Traffic Flow Prediction: An Intelligent Scheme for Forecasting Traffic Flow Using Air Pollution Data in Smart Cities with Bagging Ensemble

Sustainability

Ejaz Ahmed¹  Nabeel Asghar²  Munam Ali Shah³  Noor Ullah Khan³  Carsten Maple⁴ 

[1] Computer Science Department, National University of Computer and Emerging Sciences (NUCES-FAST), Islamabad 44000, Pakistan;Department of Computer Science, Bahauddin Zakariya University, Multan 60000, Pakistan;Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan;Warwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, UK;

关键词: bagging;    ensemble;    traffic prediction;    air pollution;    traffic forecast machine learning;    air pollution;   

DOI  :  10.3390/su14074164

来源: DOAJ

【 摘 要 】

Traffic flow prediction is the most critical part of any traffic management system in a smart city. It can help a driver to pick the most optimized way to their target destination. Air pollution data are often connected with traffic congestion and there exists plenty of research on the connection between air pollution and traffic congestion using different machine learning approaches. A scheme for efficiently predicting traffic flow using ensemble techniques such as bagging and air pollution has not yet been introduced. Therefore, there is a need for a more accurate traffic flow prediction system for the smart cities. The aim of this research is to forecast traffic flow using pollution data. The contribution is twofold: Firstly, a comparison has been made using different simple regression techniques to find out the best-performing model. Secondly, bagging and stacking ensemble techniques have been used to find out the most accurate model of the two comparisons. The results show that the K-Nearest Neighbors (KNN) bagging ensemble provides far better results than all the other regression models used in this study. The experimental results show that the KNN bagging ensemble model reduces the error rate in predicting the traffic congestion by more than 30%.

【 授权许可】

Unknown