Traffic signal optimization and coordination using neighborhood mutation

Armas, Rolando; Aguirre, Hernán; Daolio, Fabio; Tanaka, Kiyoshi

doi:10.1109/cec.2016.7743821

Cited by 2 publications

(5 citation statements)

References 14 publications

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“…cycle, offset, or green times) of one signal system S in the solution. The choice of the used mutation operator depends on specific mutation rates following Armas et al works [30]. The rates are defined as P cycle , P green and P offset for cycle time, green times and offset time mutators, respectively.…”

Section: Mutation Operatorsmentioning

confidence: 99%

“…Therefore, we compare the performances of five optimization algorithms: a stochastic hill-climbing algorithm (see Algorithm 2), a simple elitist evolutionary algorithm from [30] and three bandit-based algorithms which differ on the way their arms and rewards are defined (from equation 5). The first bandit algorithm considers the context of Liu's works, which has been outlined in section 3.5; in that respect, there are as many bandit arms as there are signal systems in the road network.…”

Section: Performances Of Optimization Algorithmsmentioning

confidence: 99%

“…One single run considers a budget of 2000 simulator evaluations and requires about 15 hours of computation on a single-threaded machine (see Appendix C). We select four random signal systems for the mutation phases, following Armas et al works [15,30] in a way to ease comparisons and discussion in section 6. For bandit algorithms, the selection strategy determines a group to be mutated, then four signal systems belonging to the latter are mutated.…”

Section: Performances Of Optimization Algorithmsmentioning

confidence: 99%

“…As a starting point for future works, we experience our methodology on Quito city, Ecuador, from [30]. Utilizing the various benchmark scenarios and their analyses (see Appendix A), the importance-based Bandit Descent manages once again to find highquality traffic light settings (see Figure 10).…”

Section: Validation Of the Benchmarkmentioning

confidence: 99%

“…Results presented in section 6.3 rely on the real-world mobility case study of Quito city, Ecuador [9,15,30]. The transport network covers a 40 km 2 area and includes 8192 links and 70 signal systems.…”

Section: Appendix a Experiments On Quito Ecuadormentioning

confidence: 99%

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Fitness landscapes analysis and adaptive algorithms design for traffic lights optimization on SIALAC benchmark

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Fonlupt

Vérel

et al. 2019

Applied Soft Computing

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Finding optimal traffic light timings at road intersections is a mandatory step for urban planners wishing to achieve a sustainable mobility in modern cities. Increasing congestion situations constantly require urbanists to enhance traffic fluidity, while limiting pollutant emissions and vehicle consumption to improve inhabitants' welfare. Various mono or multi-objective optimization methods, such as evolutionary algorithms, fuzzy logic algorithms or even particle swarm optimizations, help to reach optimal traffic signal settings. However, those methods are usually designed to tackle very specific transportation configurations. Here, we introduce an extended version of the sialac benchmark, bringing together several real-world-like study cases with various features related to population, working activities, or traffic light devices. We drive a fitness landscape analysis on these various benchmark instances, which helps to improve the design of optimization algorithms for this class of real-world mobility problems. Thereby, we propose a new adaptive optimization algorithm to tackle each scenario of the benchmark.

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Section: Mutation Operatorsmentioning

confidence: 99%

Section: Performances Of Optimization Algorithmsmentioning

confidence: 99%

Section: Performances Of Optimization Algorithmsmentioning

confidence: 99%

Section: Validation Of the Benchmarkmentioning

confidence: 99%

Section: Appendix a Experiments On Quito Ecuadormentioning

confidence: 99%

See 3 more Smart Citations

Fitness landscapes analysis and adaptive algorithms design for traffic lights optimization on SIALAC benchmark

Leprêtre

Fonlupt

Vérel

et al. 2019

Applied Soft Computing

Self Cite

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show abstract

Evolutionary design optimization of traffic signals applied to Quito city

et al. 2017

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This work applies evolutionary computation and machine learning methods to study the transportation system of Quito from a design optimization perspective. It couples an evolutionary algorithm with a microscopic transport simulator and uses the outcome of the optimization process to deepen our understanding of the problem and gain knowledge about the system. The work focuses on the optimization of a large number of traffic lights deployed on a wide area of the city and studies their impact on travel time, emissions and fuel consumption. An evolutionary algorithm with specialized mutation operators is proposed to search effectively in large decision spaces, evolving small populations for a short number of generations. The effects of the operators combined with a varying mutation schedule are studied, and an analysis of the parameters of the algorithm is also included. In addition, hierarchical clustering is performed on the best solutions found in several runs of the algorithm. An analysis of signal clusters and their geolocation, estimation of fuel consumption, spatial analysis of emissions, and an analysis of signal coordination provide an overall picture of the systemic effects of the optimization process.

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Traffic signal optimization and coordination using neighborhood mutation

Cited by 2 publications

References 14 publications

Fitness landscapes analysis and adaptive algorithms design for traffic lights optimization on SIALAC benchmark

Fitness landscapes analysis and adaptive algorithms design for traffic lights optimization on SIALAC benchmark

Evolutionary design optimization of traffic signals applied to Quito city

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