考虑有限理性和认知更新的日变交通分配模型
Day-to-day traffic assignment model considering travelers' bounded rationality and perception updating

摘要 研究交通流的逐日演化规律对缓解城市交通拥堵有着重要意义.现有日变交通分配模型所考虑的因素具有一定的片面性，不能全面描述实际交通环境下出行者的有限理性和认知更新行为.基于累计前景理论框架，提出考虑出行者有限理性和认知更新的日变交通分配模型，并通过测试网络进行仿真分析.研究发现，在考虑出行者有限理性和认知更新的条件下，路径流量收敛到最终稳定态的速度较快，在本文算例中，路径1~6分别在第40天、40天、11天、13天、22天和23天演化到稳定点；出行者对路径累积前景值认知的敏感性参数η对路径流量的逐日演化收敛速度也有着显著的影响，较小的η值意味着需要更长的时间，使得网络上的流量分布演化到最终平衡态.
Abstract：The study on the characteristics of traffic flow evolution is of great importance to alleviating urban traffic congestion. The existing day-to-day traffic assignment models have some constrains, which could not fully describe the behaviors of bounded rationality and perception updating in the actual traffic environment. This paper proposes a day-to-day traffic assignment model considering travelers’ bounded rationality and perception updating based on the cumulative prospect theory, and does a simulation experiment in a test network. The study finds that in the condition of the travelers’ bounded rationality and perception updating, the flow convergence speed to the steady state is faster, in this example, the path from 1 to 6 needs 40 d, 40 d, 11 d, 13 d, 22 d and 23 d to the stable state, respectively. The parameter η, which describes travelers’ perception sensitivity on the cumulative prospect value of the path, also has significant effects on the evolution convergence rate, the smaller η means the longer time to the final equilibrium state.