出行者有限理性下的逐日路径选择行为和网络交通流演化

Travelers' day-to-day route-choice behavior and evolution of network traffic flow based on bounded rationality

假设交通网络中同时存在完全理性和有限理性2类出行者,他们在日常出行中获取到的信息有2种情况:完全历史信息和不完全历史信息,根据出行者的逐日路径选择过程,建立2种信息条件下的理解行程时间及其更新模型、有限理性模型和路径选择模型。通过数值试验研究,结果表明:完全信息下,系统演化能否达到均衡状态与出行者对信息的依赖程度、出行者的非理性程度,以及理性出行者所占比例均有关系;不完全信息下,系统演化总能达到均衡状态;只有当出行者对信息的依赖程度较大或处于中间水平、出行者的非理性程度较大,且有限理性出行者的比例较高时,完全历史信息的系统效率才稍优于不完全历史信息,其他情况不完全历史信息优于完全历史信息。研究结果可为交通信息发布系统的应用提供参考。

It was assumed that there were two types of travelers in the network, that is, completely rational and houndedly rational travelers. Two kinds of information in the day-to-day travel may be obtained： complete historical information and incomplete historical information. According to travelers＇ day-to-day route-choice process, travelers＂ perceived travel time, its updating method, bounded rationality model and route-choice model were developed. Numerical experiments on a grid network show that whether the system evolution under complete historical information can reach equilibrium depends on travelers＇ reliance on the information, travelers＇bounded rationality level and the percentage of completely rational travelers. However, the system evolution under incomplete information can always reach equilibrium. If travelers rely on information greatly or in an intermediate degree, travelers may be mainly in bounded rationality and the percentage of boundedly rational travelers is high, the efficiency of system under complete information may be greater than that under incomplete information. Otherwise, the system under incomplete information should be superior to that under complete information. The results can be used as a reference for the application of transportation information release systems.