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http://hdl.handle.net/10397/77583
Title: | Detecting anomalous trajectories and behavior patterns using hierarchical clustering from Taxi GPS Data | Authors: | Wang, Y Qin, K Chen, Y Zhao, P |
Issue Date: | 2018 | Source: | ISPRS international journal of geo-information, Jan. 2018, v. 7, no. 1, 25, p. 1-20 | Abstract: | 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. | Keywords: | Anomalous behavior pattern Edit distance Hierarchical clustering Trajectory anomalies Trajectory clustering |
Publisher: | Molecular Diversity Preservation International (MDPI) | Journal: | ISPRS international journal of geo-information | EISSN: | 2220-9964 | DOI: | 10.3390/ijgi7010025 | Rights: | © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). The following publication Wang, Y., Qin, K., Chen, Y., & Zhao, P. (2018). Detecting anomalous trajectories and behavior patterns using hierarchical clustering from Taxi GPS Data. Isprs International Journal of Geo-Information, 7(1), (Suppl. ), 25, - is available athttps://dx.doi.org/10.3390/ijgi7010025 |
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