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The Optimization Study on Passenger Organization at Passenger Station Based on CAS

机译:基于CAS的客运站旅客组织优化研究。

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The paper combines the passenger behavior with the decision of administrative department based on multi-agent simulation method of complex system theory, considering each advantage of CA and multi-agent, with the entrance of administrative departments and passenger behavior. In the passenger organization optimization model involving in different kinds of agents such as passenger organization agent and passenger agent, which combines multi-agent with CA, the environment factor is expressed by CA and the paper constructs multi-agent behavior decision model depending on the basic frame of cellular automata (CA) pedestrian flow. Individuals respond to the changes of environment around through the interaction between resource and the environment or negotiating with another individual. Through simulating the complex process of decision making of administrative departments, it brings forward the technical route and the basic frame of the passenger organization optimization of large-scale passenger station on complex adaptive system(CAS), constructs the optimization system of passenger organization based on multi-agent, discusses the structure and the competing and cooperating relationship between each agent, and designs the optimization arithmetic based on genetic algorithm(GA) of multi-agent. At last, taking the optimization of passenger organization during rush hours at Guangzhou Station as the example to analysis and getting a good effect, this can offer the reference to the decision making of administrative departments.
机译:基于复杂系统理论的多智能体模拟方法,结合行政管理部门的介入和旅客行为,结合复杂系统理论的多智能体仿真方法,将旅客行为与行政部门的决策结合起来。在涉及多种类型的旅客组织的旅客组织优化模型中,如将旅客组织代理和旅客代理相结合,将多人代理与CA结合起来,环境因子用CA表示,本文根据基本情况构造了多人行为决策模型。元胞自动机(CA)行人流的框架。个人通过资源与环境之间的互动或与另一个人进行谈判来应对周围环境的变化。通过模拟行政部门决策的复杂过程,提出了基于复杂自适应系统(CAS)的大型客运站客运组织优化的技术路线和基本框架,构建了基于客运专线的客运组织优化系统。讨论了多智能体的结构以及它们之间的竞争与合作关系,并设计了基于多智能体遗传算法的优化算法。最后,以广州站高峰时段旅客组织的优化为例进行分析,取得了良好的效果,可为行政部门的决策提供参考。

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