Preemptive MACO (MACO-P) Algorithm for Reducing Travel Time in VANETs

Abstract

ABSTRACTVehicular Ad-hoc NETworks (VANETs) are extremely flexible and dynamic ad-hoc networks that are used to provide smooth, safe and comfortable journey to commuters. The commuters spend significant time of their journey either for their turn to cross the intersections or in the congestion on the path. For reducing the total travel time, it is essential to minimize the waiting time at intersections and find best/optimal congestion-free paths for smoother movement of vehicular traffic on roads. A novel Preemptive Modified Ant Colony Optimization (MACO-P) algorithm has been proposed in this paper for reducing the total travel time. The Modified Ant Colony Optimization (MACO) algorithm is used in literature to avoid the congested path by sensing the pheromone trail. Adding preemption to the existing MACO algorithm will result in the reduction of the average queue length at intersections, meaning thereby, less waiting time ensuring smooth mobility of vehicles. For implementation, various open source softwares, i.e., OSM, Simulation of Urban MObility (SUMO), MObility model generator for VEhicular networks (MOVE), Python and Traffic Control Interface (TraCI) are being used. Real-time maps are being fetched by OSM. Traffic simulation is done using SUMO and the Mobility model is generated through MOVE. Python is used for writing client scripts that initiate and control the simulation, with traffic control interface provided by TraCI. Experimental results confirm that the the total travel time is reduced using the proposed MACO-P algorithm, resulting in significant reduction in consumption of fuel.