Co-Evolutionary Optimization Algorithm Based on the Future Traffic Environment for Emergency Rescue Path Planning

Emergency rescue plays a key role in accident remediation and prevention. It has been the most critical factor to control the negative impacts of accident deterioration, which can save more lives and reduce property loss in time. As an essential component, emergency rescue path planning can effectively shorten the travelling time and improve the robustness of the rescue path. However, there still exist various uncertainties that may make a great impact on selecting the rescue path, which is less successful and still requires further research. To address the problem of low rescue efficiency, a co-evolutionary optimization algorithm (CEOA) is proposed in this study. Meanwhile, this study presents how the sub-path weight function co-evolves with the future traffic environment dynamics using the evolution mechanism, considering the complex vehicle running characteristics in the urban roads. Three sets of simulation experiments are conducted to test the comprehensive performance of CEOA under various scenarios. Experimental results show that the proposed CEOA is superior to traditional and emerging path optimization methods in terms of the travelling time and its stability, such as on-line re-optimization (OLRO) and co-evolutionary path optimization (CEPO). The proposed CEOA integrates the advanced advantages of regular re-optimization and co-evolutionary optimization, and opens the door to develop new path optimization technology. The findings provide powerful technology support and a theoretical basis for emergency rescue management improvement.