Route Planning of Rescue Vehicles in the Process of Dynamic Change of Traffic Volume under Emergency Conditions

Abstract

Emergency evacuation on freeways is a process aiming to transfer people from dangerous area to the safe area as quickly as possible. Route planning, therefore, plays an important role during this process. This paper proposes a systematic method to generate optimized routes for rescue vehicles step by step. First, the free flow traveling time and historical traffic volume is calculated from large regional toll collection data. Then the Temporal Convolutional Network (TCN) is adopted to generate real-time ratio between road segments and toll gates. Finally, the Dynamic Bureau of Public Road (DBPR) function and Dijkstra algorithm are used to obtain real-time optimized routes for rescue vehicles. The proposed algorithms are tested on a hypothetical emergency event taking place on the Shantou-Kunming expressway in Xingyi, Anhui Province. The computational results show that the generated rescue routes are helpful for rescue vehicles and can save plenty of time. Generate rescue routes rapidly and accurately may provide a practical method for emergency evacuation without expensive facilities and can be a guide for further rescue operations.