Traffic signal optimization in "La Almozara" district in Saragossa under congestion conditions, using genetic algorithms, traffic microsimulation, and cluster computing
IEEE Transactions on Intelligent Transportation Systems(2010)
摘要
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-instruction-multiple-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 network--from 36 up to 3600 vehicles per hour--we get better performance from our architecture. Finally, we present new research directions in this area.
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关键词
traffic network,congestion condition,cluster computing,new model,traffic signal optimization,new research direction,la almozara,optimization task,large-scale real-world test case,urban traffic congestion,extreme case,congestion situation,traffic microsimulation,better performance,genetic algorithms,fitness function,cellular automata,intelligent transportation systems,automata,statistics,testing,genetic algorithm
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