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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
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    References listed on IDEAS

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    2. Qingwen Xue & Lingzhi Cheng & Zhichao Li & Yingying Xing & Hongwei Wang & Hongwei Li & Yichuan Peng, 2025. "Unraveling Spatial–Temporal and Interactive Impact of Built Environment on Metro Ridership: A Case Study in Shanghai, China," Sustainability, MDPI, vol. 17(21), pages 1-20, October.

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