Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data
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 tr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-01-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | http://www.mdpi.com/2220-9964/7/1/25 |
| _version_ | 1828365308116598784 |
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| author | Yulong Wang Kun Qin Yixiang Chen Pengxiang Zhao |
| author_facet | Yulong Wang Kun Qin Yixiang Chen Pengxiang Zhao |
| author_sort | Yulong Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T05:29:18Z |
| format | Article |
| id | doaj.art-8a12c79a11d04be797e9b45ea59ef3cc |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-04-14T05:29:18Z |
| publishDate | 2018-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-8a12c79a11d04be797e9b45ea59ef3cc2022-12-22T02:09:52ZengMDPI AGISPRS International Journal of Geo-Information2220-99642018-01-01712510.3390/ijgi7010025ijgi7010025Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS DataYulong Wang0Kun Qin1Yixiang Chen2Pengxiang Zhao3School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaDepartment of Surveying and Geoinformatics, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, ChinaAnomalous 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.http://www.mdpi.com/2220-9964/7/1/25trajectory clusteringtrajectory anomaliesedit distancehierarchical clusteringanomalous behavior pattern |
| spellingShingle | Yulong Wang Kun Qin Yixiang Chen Pengxiang Zhao Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data ISPRS International Journal of Geo-Information trajectory clustering trajectory anomalies edit distance hierarchical clustering anomalous behavior pattern |
| title | Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data |
| title_full | Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data |
| title_fullStr | Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data |
| title_full_unstemmed | Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data |
| title_short | Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data |
| title_sort | detecting anomalous trajectories and behavior patterns using hierarchical clustering from taxi gps data |
| topic | trajectory clustering trajectory anomalies edit distance hierarchical clustering anomalous behavior pattern |
| url | http://www.mdpi.com/2220-9964/7/1/25 |
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