Co-Evolutionary Optimization Algorithm Based on the Future Traffic Environment for Emergency Rescue Path Planning
Co-Evolutionary Optimization Algorithm Based on the Future Traffic Environment for Emergency Rescue Path Planning
复制标题
基于未来交通环境的应急救援路径规划协同进化优化算法
DOI：
10.1109/access.2020.3014609
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发表时间：
2020-08
期刊：
影响因子：
3.9
通讯作者：
Wu JiaBin
中科院分区：
文献类型：
--
作者：
Wen Huiying;Lin Yifeng;Wu JiaBin
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.
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考虑未来环境不确定性的事故应急救援时序协同进化路径优化方法
影响因子：
3.9
作者：
Wen Huiying;Wu Jiabin;Duan Yuchen;Qi Weiwei;Zhao Sheng
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DOI：
10.1016/j.physa.2016.11.129
发表时间：
2017-03
影响因子：
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DOI：
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发表时间：
2019-10
期刊：
Transportation Research Part B: Methodological
影响因子：
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作者：
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E. Suzdaleva;I. Nagy
DOI：
10.1109/icccs.2009.12
发表时间：
2009-12
期刊：
2009 International Conference on Computer and Communications Security
影响因子：
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通讯作者：
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DOI：
10.21311/001.39.7.32
发表时间：
2016
期刊：
--
影响因子：
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