Short-Term Traffic Prediction Model (STTPM)

Many metropolitan cities are encountering traffic congestion as a result of rapid population growth, which has made vehicles a necessity. In India, traffic congestion is escalating at an uncontrolled rate. In many cities, Big Data Analytics is gaining traction as part of a Smart Intelligent Traffic Management System. Predicting traffic flow in states with abnormally high levels of traffic in metropolitan areas is necessary to alleviate traffic jams and improve traffic situations. In one of India’s cities, we propose a Short-Term Traffic Prediction Model (STTPM) using an energy-efficient traffic flow pattern. In order to anticipate short-term traffic flow, the relationship between the current segment and upstream stations is evaluated. Freeway toll data can be used to construct a traffic flow structure pattern. A prediction approach based on LWL and regression was presented using this structure pattern. This is used to forecast short-term traffic flow in high-vehicle-traffic states and during holidays. This will aid in the prediction of traffic congestion, which will benefit travelers by allowing them to reroute and save fuel. Green priority and energy conservation are also promoted by the proposed strategy.