors extend their appreciation to the International Scientific Partnership Program ISPP at King Saud University for funding this research work through ISPP#0078. This work was partially supported by the National Natural Science Foundation of China under Grants no. 61572106, the Natural Science Foundation of Liaoning Province, China under Grants no. 201602154, and the Dalian Science and Technology Planning Project under Grant no. 2015A11GX015 and 2015R054.

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Correspondence to Feng Xia.

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This article belongs to the Topical Collection: Special Issue on Mobile Crowdsourcing

Guest Editors: Bin Guo, Xing Xie, Raghu K. Ganti, Daqing Zhang, and Zhu Wang

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Kong, X., Song, X., Xia, F. et al. LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data. World Wide Web 21, 825–847 (2018).

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  • Traffic anomaly detection
  • Mobile crowdsourcing
  • Urban big data
  • Anomaly index