Analysing the impact of weather on bus ridership using smart card data
- Author(s): Linbo Li 1 ; Jing Wang 1 ; Ziqi Song 2 ; Zhi Dong 1 ; Bing Wu 1
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View affiliations
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Affiliations:
1:
Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai, 201804, People's Republic of China;
2: Department of Civil and Environmental Engineering, Utah State University, Logan, UT 84322, USA
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Affiliations:
1:
Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai, 201804, People's Republic of China;
- Source:
Volume 9, Issue 2,
March 2015,
p.
221 – 229
DOI: 10.1049/iet-its.2014.0062 , Print ISSN 1751-956X, Online ISSN 1751-9578
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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.
Inspec keywords: regression analysis; smart cards; road vehicles; environmental factors; public transport
Other keywords: public transportation; rainfall; long-term transit policy making; weather variables; ridership data analysis; Shanghai; season characteristics; Fengxian; data collection; temperature; weather impact analysis; short-term ridership forecasting; geographical contexts; decision making tool; mode share characteristics; separate multiple linear regression models; wind speed; weather attributes; smart card data; humidity; bus ridership; bus route types; dummy variables; cluster analysis
Subjects: Systems theory applications in transportation; Other topics in statistics
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