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Open AccessArticle

Anomalous Urban Mobility Pattern Detection Based on GPS Trajectories and POI Data

by 1, 2,*, 3 and 1,2
1
School of Information and Technology, Northwest University, Xi’an 710069, China
2
Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
3
Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(7), 308; https://doi.org/10.3390/ijgi8070308
Received: 30 May 2019 / Revised: 4 July 2019 / Accepted: 12 July 2019 / Published: 17 July 2019
Anomalous urban mobility pattern refers to abnormal human mobility flow in a city. Anomalous urban mobility pattern detection is important in the study of urban mobility. In this paper, a framework is proposed to identify anomalous urban mobility patterns based on taxi GPS trajectories and Point of Interest (POI) data. In the framework, functional regions are first generated based on the distribution of POIs by the DBSCAN clustering algorithm. A Weighted Term Frequency-Inverse Document Frequency (WTF-IDF) method is proposed to identify function values in each region. Then, the Origin-Destination (OD) of trips between functional regions is extracted from GPS trajectories to detect anomalous urban mobility patterns. Mobility vectors are established for each time interval based on the OD of trips and are classified into clusters by the mean shift algorithm. Abnormal urban mobility patterns are identified by processing the mobility vectors. A case study in the city of Wuhan, China, is conducted; the experimental results show that the proposed method can effectively identify daily and hourly anomalous urban mobility patterns. View Full-Text
Keywords: anomalous mobility pattern detection; functional region identification; GPS trajectories; POI data anomalous mobility pattern detection; functional region identification; GPS trajectories; POI data
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Xu, Z.; Cui, G.; Zhong, M.; Wang, X. Anomalous Urban Mobility Pattern Detection Based on GPS Trajectories and POI Data. ISPRS Int. J. Geo-Inf. 2019, 8, 308.

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ISPRS Int. J. Geo-Inf., EISSN 2220-9964, Published by MDPI AG
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