Towards greener smart cities and road traffic forecasting using air pollution data. (September 2021)

• Record Type: Journal Article
• Authors: Shahid, Nimra; Shah, Munam Ali; Khan, Abid; Maple, Carsten; Jeon, Gwanggil
• Is Part Of: Sustainable cities and society. Volume 72(2021)
• Publication Date: 2021-09
• Subjects: Traffic forecast -- Machine learning -- Ensemble -- Air pollution -- Regression models; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2021.103062
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: Road traffic flow forecasting is among the most important use case associated with smart cities. Traffic forecasting allows drivers to select the fastest route towards their target destinations. A prerequisite for traffic flow management is accurate traffic forecasting. We introduce a framework for traffic forecasting that uses data on air pollution. We performed a comparative analysis of 7 different regression models. Abstract: Road traffic flow forecasting is among the most important use case associated with smart cities. Traffic forecasting allows drivers to select the fastest route towards their target destinations. A prerequisite for traffic flow management is accurate traffic forecasting. In this study, we introduce a framework for traffic forecasting that uses data on air pollution. The reason to select that data is air pollution rates are often associated with traffic congestion, and there is intensive research that exists to forecast air pollution by road traffic. To the best of our knowledge, any effort to enhance road traffic prediction through air quality and ensemble regression model techniques is not yet been proposed. In this research, our contribution is twofold. Firstly, we have performed a comparative analysis of 7 different regression models to find out which model gives better accuracy. Secondly, we propose a framework using regression models in which the first regression model's result is boosted using boosting ensemble method and is passed to the next regression model which shows that the proposed framework gives more satisfying results than the above 7 regression models. The experimental findings show the effectiveness of the proposed framework which decreases the error rate by 2.47 %.