摘要
随着城市化进程的加快,我国城市机动车数量快速增加,使得现有路网容量难以满足交通运输需求,交通拥堵、环境污染、交通事故等问题与日俱增。准确高效的交通流预测作为智能交通系统的核心,能够有效解决交通出行和管理方面的问题。现有的短时交通流预测研究往往基于浅层的模型方法,不能充分反映交通流特性。文中针对复杂的交通网络结构,提出了一种基于DCGRU-RF(Diffusion Convolutional Gated Recurrent Unit-Random Forest)模型的短时交通流预测方法。首先,使用DCGRU(Diffusion Convolutional Gated Recurrent Unit)网络刻画交通流时间序列数据中的时空相关性特征;在获取数据中的依赖关系和潜在特征后,选择RF(Random Forest)模型作为预测器,以抽取的特征为基础构建非线性预测模型,得出最终的预测结果。实验以两条城市道路中的38个检测器为实验对象,选取了5周工作日的交通流数据,并将所提方法与其他常见交通流量预测模型进行比较。结果表明,DCGRU-RF模型能够进一步提高预测精度,准确度可达95%。
With the acceleration of urbanization,the number of motor vehicles in cities in China is increasing rapidly,which makes the existing road network capacity difficult to meet the transportation needs,traffic congestion,environmental pollution and traffic accidents are increasing day by day.Accurate and efficient traffic flow prediction,as the core of ITS,can effectively solve the problems of traffic travel and management.The existing short-term traffic flow prediction researches mainly use the shallow mo-del method,so they cannot fully reflect the traffic flow characteristics.Therefore,this paper proposed a short-term traffic flow prediction method based on DCGRU-RF model for complex traffic network structure.The DCGRU network is used to characte-rize the spatio-temporal correlation features in the traffic flow time series data.After obtaining the dependencies and potential features in the data,the RF model is selected as the predictor,and the nonlinear prediction model is constructed based on the extracted features,and finally getting the prediction result.In this experiment,38 detectors in two urban roads were selected as experimental objects,traffic flow data of five working days were selected,and the proposed model was compared with other common traffic flow prediction models.The experimental results show that DCGRU-RF model can further improve the prediction accuracy,the accuracy can reach 95%.
作者
熊亭
戚湧
张伟斌
XIONG Ting;QI Yong;ZHANG Wei-bin(School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China;School of Electronic and Optical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)
出处
《计算机科学》
CSCD
北大核心
2020年第5期84-89,共6页
Computer Science
基金
国家重点研发计划政府间国际科技创新合作重点专项(2016YFE0108000)
江苏省重点研发计划(产业前瞻与共性关键技术)项目(BE2017163)
江苏省交通运输科技与成果转化项目(2018Y51)。
关键词
交通流量预测
组合预测模型
深度学习
路网
Traffic flow forecast
Combined forecasting model
Deep learning
Road network
