Vehicle traffic delay prediction in ferry terminal based on Bayesian multiple models combination method

This paper presents a new class of models for predicting vehicle traffic delay in ferry terminals. Ferry service plays an important role in many cities adjacent to navigable bodies of water. Transportation agencies, port authorities, and drivers can benefit from a reliable method for ferry traffic delay prediction, which can support improved ferry service scheduling, land-based transit and ferry coordination, and proactive trip planning. In this study, vehicle traffic delay within a ferry terminal is considered to be comprised of both periodic and dynamic components. Frequency analysis is applied to confirm the presence of a consistent periodic trend. A combination method is adopted to fit the dynamic component, which assembles artificial neural network, support vector machine, and ARIMA model in a Bayesian framework. The results presented indicate that, by aggregating the output of multiple models, this model ensemble approach can lead to greater predictive accuracy in modelling ferry traffic delay.