Fitness landscapes analysis and adaptive algorithms design for traffic lights optimization on SIALAC benchmark - Archive ouverte HAL Access content directly
Journal Articles Applied Soft Computing Year : 2019

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

Abstract

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.
Fichier principal
Vignette du fichier
sialac_asoc_4.pdf (3.92 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02547587 , version 1 (30-06-2020)

Identifiers

  • HAL Id : hal-02547587 , version 1

Cite

Florian Leprêtre, Cyril Fonlupt, Sébastien Verel, Virginie Marion, Rolando Armas, et al.. Fitness landscapes analysis and adaptive algorithms design for traffic lights optimization on SIALAC benchmark. Applied Soft Computing, 2019, 85. ⟨hal-02547587⟩
43 View
93 Download

Share

Gmail Facebook X LinkedIn More