Abstract
Emergency events can induce serious traffic congestions in a local area which may propagate to the upstream roads, and even the whole network. Until now, the methodology forecasting spatiotemporal boundary propagation of emergency-event-based traffic congestions, with both explicitness and road network availability, has not been found. This study develops a new method for predicting spatiotemporal boundary of the congestion caused by emergency events, which is more applicable and practical than cell transmission model (CTM)-derived methods. This method divides the expressway network into different sections based on their functions and the shockwave direction caused by the emergency events. It characterizes the velocity of the moving congestion boundary based on kinetic wave theory and volume–density relationship. After determining whether the congestion will spread into the network level through an interchange using a new concept, highway node acceptance capacity (HNAC), we can predict the spatiotemporal boundary and corresponding traffic condition within the boundary. The proposed method is tested under four traffic incident cases with corresponding traffic data collected through field observations. We also compare its prediction performances with other methods used in the literature.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
Based on the above analysis, we could draw two conclusions. The accuracy of this method in forecasting spatiotemporal boundary propagation time is adequate in practical work
Explicit and practical methods on forecasting spatiotemporal boundary propagation of emergency-event-based congestion in expressway network have not been found in related works
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Emergency events such as traffic accidents are disruptive to traffic flow. When an emergency event happens, it can induce serious traffic congestion, and it can further spread to upstream road network. Predicting the evolution of the traffic congestion (i.e., the spatiotemporal boundary of traffic congestion), the traffic condition in the congested area is critical to design effective measures to address the negative effects of these events. The traffic managers could close some entrances and diverge the trapped vehicles according to the predicted spatiotemporal boundary to reduce the traffic congestion
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