A novel digital twin-centric approach for driver intention prediction and traffic congestion avoidance

Abstract

Road traffic has been exponentially growing with surging people and vehicle population. Road connectivity infrastructure has not been growing correspondingly and hence the research endeavors for optimal resource allocation and utilization of connectivity resources has gained a lot these days. Therefore, insights-driven real-time traffic management is turning out to be an important component in establishing and sustaining smarter cities across the globe. 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 an inefficient solution for the increasingly connected and dynamic city environments. Therefore, unveiling real-time, adaptive, precision-centric and predictive traffic monitoring, measurement, management and enhancement solutions are being insisted as an indispensable requirement toward sustainable cities. We have come out with a novel approach leveraging a few potential and promising technologies and tools such as a reliable and reusable virtual model for vehicles, a machine learning model, the IoT fog or edge data analytics, a data lake for traffic and vehicle data on public cloud environments, and 5G communication. The paper details all these in a cogent fashion and how these technological advancements come handy in avoiding the frequent traffic congestions and snarls due to various reasons.