ATSOT: Adaptive traffic signal using mOTes

Abstract

This paper presents design and development of Adaptive Traffic Signal using mOTes (ATSOT) system for crossroads to reduce the average waiting time in order to help the commuter drive smoother and faster. Motes are used in the proposed system to collect and store the data. This paper proposes an adaptive algorithm to select green light timings for crossroads in real time environment using clustering algorithm for VANETs. Clustering algorithms are used in VANETs to reduce message transfer, increase the connectivity and provide secure communication among vehicles. Direction and position of vehicles is used in literature for clustering. In this paper, difference in the speed of vehicles is also considered along with direction, node degree, and position to create reasonably stable clusters. A mechanism to check the suitability of cluster initiator is also proposed in the paper. The proposed ATSOT system can be used for hassle free movement of vehicles across the crossroads. Prototype of the system has been designed and developed using open source software tools: MOVE for the Mobility model generation, SUMO for traffic simulation, TraCI for traffic control Interface and Python for client scripting to initiate and control the simulation. Results obtained by simulating ATSOT approach are compared with both OAF algorithm for adaptive traffic signal control and pre-timed approach to show the efficiency in terms of reduced waiting timing at the crossroads. Results are also compared with pre-timed method for single lane and multi-lane environment using Webster’s delay function.