路网拥塞控制中的多目标路径决策模型研究

智能交通系统领域中的路网拥塞控制是解决路网拥塞问题的主要手段之一,针对该问题,利用自底向上的agent建模方式,构建一种多目标路径决策agent移动模型.在该模型中,车辆agent兼顾最短路径和拥塞避免两个优化目标,通过车辆agent行驶距离最短(最短路径)和途经区域的拥塞程度最低(拥塞避免)两个目标优化来动态进行路径决策.基于多目标路径决策移动模型一方面能够实现对交通拥堵路段的分流控制,另一方面能够挖掘网络拓扑结构中易发生拥塞的路口的共同特征,为路网拥塞控制提供帮助.仿真实验结果表明,该模型能较好地改善路网结构中的拥塞路段.针对不同链路密度及链路分布的网络所进行的仿真实验结果进一步表明,路网结构的链路密度对拥塞路段出现在网络中的地理位置影响不同,而路口节点位置影响其拥塞程度;网络结构的链路分布形态对发生拥塞路段的地理位置和拥塞优化结果具有直接影响.

A Multi-objective Routing Decision Model in Vehicle Transport Network Congestion Control

Vehicle transport network congestion control in the field of ITS is one of the main means to solve the problem of network congestion. By using a bottom-up agent modeling method, a multi-objective routing decision agent model was constructed to solve the problem. In the model, vehicles were simulated as mobile agents, which determined the routing decision dynamically by a two-objective optimization: the shortest travel distance and the minimum congestion degree of the road network. The multi-objective routing decision model could improve the congestion intersection, and on the other hand, could dig out the common features of congestion intersection to help the network congestion control. The simulation results show that the model can improve road network congestion. And the simulations to compare the effect of network structure with different link densities and link distribution on the road network congestion distribution further indicate that link density of the road network has no effect on the location of the emergent congestion intersections in the network, and the intersection location affects the degree of congestion. It also indicates that the link distribution of the network has a direct effect on the location of the emergent congestion intersections in the network and congestion optimization results.