MRGM: An Adaptive Mechanism for Congestion Control in Smart Vehicular Network

Gurpreet Singh Shahi, Ranbir Singh Batth, Simon Egerton

2020Open MIND16 citationsDOI

Abstract

© 2020, Kohat University of Science and Technology. Traffic flow on roads has increased manifolds from past few decades due to increase in number of vehicles and rise in population. With fixed road infrastructure and more vehicles on traffic routes lead to traffic congestion conditions especially in urban areas of developing nations. Traffic jams are normal in major cities which ultimately cause delay in travel time, more fuel consumption and more pollution. This manuscript propose a Multi-metric road guidance mechanism (MRGM) which considers multiple metrics to analyze the traffic congestion conditions and based on the conditions effective optimal routes are suggested to the vehicles. The Simulation of the proposed mechanism is performed with the SUMO by using the python script and the results show that proposed mechanism i.e. MRGM outperforms other mechanism in terms of traffic efficiency, travel time, fuel consumption and pollution levels in the smart vehicular network.

Topics & Concepts

Traffic congestionFuel efficiencyTransport engineeringComputer scienceTraffic congestion reconstruction with Kerner's three-phase theoryTraffic bottleneckTraffic flow (computer networking)PopulationTraffic optimizationFloating car dataEngineeringComputer networkAutomotive engineeringDemographySociologyTraffic control and managementVehicular Ad Hoc Networks (VANETs)Traffic Prediction and Management Techniques