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A conflict-congestion model for pedestrian-vehicle mixed evacuation based on discrete particle swarm optimization algorithm

机译:基于离散粒子群算法的行车混合疏散冲突拥挤模型

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摘要

A simulation model based on temporal-spatial conflict and congestion for pedestrian-vehicle mixed evacuation has been investigated. Assuming certain spatial behaviors of individuals during emergency evacuation, a discrete particle swarm optimization with neighborhood learning factor algorithm has been proposed to solve this problem. The proposed algorithm introduces a neighborhood learning factor to simulate the sub-group phenomenon among evacuees and to accelerate the evacuation process. The approach proposed here is compared with methods from the literatures, and simulation results indicate that the proposed algorithm achieves better evacuation efficiency while maintaining lower pedestrian-vehicle conflict levels.
机译:研究了基于时空冲突和拥堵的人车混合疏散仿真模型。假设紧急疏散过程中个体的某些空间行为,提出了一种基于邻域学习因子算法的离散粒子群优化算法来解决该问题。所提出的算法引入了邻域学习因子,以模拟疏散人员中的小组现象并加快疏散过程。将本文中提出的方法与文献中的方法进行了比较,仿真结果表明,该算法在保持较低的行人与车辆之间的冲突水平的同时,实现了更高的疏散效率。

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