Abstract: With rapid growth in personal luxury and increasing jobs, People are comfortable using their personal vehicles rather than public transport to fulfill their transportation needs. This is because of ease of access and feasibility to use the vehicles at their own will at any point of time. It is leading to heavy traffic congestions and long waiting periods at traffic signals which is becoming a heavy burden in all major cities and will be affecting environment because of pollution caused by so many vehicles and also will disturb the individual’s time schedule. This paper proposes a system using data analytics, machine learning algorithms, Internet of things to predict the traffic flow, generate precise data about real time traffic congestions at that instant and rerouting the vehicles using navigation through a less congested path ultimately developing an Intelligent Traffic Management system. The architecture of the system is based on image analysis of vehicles using cameras at signals, using GPS in mobiles to monitor traffic in particular route. The combination of these two can be used to generate useful data about traffic congestions. Next part is calculating the efficient path to reach the destination with the generated data to minimize traffic and reach destination short period of time. The generated efficient route and traffic intensity is updated to the user with the help of maps application. Keywords: data analytics, machine learning, GPS, image analysis, intelligent traffic management system, Internet of things
Retraction notice to ‘Erratum to “Smart Health Monitoring and Management System: Toward autonomous wearable sensing for Internet of Things using Big Data Analytics [Future Gener. Comput. Syst. 91 (2019) 611–619]”’ [Future Gener. Comput. Syst. 108 (2019) 1350–1359]