基于Stackelberg博弈的混合路网协同管控模型

Integrated Control and Management Model for Mixed Networks Based on Stackelberg Games

摘要:

针对快速路网和普通道路网所组成的混合路网由于非集中化协同管控而导致的局部拥堵易演变为大规模拥堵现象，以及高峰时段内路网流量压力分配不均衡问题，基于Stackelberg博弈过程构建了管理者与用户间对策角色可变的混合路网集中化协同优化模型LC-MPC（可变角色模型预测控制）。将用户服从率作为表征路段流量压力分配的均衡指标，设计角色交换函数实现不同交通状态下领导与跟随关系的转换，并通过模型预测控制（MPC）的中央控制器实现了对混合路网CAC（交通分配与信号控制的组合问题）的同步优化求解，最后与不同控制模型对照以验证设计模型的有效性。分析结果显示，结构集中化程度最高的LC-MPC模型较之其他对照模型在降低路网出行成本上有更好的效果；同时，该模型能将用户服从率稳定在一定范围内，有利于路网流量均衡分配及整体运行稳定。

Abstract:

Aimed at the phenomenon that local congestion is easy to evolve into large-scale congestion due to the decentralized coordinated control of the mixed network composed of the expressways and the surface streets， as well as the unbalanced distribution of traffic flow pressure in the road network during peak hours， a centralized cooperative optimization model LC-MPC（level change - model predictive control） of mixed network with changeable game roles between authority and users is constructed based on Stackelberg Games. User compliance rate is taken as the equilibrium index to represent the pressure distribution of road traffic flow， while role-change function is designed to fulfill the transformation of the relationship between leader and follower under different traffic states. The central controller of model predictive control（MPC） realizes the synchronous optimization solution of CAC （combined traffic assignment - signal control problem） of the mixed network. Finally， the effectiveness of the designed model is verified by comparing with different control models. The results show that the LC-MPC model with the highest degree of centralization has a better effect on reducing road network travel cost than other control models. Meanwhile， the model can maintain the user compliance rate within a certain range， which is conducive to the balanced distribution of traffic flow and the overall stability of the road network.