Forecasting Spatiotemporal Boundary of Emergency-Event-Based Traffic Congestion in Expressway Network Considering Highway Node Acceptance Capacity

Xingliang Liu, Jian Wang, Tangzhi Liu and Jin Xu
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Xingliang Liu: College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Jian Wang: School of Transportation, Southeast University, Nanjing 211189, China
Tangzhi Liu: College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Jin Xu: College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China

Sustainability, 2021, vol. 13, issue 21, 1-17

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.

Keywords: spatiotemporal boundary; emergency event; expressway network; HNAC; traffic incident cases (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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