摘要
交通流量的预测可以为交通管理部门的工作和车主的出行规划提供很大帮助,如何进行准确且高效的交通流量预测是一个非常重要的问题。传统的交通流量预测数据通常是车速和行车轨迹,研究人员通过在高速上每隔一段距离布置交通传感器获得数据,这些方法应用于城郊地区和高速公路上,取得了很好的效果,但城市道路人口密集且交通情况复杂,不适合大规模布置传感器获得所需交通数据,所以不能使用现有的方法进行预测。笔者提出了一种利用城市道路卡口的交通流量数据进行预测的方法。首先,通过对已有的交通数据分析来总结交通流量周期性变化的特点;然后,基于这些周期性变化的特点来提取相应特征;最后,依据这些特征训练适用于城市卡口的交通流量预测模型。基于真实交通数据集进行了大量实验,结果表明,交通流量预测模型的预测值的RMSE和MAPE分别为15.3和7.3,即预测准确度可以达到92.7%。
The prediction of traffic flow can be greatly useful for the work of traffic management departments and the travel planning of drivers.How to make accurate and efficient traffic flow prediction is a very important issue.Traditional traffic flow prediction data sources are usually vehicle speed and driving trajectory which are obtained by arranging traffic sensors on the highway at regular intervals.Although the existing method applied to suburban areas and highways have achieved good results,it can not be used to make the predictions on dense and complicated urban roads for the inconvenience of large-scale deployment of sensors to obtain the required data.This paper proposed a forecasting method by using traffic flow data of urban road checkpoints.We first got the characteristics of cyclic changes in traffic flow by analyzing existing traffic data.Then we extracted corresponding features based on these cyclic changes.Finally we trained traffic flow prediction models suitable for urban checkpoints based on these features.A large number of experiments have been carried out according to real traffic data sets,and the results show that our traffic flow prediction model has a good prediction effect.With RMSE(15.3)and MAPE(7.3)of the predicted values,the accuracy can reach 92.7%.
作者
李浩
张杉
曹斌
范菁
LI Hao;ZHANG Shan;CAO Bin;FAN Jing(College of Computer Science&Technology,Zhejiang University of Technology,Hangzhou 310023,P.R.China)
出处
《重庆大学学报》
EI
CAS
CSCD
北大核心
2020年第11期29-40,共12页
Journal of Chongqing University
基金
国家重点研发计划资助项目(2018YFB1402800)。
关键词
交通流量预测
周期性变化
特征模型
随机森林
交通卡口
traffic flow forecast
cyclic change
feature model
random forest
traffic intersection
