摘要
为了更准确地预测从出发到目的地的出行时间,选择一个有代表性的路段进行数据收集和分析。首先将交通流状态分为拥堵、阻塞和平滑三大类,然后利用神经网络理论建立交通流特征参数和出行时间模型。经过两种神经网络模型的运行对比,时间序列的神经网络模型能够更好地预测车辆。旅行时间的准确度可达85%。
In order to predict the travel time from departure to destination more accurately,a representative road section is selected for data collection and analysis.First,the traffic flow state is divided into three categories:congestion,stoppage and smoothing,and then the neural network theory is used to establish traffic flow characteristic parameters and travel time models.After comparing the operation of the two neural network models,the time-series neural network model can better predict vehicles.Accuracy of travel time can reach 85%.
作者
王楷文
李佳骏
千月欣
王永忠
万连成
李朝潞
徐天羿
WANG Kai-wen;LI Jia-jun;QIAN Yue-xin;WANG Yong-zhong;WAN Lian-cheng;LI Zhao-lu;XU Tian-yi(Civil Aviation Flight University of China,Guanghan 618307,China)
出处
《价值工程》
2021年第8期220-221,共2页
Value Engineering
基金
四川省2020年大学生创新创业项目(S202010624131)。