Impacts of weather on cycling and walking on twin trails in Seattle

• Published in: Transportation research. Part D, Transport and environment Transport and environment, 2019-12, Vol.77, p.573-588
• Main Authors: Zhao, Jinbao, Guo, Cong, Zhang, Ruhua, Guo, Dong, Palmer, Mathew
• Format: Article
• Language: English
• Subjects: Active travel; Buffered effects; Rainfall; Trail; Weather
• ISSN: 1361-9209; 1879-2340
• DOI: 10.1016/j.trd.2019.09.022

•Cycling could be more severely influenced by adverse weather than walking.•Walking on the forest Burke–Gilman Trail is very resilient to weather influence.•Rainfall have significant impacts on active travel of 1 h before and 2 h after the rainfall.•Reducing the buffered effects of rainfall has potential in increasing active travel activities. Active travel is associated with various health, environmental, social, and economic benefits. However, barriers exist in active travel activities and promotion. In particular, cycling and walking tend to be more severely affected by inclement weather than encapsulated modes, such as passenger and transit vehicles. This study analyzes the impacts of weather and climate conditional changes on the usage of twin multi-use trails in Seattle, United States. Both comparative analysis and residual regression analysis methods are used to examine the impacts. The buffered effects of rainfall on cycling and walking are particularly investigated. The findings indicate that at the daily level, weather conditions are more influential on active travel on the two trails on weekdays than on weekends. Nevertheless, cyclists and pedestrians on weekdays tend to be more resilient to weather influence than weekend riders and walkers at the hourly level. Cycling could be more severely influenced by weather condition changes than walking, especially on weekdays. The concurrent rainfall not only affects the concurrent active travel, but it also affects the usage of trails 1 h earlier. Comparatively, the delay effects of rainfall on active travel can last a longer period of time for cycling on weekdays. Note that self-selection in a time-series data analysis, particularly at a finer temporal scale, must be controlled. This study discusses the implications of these findings and highlights the potentials of accurate real-time or near real-time weather prediction, weather information push service for active travelers, and facilities’ clearing in improving the cycling and walking experience.