文章基本信息

标题：Traffic Congestion Prediction using Decision Tree, Logistic Regression and Neural Networks
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作者：Tariku Sinshaw Tamir ; Gang Xiong ; Zhishuai Li 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：5
页码：512-517
DOI：10.1016/j.ifacol.2021.04.138
语种：English
出版社：Elsevier
摘要：AbstractTraffic congestion is a serious problem around the world and to a great extent influences urban communities in various manners including increased stress levels, delayed deliveries, fuel wastage, and monetary losses. Therefore, an accurate congestion prediction algorithm to limit these misfortunes is fundamental. This paper presents a comparative study of traffic congestion prediction systems including decision tree, logistic regression, and neural networks. Five days of traffic information (1,231,200 samples) are utilized to drive the prediction model. The TensorFlow and the Clementine machine learning platforms are used for data preprocessing, training, and testing of the model. The confusion matrix clears that decision tree has better prediction performance and leads the other two methods with accuracy (97%), macro-average precision (95%), macro-average recall (96%), and macro-average F1_score (96%) in the python programming environment. Moreover, performance of the three prediction models is verified in Clementine environment and decision tree outperforms all other models with an accuracy of 97.65%.
关键词：KeywordsConfusion matrixData preprocessingPythonTensorFlowClementine environmentITS