Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data
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School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
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Collaborative Innovation Center for Geospatial Technology, Wuhan University, Wuhan 430079, China
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Department of Surveying and Geoinformatics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(1), 25; https://doi.org/10.3390/ijgi7010025
Received: 3 November 2017
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Revised: 7 January 2018
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Accepted: 11 January 2018
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Published: 12 January 2018
(This article belongs to the Special Issue Discovery and Prediction of Moving Objects in Databases using GIS-based Tools)
Anomalous taxi trajectories are those chosen by a small number of drivers that are different from the regular choices of other drivers. These anomalous driving trajectories provide us an opportunity to extract driver or passenger behaviors and monitor adverse urban traffic events. Because various trajectory clustering methods have previously proven to be an effective means to analyze similarities and anomalies within taxi GPS trajectory data, we focus on the problem of detecting anomalous taxi trajectories, and we develop our trajectory clustering method based on the edit distance and hierarchical clustering. To achieve this objective, first, we obtain all the taxi trajectories crossing the same source–destination pairs from taxi trajectories and take these trajectories as clustering objects. Second, an edit distance algorithm is modified to measure the similarity of the trajectories. Then, we distinguish regular trajectories and anomalous trajectories by applying adaptive hierarchical clustering based on an optimal number of clusters. Moreover, we further analyze these anomalous trajectories and discover four anomalous behavior patterns to speculate on the cause of an anomaly based on statistical indicators of time and length. The experimental results show that the proposed method can effectively detect anomalous trajectories and can be used to infer clearly fraudulent driving routes and the occurrence of adverse traffic events.
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Keywords:
trajectory clustering; trajectory anomalies; edit distance; hierarchical clustering; anomalous behavior pattern
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MDPI and ACS Style
Wang, Y.; Qin, K.; Chen, Y.; Zhao, P. Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data. ISPRS Int. J. Geo-Inf. 2018, 7, 25. https://doi.org/10.3390/ijgi7010025
AMA Style
Wang Y, Qin K, Chen Y, Zhao P. Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data. ISPRS International Journal of Geo-Information. 2018; 7(1):25. https://doi.org/10.3390/ijgi7010025
Chicago/Turabian StyleWang, Yulong, Kun Qin, Yixiang Chen, and Pengxiang Zhao. 2018. "Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data" ISPRS International Journal of Geo-Information 7, no. 1: 25. https://doi.org/10.3390/ijgi7010025
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