@ARTICLE{ iet:/content/journals/10.1049/iet-its.2014.0062, author = {Linbo Li}, affiliation = {Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai, 201804, People's Republic of China}, author = {Jing Wang}, affiliation = {Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai, 201804, People's Republic of China}, author = {Ziqi Song}, affiliation = {Department of Civil and Environmental Engineering, Utah State University, Logan, UT 84322, USA}, author = {Zhi Dong}, affiliation = {Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai, 201804, People's Republic of China}, author = {Bing Wu}, affiliation = {Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai, 201804, People's Republic of China}, 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;}, ISSN = {1751-956X}, language = {English}, 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.}, title = {Analysing the impact of weather on bus ridership using smart card data}, journal = {IET Intelligent Transport Systems}, issue = {2}, volume = {9}, year = {2015}, month = {March}, pages = {221-229(8)}, publisher ={Institution of Engineering and Technology}, copyright = {© The Institution of Engineering and Technology}, url = {https://digital-library.theiet.org/;jsessionid=8hoohn2qlqok1.x-iet-live-01content/journals/10.1049/iet-its.2014.0062} }