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

Abstract

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.

DataFITS: A Heterogeneous Data Fusion Framework for Traffic and Incident Prediction

“…Data fusion combines data from multiple sources, enriching spatiotemporal information [7], [8], [9], [10]. Several applications benefit from data fusion, such as emergency management [11] and path planning [12]. However, fusing heterogeneous data requires additional preprocessing to combine various data types and features [13], [14].…”

State-of-the-Art Review on Traffic Control Strategies for Emergency Vehicles

“…In addition, considering the dynamic nature of urban roads, an improved RSA is used for emergency rescue route planning. Subsequently, Wen et al further investigate vehicle dynamic route planning and propose a co-evolutionary algorithm to solve the route planning problem for emergency rescue [33], where the optimal route is calculated using the Dijkstra's algorithm and RSA. The optimal route is periodically optimized as the dynamic traffic environment changes.…”

“…Response time + Robustness of emergency routes [32], [33], [38] \ \ [97] Others [16], [21]- [23], [25], [41], [42] [59], [63], [65]- [67], [69], [73], [76] [87]- [89] [93]…”

SoC-VRP: A Deep-Reinforcement-Learning-Based Vehicle Route Planning Mechanism for Service-Oriented Cooperative ITS

“…Su et al [34] presented a decentralized non-preemptive framework for simultaneous dynamic routing. Wen et al [35] introduced the coevolutionary algorithm that uses the evolution mechanism to calculate the subpath weight function for emergency rescue path planning. Wu et al [36] created an algorithm based on search and integer linear programming to clear a lane for nearby emergency lanes to ensure smooth and fast passage.…”