Automatic generation of optimal road trajectory for the rescue vehicle in case of emergency on mountain freeway using reinforcement learning approach

Abstract

Abstract Rapid rescue response has the highest priority in case of emergency randomly happening on the freeway network, which allows rescue vehicles to have many trajectory options. Searching for the fastest way is not easy within a short time after traffic accident happens especially for the mountainous area with special characteristics such as limited traffic capacity, enclosed internal space and so on. Here, road segment model is proposed to determine smallest road segment covering possible rescue ways. Other than traditional optimal search methods, modified reinforcement‐leaning is introduced to find the optimal road trajectory. The proposed methods are tested in the freeway of Qinling Tunnel group, Xihan Freeway of Shaanxi province, China as a case study. Compared with traditional shortest path method, the rescue vehicle arrival time to the accident location is shortened from 22.9 to 6.5 min and dissipation time is also shortened from 52.4 to 25.6 min. Both of them show the proposed road trajectory could improve the rescue effectiveness and reduce the influence to road network. Successful application of these case study shows they could probably extend to use to other scenarios and contribute to improve the intelligence transportation system.

Keywords

• Optimisation techniques
• Traffic engineering computing
• Reinforcement learning