Traffic lights timing inside microregion simulator using multiobjective optimization

Costa, Breno C.; Almeida, Paulo E. M.; Caldeira, Evandro

doi:10.1109/cec.2011.5949777

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Costa et al (2011) employed the NSGA2 algorithm to optimize traffic signal optimization in an urban network. Two objective functions, evaluated based on green times, were considered: to maximize the number of vehicles leaving the network and to minimize vehicle travel times.…”

Section: Traffic Signal Optimizationmentioning

confidence: 99%

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Fixed-time traffic signal optimization using a multi-objective evolutionary algorithm and microsimulation of urban networks

Costa

Leal

Almeida

et al. 2016

Transactions of the Institute of Measurement and Control

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Large cities have been facing serious problems in the management of traffic, owing to the increasing number of vehicles and pedestrians. Traffic engineering is essential in managing traffic and improving urban mobility. This paper deals with the problem of fixed-time signal programming on traffic networks. A new bi-objective optimization model is proposed to maximize the average and minimize the variance of the vehicle speeds in the network. Although the first function is commonly discussed in the literature, the second one is novel, and its aim is to provide flow balance along the network. This combination of functions is optimized by the Memory-Based Variable-Length Nondominated Sorting Genetic Algorithm 2 (MBVL-NSGA2), which avoids the revaluation of candidate solutions. This approach was validated through experiments using the microscopic simulator GISSIM, in a multi-intersection real network, using measured data from Belo Horizonte traffic engineering company (BHTRANS). The practical results of MBVL-NSGA2 were compared with four approaches: (1) current BHTRANS solutions; (2) a genetic algorithm optimizing the first function; (3) a genetic algorithm optimizing the second function, and; (4) the traditional NSGA2. Analysis showed that this proposal is able to generate better traffic signal plans, at the same time that it generates a diversified set of efficient candidate solutions.

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Section: Traffic Signal Optimizationmentioning

confidence: 99%

“…After testing other combinations of functions, such as maximizing the number of vehicles leaving the network and minimizing travel time (Costa et al, 2011), it was noticed that this combination leads to better results, taking real networks into account.…”

Section: The Proposed Approachmentioning

confidence: 99%