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Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM

Author

Listed:
  • Jinjun Tang

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Fan Gao

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Fang Liu

    (School of Transportation Engineering, Changsha University of Science and Technology, Changsha 410205, China)

  • Wenhui Zhang

    (Traffic School, Northeast Forestry University, Harbin 150040, China)

  • Yong Qi

    (School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China)

Abstract

Taxis are an important part of the urban public transit system. Understanding the spatio-temporal variations of taxi travel demand is essential for exploring urban mobility and patterns. The purpose of this study is to use the taxi Global Positioning System (GPS) trajectories collected in New York City to investigate the spatio-temporal characteristic of travel demand and the underlying affecting variables. We analyze the spatial distribution of travel demand in different areas by extracting the locations of pick-ups. The geographically weighted regression (GWR) method is used to capture the spatial heterogeneity in travel demand in different zones, and the generalized linear model (GLM) is applied to further identify key factors affecting travel demand. The results suggest that most taxi trips are concentrated in a fraction of the geographical area. Variables including road density, subway accessibility, Uber vehicle, point of interests (POIs), commercial area, taxi-related accident and commuting time have significant effects on travel demand, but the effects vary from positive to negative across the different zones of the city on weekdays and the weekend. The findings will be helpful to analyze the patterns of urban travel demand, improve efficiency of taxi companies and provide valuable strategies for related polices and managements.

Suggested Citation

  • Jinjun Tang & Fan Gao & Fang Liu & Wenhui Zhang & Yong Qi, 2019. "Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM," Sustainability, MDPI, vol. 11(19), pages 1-19, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5525-:d:273952
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    References listed on IDEAS

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    Cited by:

    1. Yanyan Chen & Hanqiang Qian & Yang Wang, 2020. "Analysis of Beijing’s Working Population Based on Geographically Weighted Regression Model," Sustainability, MDPI, vol. 12(12), pages 1-16, June.
    2. Lilis Laome & I Nyoman Budiantara & Vita Ratnasari, 2022. "Estimation Curve of Mixed Spline Truncated and Fourier Series Estimator for Geographically Weighted Nonparametric Regression," Mathematics, MDPI, vol. 11(1), pages 1-13, December.
    3. Jiawei Gui & Qunqi Wu, 2020. "Multiple Utility Analyses for Sustainable Public Transport Planning and Management: Evidence from GPS-Equipped Taxi Data in Haikou," Sustainability, MDPI, vol. 12(19), pages 1-46, September.
    4. Chen Xie & Dexin Yu & Ciyun Lin & Xiaoyu Zheng & Bo Peng, 2022. "Exploring the Spatiotemporal Impacts of the Built Environment on Taxi Ridership Using Multisource Data," Sustainability, MDPI, vol. 14(10), pages 1-24, May.
    5. Disheng Yi & Yusi Liu & Jiahui Qin & Jing Zhang, 2020. "Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing," Sustainability, MDPI, vol. 12(22), pages 1-20, November.
    6. Tang, Jinjun & Gao, Fan & Han, Chunyang & Cen, Xuekai & Li, Zhitao, 2021. "Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi," Journal of Transport Geography, Elsevier, vol. 95(C).

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