无诱导信息条件下基于信念学习博弈的车辆路径选择研究

Study on Non-guidance Vehicle Routing Problem Based on Brief Learning Game

无诱导信息条件下,对驾驶员路径选择影响最大的是近期经验。本文以博弈论、信念学习理论为基础,建立γ-加权信念学习的无诱导信息车辆路径选择模型,通过matlab仿真得出不同初始状态下的博弈平衡结果。结果表明:当路网交通量小于路网通行能力时,经过多次选择博弈后路网会呈稳定平衡的状态,两条道路上的交通流趋向于均匀分布,路网交通流的分布与两条路径初始比例m无显著关系;当交通量大于等于路网通行能力时,路网会呈峰谷平衡的状态,路网交通量越大波动越明显,路网交通流的分布与两条路径初始比例m相关。

In the absence of guidance information conditions , the drivers usually rely on recent experience to choose the route. In this paper ,based on the game theory and belief learning theory ,the paper establishes a model of the no induced information vehicle routing model for the study of gamma weighted beliefs ,and obtains the game equilibrium results under different initial states through MATLAB simulation . The results showed that the network traffic flow amount is less than the road network capacity ,after repeated selection game posterior network will be a stable equilibrium state , two on the road traffic flow tends to uniform distribution ,the distribution of network traffic flow and two initial path ratio m have no significant relationship ;when the traffic flow is greater or equal to the road network capacity ,the network will show the status of peak and valley balance ,the larger the amount of network traffic flucations more obvious ,the road net-work traffic flow distribution and two initial path ratio m have related .