【摘要】 针对启发式算法利用Dijkstra算法求解大型动态网络中疏散路径规划问题时,存在疏散时间长、稳定性差等不足,该文提出了一种改进CCRP的方法,即用蚁群算法替代Dijkstra算法求解最优路径,进而减少疏散时间,增加估算疏散时间的精确性。实验表明,该方法能够在大型动态网络下实现路网容量受限的疏散路径规划有效求解,具有疏散时间短、疏散路径少、线性关系强等特点,相比原有CCRP算法更能满足实际疏散的需要。在寻找最优路径上采用蚁群算法求解,相比贪心算法更能支持全局最优、并行计算、疏散效率更高,在支持路况信息实时更新、大规模人群快速疏散、及时调整疏散路线等方面更具优势。更多还原

【Abstract】 In view of the shortcomings such as long evacuation time,poor stability and so on,when the heuristic algorithm for CCRP using the Dijkstra algorithm to solving large-scale dynamic network evacuation route planning problem,this paper presented an improved CCRP method,which used ant colony algorithm to find the optimal path instead of Dijkstra algorithm,which reduced evacuation time and increased the accuracy of estimating evacuation time.Experiments showed that this method could effectively solve the evacuation route planning with capacity constraints in large-scale dynamic network.It has the characteristics of short evacuation time,less evacuation route and strong linear relationship.Compared with the original CCRP algorithm,this method could better meet the needs of actual evacuation.Ant colony algorithm was used to find the optimal path.Compared with greedy algorithm,it could support global optimization,parallel computing and higher evacuation efficiency.And more advantages in supporting real-time updating of road condition,rapid evacuation of large-scale crowd,timely adjustment of evacuation route and so on.更多还原