Journal article Open Access
Rowe, Francisco; Mahony, Michael; Tao, Sui
Given an increasingly volatile climate, the relationship between weather and transit ridership has drawn increasing interest. However, challenges stemming from spatio-temporal dependency and non- stationarity have not been fully addressed in modelling and predicting transit ridership under the influence of weather conditions. Drawing on three-month smart card data in Brisbane, Australia, this research implements a suite of tree-based machine-learning algorithms, to model and predict near real-time bus ridership in relation to sudden change of weather conditions. The study confirms that there indeed exists a significant level of spatio-temporal variability of weather-ridership relationship, which produces equally dynamic patterns of prediction errors.