Analysing the impact of weather on bus ridership using smart card data. Issue 2 (1st March 2015)

• Record Type: Journal Article
• Authors: Li, Linbo; Wang, Jing; Song, Ziqi; Dong, Zhi; Wu, Bing
• Is Part Of: IET intelligent transport systems. Volume 9:Issue 2(2015)
• Page Start: 221
• Page End: 229
• Publication Date: 2015-03-01
• Subjects: road vehicles -- smart cards -- environmental factors -- regression analysis -- public transport; weather impact analysis -- bus ridership -- smart card data -- Shanghai -- Fengxian -- cluster analysis -- ridership data analysis -- separate multiple linear regression models -- weather variables -- dummy variables -- humidity -- wind speed -- rainfall -- temperature -- bus route types -- mode share characteristics -- season characteristics -- weather attributes -- geographical contexts -- long‐term transit policy making -- decision making tool -- short‐term ridership forecasting -- data collection -- public transportation; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2014.0062
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : This study investigates the impact of weather on bus ridership using smart card data collected in Fengxian, Shanghai. The ridership data are categorised into three representative groups by the cluster analysis. The ridership data for each cluster are further divided according to the four seasons. Twelve separate multiple linear regression models with four weather variables and two dummy variables are constructed and calibrated. All four weather variables, namely humidity, wind speed, rainfall and temperature are found to have statistically significant negative effects on bus ridership. The magnitude of the impact varies depending on bus route types, seasons and mode share characteristics. Our analysis provides a valuable case study on weather's impact on bus ridership and concludes that there is no one‐size‐fits‐all conclusion about the relationships between weather attributes and bus ridership, and it is critical to investigate those relationships in different geographical contexts. The results of this study can be used not only for long‐term transit policy making but also as a decision making tool for short‐term ridership forecasting.