Machine Learning Techniques for Sustainable Smart Cities Traffic Management

Abstract

The issue of traffic congestion is one of the problems that disrupt the smoothness and comfort of the community in carrying out daily affairs. Rapid development, with an expanding population and vehicles in cities, has caused traffic congestion to become one of the most pressing concerns. As a result, this study proposed vehicle tracking method for traffic system management to improve and keep up with the rising demand for better traffic on the road. In the proposed system, cameras were placed at traffic intersections and along the road or highway to gather real-time traffic data. Image processing and machine learning algorithm were applied to calculate the number of vehicles based on the traffic flow and vehicle speed on the road to reflect the current traffic condition so that it helps general public to plan their trip and less pollution is produced in long run. This study focused on the cities of Cyberjaya, Putrajaya and selected Klang Valley areas to study the traffic condition during the peak and off-peak hour. Experimental results showed that the proposed machine learning solution provided high accuracy and effective detection on real-time traffic congestion. Current method used variables such as vehicle speed and number of vehicles to calculate the traffic flow on the road. As future direction, this method can be expanded to incorporate vehicle density as one of the variables for more precise traffic congestion reporting.