A preemptive approach to reduce average queue length in VANETs

This paper proposes a novel preemptive algorithm for optimization of traffic signals in VANETs to reduce large queuing at crossroads by allowing smooth movement of traffic on the roads without much waiting. The proposed algorithm selects green light timings according to real time vehicular density and can select any phase out of predefined order depending on the traffic density on that phase to reduce the congestion. It also ensures that no vehicle halts on the crossroad for more than a threshold limit. Implementation of the algorithm is done using the open source tools: Open Street Map (OSM), Simulation of Urban MObility (SUMO), MObility model generator for VEhicular networks (MOVE), Traffic Control Interface (TraCI) and python scripts. The accuracy of the proposed algorithm is validated both graphically and statistically using hypothesis testing by comparing it with non-preemptive adaptive traffic signal control and the pre-timed approach. The comparisons are performed by simulation on three types of networks: simple simulated network, complex simulated network and Real Time University of Delhi map. Results illustrate the efficiency of the proposed approach in all the three networks with reduced average queue length at the crossroads with 95% confidence.