A novel approach for vehicle specific road/traffic congestion

Abstract

Large population, poor road infrastructure, and rapidly growing economies lead to severe tra c congestion in many parts of the world. The problem is exacerbated by increased diversity in vehicle types and poor adherence to lane discipline. Existing approaches for detecting tra c congestion do not deal with the diversity of vehicle types and lack of lane discipline. We propose a novel approach to detect congestion levels as per on the vehicle type. We present a thresholdbased heuristic and an improved classi cation algorithm to infer the main regions of high tra c in a city. We do transportation mode classi cation from the real time crowd-sourced smartphone sensor data followed by congestion level detection. Our approach can be useful in congestion management by suggesting optimal alternate route as per the vehicle type, thus saving travel time and reducing fuel consumption and emission reduction. The proposed congestion detection system was able to predict more than 80% major hot-spots of tra c classi ed as per di erent vehicle types.