IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i17p7386-d1465353.html
   My bibliography  Save this article

Analysis of Taxi Demand and Traffic Influencing Factors in Urban Core Area Based on Data Field Theory and GWR Model: A Case Study of Beijing

Author

Listed:
  • Man Zhang

    (School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing 100044, China)

  • Dongwei Tian

    (School of Architecture and Urban Planning, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
    Beijing Jiaotong University, Beijing 100044, China)

  • Jingming Liu

    (Development Strategy Institute for Building Materials Industry, Beijing 100035, China)

  • Xuehua Li

    (College of Applied Arts and Science, Beijing Union University, Beijing 100088, China)

Abstract

Urban transportation constitutes a complex and dynamic system influenced by various factors, including population density, infrastructure, economic activities, and individual travel behavior. Taxis, as a widespread mode of transportation in many cities, play a crucial role in meeting the transportation needs of urban residents. By using data field theory and the Geographically Weighted Regression (GWR) modeling method, this study explored the complex relationship between taxi demand and traffic-related factors in urban core areas and revealed the potential factors affecting taxi starting and landing points. This research reveals that during the morning peak hours (7:00–9:00), at locations such as long-distance bus terminals, bus stations, parking areas, train stations, and bike-sharing points, taxi demand significantly increases, particularly in the central and southeastern regions of the urban core. Conversely, demand is lower in high-density intersection areas. Additionally, proximity to train stations is positively correlated with higher taxi demand, likely related to the needs of long-distance travelers. During the evening peak hours (17:00–19:00), the taxi demand pattern resembles that of the morning peak, with long-distance bus terminals, bus stations, and parking and bike sharing areas remaining key areas of demand. Notably, parking areas frequently serve as pick-up points for passengers during this time, possibly associated with evening activities and entertainment. Moreover, taxi demand remains high around train stations. In summary, this study enhances our understanding of the dynamics of urban taxi demand and its relationship with various transportation-related influencing factors within the core urban areas. The proposed grid partitioning and GWR modeling methods provide valuable insights for urban transportation planners, taxi service providers, and policymakers, facilitating service optimization and improved urban mobility.

Suggested Citation

  • Man Zhang & Dongwei Tian & Jingming Liu & Xuehua Li, 2024. "Analysis of Taxi Demand and Traffic Influencing Factors in Urban Core Area Based on Data Field Theory and GWR Model: A Case Study of Beijing," Sustainability, MDPI, vol. 16(17), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7386-:d:1465353
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/17/7386/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/17/7386/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Albalate, Daniel & Bel, Germà, 2010. "Tourism and urban public transport: Holding demand pressure under supply constraints," Tourism Management, Elsevier, vol. 31(3), pages 425-433.
    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. Wong, R.C.P. & Szeto, W.Y. & Wong, S.C., 2014. "Bi-level decisions of vacant taxi drivers traveling towards taxi stands in customer-search: Modeling methodology and policy implications," Transport Policy, Elsevier, vol. 33(C), pages 73-81.
    4. Chen, Ting-Yu & Chang, Hsin-Li & Tzeng, Gwo-Hshiung, 2002. "Using fuzzy measures and habitual domains to analyze the public attitude and apply to the gas taxi policy," European Journal of Operational Research, Elsevier, vol. 137(1), pages 145-161, February.
    5. Jiayu Wu & Qingsong He & Yunwen Chen & Jian Lin & Shantong Wang, 2020. "Dismantling the fence for social justice? Evidence based on the inequity of urban green space accessibility in the central urban area of Beijing," Environment and Planning B, , vol. 47(4), pages 626-644, May.
    6. Kou, Zhaoyu & Cai, Hua, 2019. "Understanding bike sharing travel patterns: An analysis of trip data from eight cities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 785-797.
    7. Dolnicar, Sara & Laesser, Christian & Matus, Katrina, 2010. "Short-haul city travel is truly environmentally sustainable," Tourism Management, Elsevier, vol. 31(4), pages 505-512.
    8. Diem-Trinh Le-Klähn & C. Michael Hall, 2015. "Tourist use of public transport at destinations – a review," Current Issues in Tourism, Taylor & Francis Journals, vol. 18(8), pages 785-803, August.
    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. García-Almeida, Desiderio Juan & Klassen, Norbert, 2017. "The influence of knowledge-based factors on taxi competitiveness at island destinations: An analysis on tips," Tourism Management, Elsevier, vol. 59(C), pages 110-122.
    2. 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.
    3. Xu, Zhengtian & Yin, Yafeng & Zha, Liteng, 2017. "Optimal parking provision for ride-sourcing services," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 559-578.
    4. Ren, Xiaohang & Zeng, Gudian & Dong, Kangyin & Wang, Kun, 2023. "How does high-speed rail affect tourism development? The case of the Sichuan-Chongqing Economic Circle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    5. 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).
    6. Albalate, Daniel & Fageda, Xavier, 2016. "High speed rail and tourism: Empirical evidence from Spain," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 174-185.
    7. Erik Karger & Marvin Jagals & Frederik Ahlemann, 2021. "Blockchain for Smart Mobility—Literature Review and Future Research Agenda," Sustainability, MDPI, vol. 13(23), pages 1-32, November.
    8. Ram, Yael & Gal-Tzur, Ayelet & Rechavi, Amit, 2021. "Identifying attributes of public transport services for urban tourists: A data-mining method," Journal of Transport Geography, Elsevier, vol. 93(C).
    9. Zamparini, L. & Domènech, A. & Miravet, D. & Gutiérrez, A., 2022. "Green mobility at home, green mobility at tourism destinations: A cross-country study of transport modal choices of educated young adults," Journal of Transport Geography, Elsevier, vol. 103(C).
    10. Velisaria Matzana & Aikaterina Oikonomou & Michael Polemis, 2022. "Tourism Activity as an Engine of Growth: Lessons Learned from the European Union," JRFM, MDPI, vol. 15(4), pages 1-15, April.
    11. Daniel Albalate del sol, 2015. "Evaluating HSR availability on Tourism: Evidence from Spanish Provinces and Cities," ERSA conference papers ersa15p288, European Regional Science Association.
    12. Benjamin Guinaudeau & Mark Brink & Beat Schäffer & Martin A. Schlaepfer, 2023. "A Methodology for Quantifying the Spatial Distribution and Social Equity of Urban Green and Blue Spaces," Sustainability, MDPI, vol. 15(24), pages 1-20, December.
    13. Fernández, Xosé Luis & Coto-Millán, Pablo & Díaz-Medina, Benito, 2018. "The impact of tourism on airport efficiency: The Spanish case," Utilities Policy, Elsevier, vol. 55(C), pages 52-58.
    14. Horng, Jeou-Shyan & Hu, Meng-Lei (Monica) & Teng, Chih-Ching (Chris) & Hsiao, Han-Liang & Liu, Chih-Hsing (Sam), 2013. "Development and validation of the low-carbon literacy scale among practitioners in the Taiwanese tourism industry," Tourism Management, Elsevier, vol. 35(C), pages 255-262.
    15. Qiang Yan & Kun Gao & Lijun Sun & Minhua Shao, 2020. "Spatio-Temporal Usage Patterns of Dockless Bike-Sharing Service Linking to a Metro Station: A Case Study in Shanghai, China," Sustainability, MDPI, vol. 12(3), pages 1-14, January.
    16. Nakamura, Hiroki & Abe, Naoya, 2016. "Tourist decisions in renting various personal travel modes: A case study in Kitakyushu City, Japan," Tourism Management, Elsevier, vol. 55(C), pages 85-93.
    17. Maurici Ruiz-Pérez & Joana Maria Seguí-Pons, 2020. "Transport Mode Choice for Residents in a Tourist Destination: The Long Road to Sustainability (the Case of Mallorca, Spain)," Sustainability, MDPI, vol. 12(22), pages 1-31, November.
    18. Gabriel Koman & Dominika Toman & Radoslav Jankal & Silvia Krúpová, 2024. "Public Transport Infrastructure with Electromobility Elements at the Smart City Level to Support Sustainability," Sustainability, MDPI, vol. 16(3), pages 1-25, January.
    19. Wang, Ruoyu & Cao, Mengqiu & Yao, Yao & Wu, Wenjie, 2022. "The inequalities of different dimensions of visible street urban green space provision: A machine learning approach," Land Use Policy, Elsevier, vol. 123(C).
    20. Kevin Blattler & Hannes Wallimann & Widar von Arx, 2024. "Free public transport to the destination: A causal analysis of tourists' travel mode choice," Papers 2401.14945, arXiv.org, revised Feb 2024.

    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:16:y:2024:i:17:p:7386-:d:1465353. 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.