Traffic signal optimization in la Almozara District in Saragossa under congestion conditions, using genetic algorithms, traffic microsimulation, and cluster computing

Abstract

Urban traffic congestion is a pandemic illness affecting many cities around the world. We have developed and tested a new model for traffic signal optimization based on the combination of three key techniques: 1) genetic algorithms (GAs) for the optimization task; 2) cellular-automata-based microsimulators for evaluating every possible solution for traffic-light programming times; and 3) a Beowulf Cluster, which is a multiple- instructionmultiple-data (MIMD) multicomputer of excellent price/performance ratio. This paper presents the results of applying this architecture to a large-scale real-world test case in a congestion situation, using four different variables as fitness function of the GA. We have simulated a set of congested scenarios for La Almozara in Saragossa, Spain. Our results in this extreme case are encouraging: As we increase the incoming volume of vehicles entering the traffic networkfrom 36 up to 3600 vehicles per hourwe get better performance from our architecture. Finally, we present new research directions in this area.