城市路网多路口路径动态实时选择方法

为了缓解城市交通拥堵问题，如何充分利用现有的道路资源进行有效的路线导航，一直是学者们关心的热点问题.现有的研究方法包括：优化交通灯信号周期以增大交通流量；对个别车辆的行驶路线进行优化；利用历史交通数据或者通过路网中心和车辆之间的主从式博弈进行路径导航等.然而，这些研究并没有考虑到微观行驶车辆的个性化交通需求以及多车辆彼此之间的路线选择冲突，对于城市路网中交通状况的动态不确定性也没有充分考虑.基于以上问题，提出了城市交通路网动态实时多路口路径选择模型DR²SM（dynamic and real-time route selection model in urban traffic networks），结合车辆对前方可选路线的偏好以及可选路线的实时交通状况，并利用自适应学习算法SALA（self-adaptive learning algorithm）进行博弈，以使得各行驶车辆的动态路线选择策略达到Nash均衡.

Dynamic Real-Time Algorithm for Multi-Intersection Route Selection in Urban Traffic Networks

In order to alleviate traffic congestion for vehicles in urban traffic networks, many researchers have studied how to utilize the traffic resources such as roads effectively to supply effective route selection strategies for vehicles. Most of the current researches mainly focus on optimizing the signal cycle of traffic lights, supplying the optimized route selection for individual vehicles, and dispersing vehicles on the alternative routes based on their historical driving data or through the traffic game between the information center and the vehicles. However, the above methods have not considered the personalized traffic demands of each vehicle, the route selection conflicts between vehicles, or even the dynamic and uncertain traffic conditions in urban road networks. To solve these problems, this paper proposes a dynamic and real-time route selection model in urban traffic networks (DR²SM), which incorporates the preference for the alternative routes and the real-time traffic conditions. Through mutual information exchange, each vehicle uses a self-adaptive learning algorithm (SALA) to play the congestion game with each other to reach Nash equilibrium.