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Open AccessArticle

Spatiotemporal Analysis of Taxi-Driver Shifts Using Big Trace Data

State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China
Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China
School of Urban Design, Wuhan University, Wuhan 430070, China
Urban Informatics & Spatial Computing Lab, Department of Informatics, New Jersey Institute of Technology, Newark, NJ 07102, USA
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(4), 281;
Received: 21 February 2020 / Revised: 27 March 2020 / Accepted: 17 April 2020 / Published: 24 April 2020
In taxi management, taxi-driver shift behaviors play a key role in arranging the operation of taxis, which affect the balance between the demand and supply of taxis and the parking spaces. At the same time, these behaviors influence the daily travel of citizens. An analysis of the distribution of taxi-driver shifts, therefore, contributes to transportation management. Compared to the previous research using the real shift records, this study focuses on the spatiotemporal analysis of taxi-driver shifts using big trace data. A two-step strategy is proposed to automatically identify taxi-driver shifts from big trace data without the information of drivers’ identities. The first step is to pick out the frequent spatiotemporal sequential patterns from all parking events based on the spatiotemporal sequence analysis. The second step is to construct a Gaussian mixture model based on prior knowledge for further identifying taxi-driver shifts from all frequent spatiotemporal sequential patterns. The spatiotemporal distribution of taxi-driver shifts is analyzed based on two indicators, namely regional taxi coverage intensity and taxi density. Taking the city of Wuhan as an example, the experimental results show that the identification precision and recall rate of taxi-driver shift events based on the proposed method can achieve about 95% and 90%, respectively, by using big taxi trace data. The occurrence time of taxi-driver shifts in Wuhan mainly has two high peak periods: 1:00 a.m. to 4:00 a.m. and 4:00 p.m. to 5:00 p.m. Although taxi-driver shift behaviors are prohibited during the evening peak hour based on the regulation issued by Wuhan traffic administration, experimental results show that there are still some drivers in violation of this regulation. By analyzing the spatial distribution of taxi-driver shifts, we find that most taxi-driver shifts distribute in central urban areas such as Wuchang and Jianghan district. View Full-Text
Keywords: big trace data; taxi-driver shift; spatiotemporal analysis; transportation management big trace data; taxi-driver shift; spatiotemporal analysis; transportation management
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Cheng, L.; Yang, X.; Tang, L.; Duan, Q.; Kan, Z.; Zhang, X.; Ye, X. Spatiotemporal Analysis of Taxi-Driver Shifts Using Big Trace Data. ISPRS Int. J. Geo-Inf. 2020, 9, 281.

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