Smart real-time traffic congestion estimation and clustering technique for urban vehicular roads

Road-traffic congestion is becoming a serious concern in developing countries and impacts the economy of countries gravely. Increasing congestion on urban roads presents a genuine threat to the economic growth and livability of city regions. Most traffic congestions are caused due to unplanned road networks, high volumes of vehicles and presence of critical congestion areas. Traffic congestions not only pose a threat to the economy but also to the environment. Spillover effect from congested main roads to secondary roads and side streets as alternative routes often leads to more congestion; increasing the chances of collisions and accidents due to tight spacing and constant stopping-and-going. The following paper presents a smart congestion avoidance technique by estimating the scope of real-time traffic congestion on urban road networks and predicts an alternate shortest route to the destination. The proposed system uses K-Means Clustering Algorithm to estimate the magnitude of congestion on different roads and then employs Dijkstra's Algorithm to predict the shortest route. Once the user inputs the destination into the system, the system predicts the shortest route from the user's current location. The process is reiterated at every intersection until user reaches the destination.