为弥补已有驾驶员路径选择博弈模型将驾驶员视为完全理性的不足，探求无诱导信息情况 下路网交通流临界状态，将驾驶员视为有限理性，其依赖累积时间感受收益做出下一次的路径选 择策略，并以驾驶员的行程时间感受作为决策收益建立了基于累积自学习机制的无诱导信息驾驶 员路径选择博弈模型。利用该模型，分析了驾驶员路径选择行为对路网交通流的影响，并通过仿 真验证得出了不同初始状态下的模型博弈平衡结果。仿真结果表明：博弈平衡状态与路网车流总 量及初始流量分配比例密切相关。当路网车流总量小于或接近路网总通行能力时，不发布诱导信 息，路网的交通流分布达到稳定平衡，路网通行能力利用率较高；当路网车流总量远大于路网总 通行能力时，不发布诱导信息，路网交通流分布会形成峰谷平衡，不能有效利用路网通行能力， 应采取相应的交通管理措施。

In order to make up the shortage of regarding the drivers as totally rational by the existing game theory models of driver′s route selection, and search the fettle of network traffic flow without induc⁃ ing information, the drivers′ rationality was deemed bounded, so he or she made the selection decision of next path according to the cumulative feeling gains of time. The driver′s feeling of travel time was consid⁃ ered as the gains of decision, then the model of the driver′s routing selection without inducing informa⁃ tion was established based on cumulative self-learning mechanism, and the impact of the driver′s route choice behavior on road network traffic flow was analyzed. Finally the equilibrium results of fuzzy game on different initial states were obtained by the simulation. The simulation results show that the balance of fuzzy game is closely related to the total amount of road network traffic and initiatory flow distribution ratio. When the total amount of road network traffic is less than or close to the capacity of network, the distribution of network traffic flow reaches a stable equilibrium and the utilization rate of the capacity of network is higher without inducing information. When the total amount of road network traffic is over the capacity of network, the distribution of network traffic flow forms a peak balance, the capacity of network can′t be used effectively, and appropriate traffic management measures should be taken.