Detecting Taxi Trajectory Anomaly Based on Spatio-Temporal Relations

Autor: Minglu Li, Jian Cao, Guangtao Xue, Yanmin Zhu, Bin Cheng, Shiyou Qian, Jiadi Yu, Tao Zhang
Rok vydání: 2022
Předmět:
Zdroj: IEEE Transactions on Intelligent Transportation Systems. 23:6883-6894
ISSN: 1558-0016
1524-9050
DOI: 10.1109/tits.2021.3063199
Popis: Researchers have proposed many novel methods to detect abnormal taxi trajectories. However, most of the existing methods usually adopt a counting-based strategy, which may cause high false positives due to imprecisely identifying diverse trajectories as anomalies and therefore, they need the support of large-scale historical trajectories to work properly. To improve detection precision and efficiency, in this article, we propose STR, an online abnormal taxi trajectory detection method based on spatio-temporal relations. The basic principle behind STR is that given the displacement from the source point to a testing point, if the driving time and driving distance are not within the normal ranges, the point is identified as anomalous. To learn the two normal ranges for driving time and driving distance, STR defines two spatio-temporal models which characterize the relationship between displacement and driving distance/driving time. To improve detection efficiency, STR reduces the number of models that need to be learned by making full use of the similarity of transportation modes in different time periods and neighboring areas. The effectiveness and performance of STR are evaluated on real-world taxi trajectories. The experiment results show that compared with counting-based methods, STR achieves greater precision by reducing false positives. Furthermore, STR is more efficient than its counterparts and is suitable for online detection.
Databáze: OpenAIRE
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