Decision fusion in vehicular sensor networks for intelligent traffic management

Road traffic management is an important parameter which affects quality of life. Optimization of road traffic flow would bring considerable and multi aspect gain in day today life. To manage the traffic, we need to know the density of traffic in each area. The identification of traffic zones can be done by the vehicle itself and communicate to the internet in order to reduce the cost of traffic management system. In this light, this paper presents a traffic zone identification system that can be applied for dynamic real time traffic management. The conventional methodology is to make intelligent road infrastructure which incurs capital and operational expenses for the state. If we make the vehicle intelligent and provide minimal signalling patterns in the road traffic systems, it can result in better quality intelligent traffic management system. Here the cost of the intelligent infrastructure gets distributed in the population of vehicle owners. This is an attempt to explore this potential direction. An electronic vehicular sensor network is used in this work which employs decision fusion algorithms to make intelligent decisions for zone identification. Here we demonstrated a combination of Bayesian statistical approaches and decision fusion algorithms in the current frame work. This novel strategy can be utilized to build smarter and futuristic intelligent traffic management systems.