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<p>As a critical component of urban public transport systems, taxi services have attracted increasing scholarly attention because their supply and demand are often spatially and temporally imbalanced. Understanding the spatiotemporal patterns and drivers of taxi supply-demand imbalance is important for improving residents’ travel well-being. Using 2023 taxi origin-destination (OD) records from Urumqi and multi-source data, including points of interest (POIs), this study constructs a regional attractiveness model. With the supply-demand ratio as the core indicator, the study examines the spatiotemporal distribution of taxi supply-demand imbalance across workdays, weekends, and holidays. A multinomial logistic regression (MLR) model is then used to identify the factors associated with taxi supply-demand states. The results show that taxi supply-demand imbalance zones in Urumqi largely coincide with areas of high regional attractiveness and are closely associated with commercial centers, transport hubs, and densely populated residential zones. In 2023, daily changes in taxi supply-demand conditions were consistent with the tourism calendar in Xinjiang. At daily and weekly scales, the imbalance was mainly shaped by regional functions and residents’ travel behavior. For example, taxi demand increased markedly in residential areas during the morning peak, whereas the supply-demand state around Urumqi Railway Station was closely related to train arrival and departure schedules. Public transport stops and related facilities were the dominant factors influencing taxi supply-demand imbalance during weekday commuting hours. During weekends and holidays, shopping and catering facilities became the major attractors of taxi demand, indicating that residents’ daily activities substantially influence taxi demand dynamics. These findings deepen the understanding of the mechanisms linking urban taxi demand with multiple influencing factors and provide a reference for urban transport planning, taxi operation management, and residents’ travel decisions.</p>

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Spatiotemporal patterns and influencing factors of taxi supply-demand imbalance: A case study of Urumqi, China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月16日

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  • Shijun Lu,
  • Changbo Ma,
  • Lixiao Wang,
  • Xiaoyan Tang,
  • Jianhu Wang,
  • Xiaoyi Li

摘要

As a critical component of urban public transport systems, taxi services have attracted increasing scholarly attention because their supply and demand are often spatially and temporally imbalanced. Understanding the spatiotemporal patterns and drivers of taxi supply-demand imbalance is important for improving residents’ travel well-being. Using 2023 taxi origin-destination (OD) records from Urumqi and multi-source data, including points of interest (POIs), this study constructs a regional attractiveness model. With the supply-demand ratio as the core indicator, the study examines the spatiotemporal distribution of taxi supply-demand imbalance across workdays, weekends, and holidays. A multinomial logistic regression (MLR) model is then used to identify the factors associated with taxi supply-demand states. The results show that taxi supply-demand imbalance zones in Urumqi largely coincide with areas of high regional attractiveness and are closely associated with commercial centers, transport hubs, and densely populated residential zones. In 2023, daily changes in taxi supply-demand conditions were consistent with the tourism calendar in Xinjiang. At daily and weekly scales, the imbalance was mainly shaped by regional functions and residents’ travel behavior. For example, taxi demand increased markedly in residential areas during the morning peak, whereas the supply-demand state around Urumqi Railway Station was closely related to train arrival and departure schedules. Public transport stops and related facilities were the dominant factors influencing taxi supply-demand imbalance during weekday commuting hours. During weekends and holidays, shopping and catering facilities became the major attractors of taxi demand, indicating that residents’ daily activities substantially influence taxi demand dynamics. These findings deepen the understanding of the mechanisms linking urban taxi demand with multiple influencing factors and provide a reference for urban transport planning, taxi operation management, and residents’ travel decisions.