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行车时间估计和最优路径选择是现在交通系统中研究的热点。通过在高速公路路段安装测速器采集的信息,可以估计行车时间及推求最优路线。根据某段公路上测速器提供的数据,分析该路段的交通情况,在此基础上我们提出了平均速度模型,加速度模型,通过实测数据分析得到的车流量信息优化模型提出了速度流量模型,解决了车辆在任意时间t经过测速器,通过第五个测速器所需的时间,同时利用提出的模型又从不同的方面给出了交通堵塞的衡量标准,并利用神经网络方法对未来的车流状态进行了预测。
Traveling time estimation and optimal path selection are the hot topics in traffic system. By installing the information collected by the tachometer on the highway section, it is possible to estimate the travel time and to find the optimal route. Based on the data provided by the tachometer on a certain section of highway, we analyzed the traffic condition of the section. On this basis, we put forward the average velocity model and acceleration model, and put forward the velocity flow model based on the traffic flow information optimization model obtained from the measured data. The time required for the vehicle to pass the tachometer and the fifth tachometer at any time t is given. At the same time, with the proposed model, the measure of traffic jams is given in different aspects and the neural network method is used to predict the future traffic flow state Made a prediction.