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原文传递 Modeling and Analysis of Daily Driving Patterns of Taxis in Reshuffled Ride-Hailing Service Market

题名：	Modeling and Analysis of Daily Driving Patterns of Taxis in Reshuffled Ride-Hailing Service Market
正文语种：	英文
作者：	Qingyu Ma1; Hong Yang, Ph.D.2; Hua Zhang, Ph.D.3; Kun Xie, Ph.D.4; Zhenyu Wang5
作者单位：	1Graduate Research Assistant, Dept. of Computational Modeling and Simulation Engineering, Old Dominion Univ., Norfolk, VA 23529. 2Assistant Professor, Dept. of Computational Modeling and Simulation Engineering, Old Dominion Univ., Norfolk, VA 23529. 3Assistant Professor, National Maglev Transportation Engineering R&D Center, Tongji Univ., Shanghai 201804, China; Associate Research Scientist, Dept. of Civil Engineering and Engineering Mechanics, Columbia Univ., New York, NY 10027 (corresponding author). 4Assistant Professor, Dept. of Civil and Environmental Engineering, Old Dominion Univ., 135 Kaufman Hall, Norfolk, VA 23529. 5Graduate Research Assistant, Dept. of Computational Modeling and Simulation Engineering, Old Dominion Univ., Norfolk, VA 23529.
关键词：	Taxi; Ride-sourcing services; E-hailing application; Driving patterns; Trajectory data; Clustering
摘要：	This paper aims to model and analyze the changes in daily driving patterns of taxis in a disrupted market due to the boom in e-hailing services. This is accomplished by mining large-scale trajectory data sets obtained from a major taxi company in Shanghai. The taxi data set includes more than 0.8 billion trajectory points associated with over 12,000 taxis obtained in a period of 10 days (5 continuous weekdays in 2012 and 2016, respectively). The raw data were efficiently processed with the acceleration of high-performance computing. Creatively, the concept of information entropy together with principal component analysis were adopted to spatially delineate the gridded daily taxi driving trajectories. This helps describe the disordered taxi traces in comparable profiles across different spatial zones. Then, distinct patterns were extracted using the k-means clustering method. The proposed analysis pipeline has built a stable way of comparing driving patterns between different time periods after relaxing concerns about potential spreading of demand over time. By comparing statistical features associated with the identified clusters, the changes in daily taxi driving patterns in the context of the wide popularization of e-hailing services were quantitatively unveiled. This will be informative for taxi service providers revamping their business models when facing the opportunities brought by e-hailing apps and competition from other ride-sourcing vehicles in urban areas.
出版年：	2019
期刊名称：	Journal of Transportation Engineering
卷：	145
期：	10
页码：	1-19