Uncertainty Quantification and Predictability Analysis for Traffic Forecasting at Multiple Scales

Observing, modelling, predicting, and understanding the dynamics of traffic systems on different levels is one of the most critical topics in the transport and planning domain. At the macroscopic scale, traffic congestion is the central problem that impacts all aspects of society. Traffic congestion costs valuable travel time, extra fuel consumption, and frustration in daily life. Traffic congestion is not always avoidable but accurate predictions of traffic conditions in a road network are useful for road users. For example, drivers can make faster and safer route choices based on the estimated time of arrival and the predicted congestion evolution. Reliable traffic forecasting also provides essential information for real-time traffic control systems and the development of long-term sustainable mobility systems. On the other hand, on the microscopic level, modelling the interaction between road users and predicting their behaviours is drawing more and more attention due to the increasing popularity of autonomous driving. Accurately anticipating other agents’ decisions is indispensable for a safe and smooth autopilot system. This actively-studied domain has become the focus of many scholars from academia and engineers from industry.