基于宏观交通流模型的行程时间预测

Travel time prediction based on macroscopic traffic flow models

以高速道路为研究对象,概述了宏观交通模型的基本方程,提出了一种基于宏观交通模拟的路径行程时间预测方法.预测方法以宏观模型输出的速度为基础计算路径行程时间,能够考虑交通拥堵以确保行程时间预测结果更合理.预测方法包含一般计算方法、由路段行程时间合成的方法(近邻组合法)这两种计算方法,后者用以加快计算速度,确保预测能够适用于较大规模路网.算例分析说明了预测方法的有效性.

Individual travel time is key paraeter for driver information systems and rerouting strategies. The paper presents a macroscopic traffic model based travel time prediction method for freeway networks. The method has three main features： travel time calculation is based on the time-dependent speed profile provided by macroscopic model, congestion （caused by bottlenecks or incidents） is properly considered in deriving related equations, the method includes two components, the general calculation approach and the ＂neighbor-combination＂ approach, the latter of which aims to improve the computational efficiency for large-scale network applications. A simulation test example is given to show the efficiency of the proposed method. The paper＇s work can support the deployment of driver information system by the transport management agencies.