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access icon free Analysing the relationship between weather, built environment, and public transport ridership

  • Author(s): Pengfei Lin 1 ; Jiancheng Weng 1 ; Devi K. Brands 2 ; Huimin Qian 3 ; Baocai Yin 1
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  • Source: Volume 14, Issue 14, 27 December 2020, p. 1946 – 1954
    DOI:  10.1049/iet-its.2020.0469 , Print ISSN 1751-956X, Online ISSN 1751-9578
© The Institution of Engineering and Technology
Received 29/07/2020, Accepted 30/11/2020, Revised 19/11/2020, Published 14/12/2020

For a sustainable public transport system, it is important to unveil the spatiotemporal characteristics of ridership and identify the influence mechanisms. Some studies analysed the effects of weather and built environment separately, however, their effects when incorporated remains to be determined. Using smart card data, weather information, and point of interest data from Beijing, the Light Gradient Boosted Machine was employed to investigate the relative importance of weather and built environment variables contributing to daily ridership at the traffic analysis zone level, and investigate the non-linear relationship and interaction effects between them. Weather conditions and built environment contribute 30.22 and 55.83% to ridership fluctuations, respectively. Most variables show complex non-linear and threshold effects on ridership. The interaction effects of weather and weekend/public holiday have a more substantial influence on ridership than weekdays, indicating weather conditions have less impact on regular commuting trips than discretionary trips. The ridership fluctuations in response to changing weather conditions vary with spatial locations. Adverse weather, such as strong wind, high humidity, or heavy rainfall, has a more disruptive impact on leisure-related areas than on residence and office areas. This study can benefit stakeholders in making decisions about optimising public transport networks and scheduling service frequency.

Inspec keywords: rain; traffic engineering computing; road traffic; geophysics computing; regression analysis; smart cards

Other keywords: nonlinear relationship; smart card data; Light Gradient Boosted Machine; interaction effects; sustainable public transport system; weather conditions; threshold effects; influence mechanisms; environment separately; public transport networks; public transport ridership; weather information; traffic analysis zone level; ridership fluctuations; adverse weather; daily ridership; scheduling service frequency; built environment variables

Subjects: Geophysics computing; Regression analysis; Traffic engineering computing

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