Assessing Machine Learning Algorithms for Near-Real Time Bus Ridership Prediction During Extreme Weather
This work addresses a domain-specific problem for transit planners by providing incremental improvements in ridership prediction during weather events.
The study tackled predicting near-real-time bus ridership during extreme weather by assessing machine-learning algorithms like random forest, XGBoost, and Tweedie XGBoost using smart card data from Brisbane, and found that Tweedie XGBoost outperformed the others in accuracy.
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 especially with the traditional statistical approaches. Drawing on three-month smart card data in Brisbane, Australia, this research adopts and assesses a suite of machine-learning algorithms, i.e., random forest, eXtreme Gradient Boosting (XGBoost) and Tweedie XGBoost, 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. Further comparison of model performance suggests that Tweedie XGBoost outperforms the other two machine-learning algorithms in generating overall more accurate prediction outcomes in space and time. Future research may advance the current study by drawing on larger data sets and applying more advanced machine and deep-learning approaches to provide more enhanced evidence for real-time operation of transit systems.