Vehicle Congestion Estimation Using a Cooperative Infrastructure for Traffic Information Acquisition

Congestion and delays for travelers are caused by increased vehicular traffic on city roads. This is owing to a growth in the number of vehicles on the road, as well as due to the insufficient resources to monitor and regulate them. Any application that contributes to solving this issue requires efficient and comprehensive source of traffic information. This paper proposes a Co-Operative Infrastructure (CoopI) that estimates the Rate of Congestion (RaCon) for any target geographical area. CooPI combines traffic data from vision and Vehicular Adhoc Network (VANET)-based environments respectively for RaCon estimations. Using fuzzy logic, the parameter values obtained from the CoopI are used in estimating RaCon for vehicles approaching towards the target geographic area. The RaCon estimations are distributed to travelers or applications in order to avoid overcrowding and make adaptive travelling decision. The performance of CoopI is evaluated for RaCon obtained with parameters from traffic videos using fuzzy logic. The combined effect of the infrastructure contributes an improvement with maximum of 45% in determining RaCon which improves the efficiency and accuracy of application utilizing the same.