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

Author:

Liu Weiwei1ORCID,Zhang Chennan1ORCID,Zhang Jin1,Sharma Pradip Kumar2,Alfarraj Osama3ORCID,Tolba Amr3ORCID,Wang Qian4,Tang Yang5

Affiliation:

1. Business School, University of Shanghai for Science and Technology, 516 Jungong Road, Shanghai 200093, China

2. Department of Computing Science, University of Aberdeen, Aberdeen AB24 3FX, UK

3. Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia

4. Intelligent Transportation Products Department, China Mobile Shanghai Information Communication Technology Corporation, 735 Jingang Road, Shanghai 201206, China

5. Urban and Rural Planning & Design Institute, Zhejiang University, Hangzhou 310023, China

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.

Funder

National Natural Science Foundation of China

King Saud University

Publisher

MDPI AG

Subject

Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction

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Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Dynamic Graph Convolutional Network-Based Prediction of the Urban Grid-Level Taxi Demand–Supply Imbalance Using GPS Trajectories;ISPRS International Journal of Geo-Information;2024-01-24

2. Taxi Station Location Model Based on Spatio-Temporal Demand Cube;2023 International Conference on Networking and Network Applications (NaNA);2023-08

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