Adaptive prediction of traffic incident duration using change detection and Bayesian networks

Real-time incident management reduces non-recurrent congestion in transport systems. Accurate incident duration prediction is pivotal for traffic incident management and ensuring roadway safety. Recent advancements in prediction methods utilise machine learning with historical incident data and other relevant sources. However, incident duration is influenced by many dynamic factors within the transport system, such as varying traffic congestion and resources of the response team/system. Traditional machine learning models with fixed parameters may experience model drift issues, which degrade its prediction power due to changes in the environment. This paper introduces a traffic incident duration prediction model that incorporates a change detection method and Bayesian network model. The objective is to provide an accurate predictive traffic incident duration model which can automatically detect and adapt to system changes over varying temporal scales. Using New York City as a case study, our findings reveal systematic changes in the distribution of traffic incident duration during 2015-2021. The adaptability analysis also demonstrates that our proposed model is able to capture these temporal changes, update the learned parameters in real time, and minimise the need for constant off-line calibrations.