How fast the emergency rescue has been one of the most critical factors to control the further deterioration of accident conditions, which can save more lives and reduce property loss in time. Unlike other backgrounds, emergency rescue pays more attention to boost rescue efficiency and enhance its reliability than the cost of that. As an essential component, path planning applied in emergency rescue can effectively shorten the travel time and improve the robustness of the rescue path. However, there still exist various uncertainties that make a great impact on selecting rescue path. In order to address the problem of improving rescue efficiency, an extended co-evolutionary optimization (ECEO) algorithm based on the framework of A* algorithm is proposed in this study, which is particularly interesting in the evolution mechanism of the future traffic environment. Meanwhile, considering the characteristics of urban road traffic, this study improves the evolution mechanism about how the sub-path weight function co-evolves with the future traffic environment in ECEO. Finally, simulation experiments are designed to evaluate the best optimization effect and robustness of ECEO algorithm in emergency rescue. The experimental results show that the ECEO algorithm based on the cooperative optimization mechanism is superior to A* algorithm based on dynamic programming mechanism both in terms of the travelling time and its stability, and it can effectively raise the efficiency of emergency rescue.

CICTP