Traffic light optimization solutions using multimodal, distributed and adaptive approaches

Finding the optimal staging of traffic lights and determining the optimal traffic light cycles is one of the crucial tasks involved in modelling modern traffic scenarios. In this paper, we propose two distinct approaches to find optimal traffic light cycles using Multi-agent Systems (MAS) and Swarm Intelligence (SI) concepts and compare the efficiency of these solutions against a default strategy under heterogeneous traffic regions using same input parameters. The solutions obtained are simulated using SUMO (Simulation of Urban MObility), a well-known microscopic traffic simulator. We have investigated both approaches with two large and heterogeneous metropolitan areas with hundreds of traffic lights located in the cities of Colombo in Sri Lanka and Chennai in India, and our research solutions are shown to obtain optimal traffic light cycles in both scenarios. In comparison with predefined static cycle programs (using SUMO's default traffic light cycle generation algorithm), our solutions achieved quantitative improvements for the two main objectives: reducing the waiting time and the journey time of vehicles significantly and qualitatively improving smooth traffic flow over the regions.