基于深度学习的城市快速路交通运行状态判别与预测  

Traffic Operation State Discrimination and Prediction of Urban Expressway Based on Deep Learning

作　　者：赵彪 刘晓锋[1] 付甜 ZHAO Biao;LIU Xiaofeng;FU Tian(Tianjin University of Technology and Education,Tianjin 300222,China)

机构地区：[1]天津职业技术师范大学,天津300222

出　　处：《现代信息科技》2023年第1期1-7,13,共8页Modern Information Technology

基　　金：天津市科技计划项目(22YDTPJC00120,XC202028)。

摘　　要：为了实时判断城市交通及快速路交通状况,并充分考虑到快速路及交通系统的模糊性、差异性、波动性,文章首先给出了一套基于模糊C均值聚类法(Fuzzy C-Means)的快速路交通状况判断算法,将道路的交通状况区分为通畅、基本顺畅、缓行、堵塞4类。其次,利用了长短期记忆网络(LSTM)的长短时预测技术,给出了一个基于深度学习的模式,对交通运行三参数进行预测,并利用路侧检测设备不断获取新的真实数据,对预测模型进行迭代训练。然后利用已形成的模糊聚类模型,得到快速路交通运行状态的预测结果。最后以天津市黑牛城道的真实交通数据进行实验验证,结果表明,文章提出的交通流状态预测模型准确性达到93.22%,能为快速路交通控制提供有效的状态预测。In order to judge the urban traffic and traffic condition of expressway in real time,and to fully consider the fuzziness,variability,and fluctuation of expressway and traffic system,a set of judgment algorithms of expressway traffic condition based on Fuzzy C-Means is given in this paper firstly,which distinguishes the traffic conditions of roads into four categories:smooth,basically smooth,slow and blocked.Secondly,a model based on deep learning is given to predict the three parameters of traffic operation by using the Long Short-Term Memory(LSTM)long short-term prediction technique,and the prediction model is trained iteratively by continuously acquiring new real data using roadside detection equipments.Then using the developed fuzzy clustering model,the prediction results of the traffic operation state of the expressway are obtained.Finally,the real traffic data of Heiniu Road in Tianjin is used for experimental validation,and the results show that the accuracy of the traffic flow state prediction model proposed in this paper reach 93.22%,which can provide effective state prediction for expressway traffic control.

关 键 词：城市快速路 模糊C均值聚类 深度学习 LSTM短时预测方法 交通运行三参数 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]