Dynamic traffic assignment and vehicle route guidance have been important problems in ITS for some time. This paper proposes a new model for VRGS, which takes into consideration of the information propagation, user selection and information reaction. Parameter p is then defined as the updating weight for computing cost of traffic based on a distributive learning scheme. p is calculated through a function which denotes information propagation over time and space and the function needs further optimization. Comparison to static traffic assignment, DTA and feasible strategies are given, and future work is also stated.

翻译：动态交通分配与车辆路径引导一直是智能交通系统（ITS）中的重要问题。本文提出了一种车辆路径引导系统（VRGS）的新模型，该模型综合考虑了信息传播、用户选择和信息反应。随后定义了参数p作为基于分布式学习方案计算交通成本的更新权重。p通过一个表示信息在时间和空间上传播的函数计算得到，且该函数需要进一步优化。本文给出了与静态交通分配、动态交通分配（DTA）及可行策略的对比分析，并陈述了未来工作方向。