行车时间估计与路径寻优问题

The Problem of Traveling time Estimate and Optimal Routing

通过公路上安装的传感器采集数据,从而估算出车辆的行驶时间并求取最优路径,可以方便人们的出行.根据某段公路上传感器提供的数据分析了该段公路的交通状况,建立微分方程模型和关联度分析模型分析交通状况特征及相互影响,并利用ARIMA模型对速度进行了预测.通过对交通干线图进行分析,在假定各路段上的运行时间为独立的随机变量、考虑路段间的相互影响和根据给定的条件这三种情况下,分别建立模型用于估计跟路段通过时间和寻找最优路径,求解得到理想的结果.所建立模型有较强的实用性,有一定的参考作用.

Detectors mounted on the highway can help travelers to estimate the traveling time and select an optimal route, and it will make people more convenient to go out. In this paper, the traffic condition is analyzed with the data detected by the detectors on the highway, and both differential equation model and corre- lation analysis model are set up to analyze the traveling characteristics and their mutual influence, then ARIMA model is used to forecast the speed. Based on deep analyzing of the map, three models have been separately put forward to estimate traveling time and optimal routing, on three conditions of supposing that link travel times are mutually independent random variables, considering the correlation of the links, and knowing the terms been given. Finally, the ideal result is obtained, and it shows that these models can be easily used and have some practical guidance.