面向智能交通领域节能减排以及高品质服务的需求，以早晚高峰时车辆路径规划及路网资源的优化使用为研究对象，遵循“建模、仿真与实证相结合，个体和群组相结合”的技术路线，在已有路网模型及osmnx包的支持下，建立基于聚类算法的分层路网模型。运用时间地理理论，确定早晚高峰车辆出行特征。以基于聚类算法分层路网模型的资源功能层为基础，依据早晚高峰车辆出行特征，定量分析多种驾驶行为对路网资源使用情况的影响，建立模糊合作群体，提出基于博弈理论的道路资源竞争与协作机制。在车辆协作的基础上，改进蚁群算法进行群组车辆路径规划，做到群体路径规划，使得道路达到最佳的利用率。本项工作摈弃了独个车辆的路径规划，在关注有效利用道路资源的同时，更强调时间、空间两种资源分配的公平性，进行群组车辆的协同路径规划，分流热点路段，达到了路网资源的整体使用最优。

For the requirements of energy saving, emission reduction and high-quality service in the intelligent transportation field, the research object are vehicle path planning and optimal use of road network resources during morning and evening rush hours. The research route follows the combination of modeling, simulation and empirical, and considers the combination of individual and group. With the support of the existing road-network model and osmnx package, a hierarchical road-network model based on clustering algorithm is established. Using time geography theory, trip Characteristics of morning and evening rush hours are proposed. Based on the resource function layer of hierarchical road-network model and the trip Characteristics, the impact of multiple driving behaviors on road network resources is quantitatively analyzed. Fuzzy cooperation group was established. A road resource competition and cooperation mechanism based on game theory is proposed. On the basis of vehicle cooperation, the ant colony algorithm is improved for group vehicle path planning. A group path planning strategy is obtained to achieve optimal utilization of the road. The project will abandon the defects of single vehicle path planning. While paying attention to the effective use of road resources, it will emphasize the fairness of the distribution of resources in both time and space. The coordinative path planning will be carried out, shunting hotspot, and the overall use of the road network resources will be optimal.