IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i19p14080-d1245717.html
   My bibliography  Save this article

The Spatial Distribution of Taxi Stations in Bangkok

Author

Listed:
  • Suthikasem Weladee

    (Department of Urban and Regional Planning, Faculty of Architecture, Chulalongkorn University, Bangkok 10330, Thailand)

  • Peamsook Sanit

    (Department of Urban and Regional Planning, Faculty of Architecture, Chulalongkorn University, Bangkok 10330, Thailand)

Abstract

Taxis play a crucial role as an on-demand transportation mode in developing countries due to perceived inefficiencies of cities’ public transportation systems. However, studies on the locational distribution of taxis in urban areas are limited, despite the need to improve passenger service quality by balancing the demand and supply of taxi services. Notably, taxi stations possess distinct characteristics compared with other public transport stations that serve passengers directly; in contrast, taxi stations solely support taxi drivers in locations where they begin and conclude their work. This study aims to determine the spatial distribution pattern and assess the agglomeration economies of taxi stations, using Bangkok as a case study, a city with a significant number of registered taxis and dispersed taxi stations. This research takes into account various spatial variables, including land price, land use mix index, population density, and gas station locations. The primary tool for analyzing the spatial distribution pattern was the spatial statistics model, employing ArcGIS 10.8 software. The analysis consisted of three steps: testing for clustered or dispersed patterns using Moran’s I, applying Anselin’s local Moran’s I (LISA) to examine the relationship between taxi station coordinates and spatial variables such as land price, land use mix index, and population density, and evaluating the relationship between taxi stations and energy service stations using the network analysis tool. The results revealed that taxi stations exhibited a spatially clustered pattern and were closely correlated with the land use mix index, land price, and population density, as indicated by Moran’s index values of 0.425, 0.328, and 0.373, respectively, especially those located within a 3000 m radius of gas stations. These findings elucidate the location selection of taxi stations, which tend to prioritize areas that can generate maximum profits for the taxi business rather than those with minimal location costs. They also tend to be situated in proximity to market areas. Additionally, the study recommends that the government consider promoting electric taxis as a sustainable mode of urban transport in the future to reduce the usage of natural gas (NGV) and liquefied petroleum gas (LPG).

Suggested Citation

  • Suthikasem Weladee & Peamsook Sanit, 2023. "The Spatial Distribution of Taxi Stations in Bangkok," Sustainability, MDPI, vol. 15(19), pages 1-22, September.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14080-:d:1245717
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/19/14080/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/19/14080/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, Zhuo & Franz, Mark L. & Zhu, Shanjiang & Mahmoudi, Jina & Nasri, Arefeh & Zhang, Lei, 2018. "Analysis of Washington, DC taxi demand using GPS and land-use data," Journal of Transport Geography, Elsevier, vol. 66(C), pages 35-44.
    2. Camille Kamga & M. Anil Yazici & Abhishek Singhal, 2015. "Analysis of taxi demand and supply in New York City: implications of recent taxi regulations," Transportation Planning and Technology, Taylor & Francis Journals, vol. 38(6), pages 601-625, August.
    3. Zhang, Lei & Hong, Jin Hyun & Nasri, Arefeh & Shen, Qing, 2012. "How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 5(3), pages 40-52.
    4. Michael Ball & Arjang Assad & Lawrence Bodin & Bruce Golden & Frank Spielberg, 1984. "Garage Location for an Urban Mass Transit System," Transportation Science, INFORMS, vol. 18(1), pages 56-75, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiong, Ziyue & Jian Li, & Wu, Hangbin, 2021. "Understanding operation patterns of urban online ride-hailing services: A case study of Xiamen," Transport Policy, Elsevier, vol. 101(C), pages 100-118.
    2. Sun, Daniel(Jian) & Ding, Xueqing, 2019. "Spatiotemporal evolution of ridesourcing markets under the new restriction policy: A case study in Shanghai," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 227-239.
    3. Najaf, Pooya & Thill, Jean-Claude & Zhang, Wenjia & Fields, Milton Greg, 2018. "City-level urban form and traffic safety: A structural equation modeling analysis of direct and indirect effects," Journal of Transport Geography, Elsevier, vol. 69(C), pages 257-270.
    4. Li, Jingjing & Kim, Changjoo & Sang, Sunhee, 2018. "Exploring impacts of land use characteristics in residential neighborhood and activity space on non-work travel behaviors," Journal of Transport Geography, Elsevier, vol. 70(C), pages 141-147.
    5. Singh, Abhilash C. & Faghih Imani, Ahmadreza & Sivakumar, Aruna & Luna Xi, Yang & Miller, Eric J., 2024. "A joint analysis of accessibility and household trip frequencies by travel mode," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    6. Xiaoquan Wang & Chunfu Shao & Chaoying Yin & Chengxiang Zhuge & Wenjun Li, 2018. "Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun," Sustainability, MDPI, vol. 10(2), pages 1-15, February.
    7. Verma, Meghna & Rahul, T.M. & Reddy, Peesari Vamshidhar & Verma, Ashish, 2016. "The factors influencing bicycling in the Bangalore city," Transportation Research Part A: Policy and Practice, Elsevier, vol. 89(C), pages 29-40.
    8. Ali Enes Dingil & Federico Rupi & Domokos Esztergár-Kiss, 2021. "An Integrative Review of Socio-Technical Factors Influencing Travel Decision-Making and Urban Transport Performance," Sustainability, MDPI, vol. 13(18), pages 1-20, September.
    9. Bhat, Chandra R. & Astroza, Sebastian & Sidharthan, Raghuprasad & Alam, Mohammad Jobair Bin & Khushefati, Waleed H., 2014. "A joint count-continuous model of travel behavior with selection based on a multinomial probit residential density choice model," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 31-51.
    10. Yu, Haitao & Peng, Zhong-Ren, 2019. "Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression," Journal of Transport Geography, Elsevier, vol. 75(C), pages 147-163.
    11. Yujie Guo & Ying Chen & Yu Zhang, 2024. "Enhancing Demand Prediction: A Multi-Task Learning Approach for Taxis and TNCs," Sustainability, MDPI, vol. 16(5), pages 1-14, March.
    12. Rathore, Bhawana & Sengupta, Pooja & Biswas, Baidyanath & Kumar, Ajay, 2024. "Predicting the price of taxicabs using Artificial Intelligence: A hybrid approach based on clustering and ordinal regression models," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    13. Zhou, Xiaolu & Wang, Mingshu & Li, Dongying, 2019. "Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    14. Faizeh Hatami & Jean-Claude Thill, 2022. "Spatiotemporal Evaluation of the Built Environment’s Impact on Commuting Duration," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    15. Tian Li & Peng Jing & Linchao Li & Dazhi Sun & Wenbo Yan, 2019. "Revealing the Varying Impact of Urban Built Environment on Online Car-Hailing Travel in Spatio-Temporal Dimension: An Exploratory Analysis in Chengdu, China," Sustainability, MDPI, vol. 11(5), pages 1-17, March.
    16. Ying Ni & Jiaqi Chen, 2020. "Exploring the Effects of the Built Environment on Two Transfer Modes for Metros: Dockless Bike Sharing and Taxis," Sustainability, MDPI, vol. 12(5), pages 1-20, March.
    17. Arefeh Nasri & Lei Zhang, 2019. "How Urban Form Characteristics at Both Trip Ends Influence Mode Choice: Evidence from TOD vs. Non-TOD Zones of the Washington, D.C. Metropolitan Area," Sustainability, MDPI, vol. 11(12), pages 1-16, June.
    18. Yang, Xiping & Fang, Zhixiang & Xu, Yang & Yin, Ling & Li, Junyi & Lu, Shiwei, 2019. "Spatial heterogeneity in spatial interaction of human movements—Insights from large-scale mobile positioning data," Journal of Transport Geography, Elsevier, vol. 78(C), pages 29-40.
    19. Apantri Peungnumsai & Apichon Witayangkurn & Masahiko Nagai & Hiroyuki Miyazaki, 2018. "A Taxi Zoning Analysis Using Large-Scale Probe Data: A Case Study for Metropolitan Bangkok," The Review of Socionetwork Strategies, Springer, vol. 12(1), pages 21-45, June.
    20. Haitao Yu & Zhong-Ren Peng, 2020. "The impacts of built environment on ridesourcing demand: A neighbourhood level analysis in Austin, Texas," Urban Studies, Urban Studies Journal Limited, vol. 57(1), pages 152-175, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:19:p:14080-:d:1245717. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.