Rational Layout of Taxi Stop Based on the Analysis of Spatial Trajectory Data

Weiwei Liu (), Chennan Zhang, Jin Zhang, Pradip Kumar Sharma, Osama Alfarraj, Amr Tolba, Qian Wang and Yang Tang
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Weiwei Liu: Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China
Chennan Zhang: Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China
Jin Zhang: Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China
Pradip Kumar Sharma: Department of Computing Science, University of Aberdeen, Aberdeen AB24 3FX, UK
Osama Alfarraj: Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia
Amr Tolba: Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia
Qian Wang: Intelligent Transportation Products Department, China Mobile Shanghai Information Communication Technology Corporation, 735 Jingang Road, Shanghai 201206, China
Yang Tang: Urban and Rural Planning & Design Institute, Zhejiang University, Hangzhou 310023, China

Sustainability, 2023, vol. 15, issue 4, 1-14

Abstract: The implementation of the relevant management system makes the road-parking behavior standardized, while increasing the difficulty of temporary parking of operational vehicles such as taxis. Therefore, in order to improve the relevant management measures and promote the sustainable development of the taxi industry, it is necessary to survey the demand for taxi parking and study the layout of taxi stops. To process the GPS data of the taxis, and to extract the loading and unloading positions of the passengers from the spatial trajectory data, big data analysis technology is used. Compared with the data obtained using traditional survey means, the spatial trajectory data reflects the situation of the whole system, which can make the analysis more accurate. K-means cluster analysis was used to determine community demand. Finally, the immune optimization model was used to determine the optimal taxi stand location. The problem of taxi stand location at the level of urban network from two dimensions of quantity and spatial distribution is solved in this paper. The location of 10 taxi stands can not only meet the parking needs of regional taxis, but also reasonably allocate urban resources and promote sustainable development. This study also has a certain reference value for relevant management departments.

Keywords: spatial trajectory data; K-means cluster analysis; immune optimization model; rational layout of taxi stop (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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