Analysing the relationship between weather, built environment, and public transport ridership. Issue 14 (22nd February 2021)

• Record Type: Journal Article
• Authors: Lin, Pengfei; Weng, Jiancheng; Brands, Devi K.; Qian, Huimin; Yin, Baocai
• Is Part Of: IET intelligent transport systems. Volume 14:Issue 14(2020)
• Page Start: 1946
• Page End: 1954
• Publication Date: 2021-02-22
• Subjects: rain -- smart cards -- geophysics computing -- regression analysis -- road traffic -- traffic engineering computing; public transport ridership -- sustainable public transport system -- influence mechanisms -- environment separately -- smart card data -- weather information -- Light Gradient Boosted Machine -- built environment variables -- daily ridership -- traffic analysis zone level -- nonlinear relationship -- interaction effects -- weather conditions -- ridership fluctuations -- threshold effects -- adverse weather -- public transport networks -- scheduling service frequency; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2020.0469
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : 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.