An Enhanced Cloud - Based Vehicular AD-HOC Network And Traffic Control Strategy in Reducing Traffic Congestion

Abstract

Road traffic congestion occurs when the volume of traffic is too close to the maximum capacity of a road network. Traffic congestion always has negative effects on lives and environment such as fuel consumption and air pollution. More so, travel increases as a result of traffic congestion and the situation will continue to deteriorate unless better traffic flow model to provide prediction, such as , travel time prediction, route choice and flow rate. Existing traffic flow models are inadequate in handling mixed traffic stream. This research develops a conceptual framework for traffic control strategy to deliver an improved traffic congestion reduction system is developed. It also looks at rout guiding scheme that can make routing decisions easy for drivers and also looks at how much expansion of road capacity could reduce traffic congestion. The developed traffic control strategies are implemented with VISSIM traffic simulation tool, cloud Sim cloud simulator and JAVA programming language. Percentage of improvement (POI) is used to evaluate the simulation framework. Root mean square Error (RMSE) and Root mean square Normalized Error (RMSNE) are used to validate the simulation model. Route information system is developed for drives to make routing decision. Evaluation results revealed that the proposed road expansion capacity could reduce traffic congestion in an urban area by approximately 11 and 47% when expanded by one lane and two lanes respectively. Also, a free flow is achieved by approximately 54% and 34.5% when drives are guided with rout information over traffic in non-signalized traffic steam and traffic control with traffic signal respectively.