A real-time traffic congestion-avoidance framework for smarter cities

• Main Authors: Thejaswini, R. S. Saundharya, Rajaraajeswari, S.
• Format: Conference Proceeding
• Language: English
• Subjects: Analytics; Cloud computing; Data management; Edge computing; Fog; Machine learning; Real time; Traffic congestion; Traffic management
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/1.5078968

Traffic management is turning out to be an important component in establishing and sustaining smarter cities across the globe. With the vehicle population is consistently on the climb, the traffic management is becoming hugely complicated affair while maintaining the connectivity infrastructure utilization. IT solution and service organizations have come forth with a number of automated traffic management solutions and the primary problem with them is they are unfortunately reactive and hence is an inefficient solution for the increasingly connected cities. Therefore unveiling real-time, adaptive, precision-centric and predictive traffic monitoring, measurement, management and enhancement solutions are being insisted as an indispensable requirement towards smart and sustainable cities. This domain of study and research has been acquiring a lot of momentum these days and considering its growing significance for the human society, we have proposed an innovative approach and framework for smarter traffic management. This is being made possible with the faster maturity and stability of multiple technologies such as the IoT, fog/edge computing, big data analytics, software-defined cloud environments, 5G communications, and AI (machine and deep learning methods).