Traffic Signal Optimization: Minimizing Travel Time and Fuel Consumption

Proceedings of the Genetic and Evolutionary Computation Conference Companion

et al. 2018

Optimizing traffic lights in road intersections is a mandatory step to achieve sustainable mobility and efficient public transportation in modern cities. Several mono or multi-objective optimization methods exist to find the best traffic signals settings, such as evolutionary algorithms, fuzzy logic algorithms, or even particle swarm optimizations. However, they are generally dedicated to very specific traffic configurations. In this paper, we introduce the SIALAC benchmark bringing together about 24 real-world based study cases, and investigate fitness landscapes structure of these problem instances.

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SIALAC benchmark

Proceedings of the Genetic and Evolutionary Computation Conference Companion

et al. 2018

“…These methods help to clarify the nature of the conflicts between several mobility objectives. This was established in the Quito City real-world case study, using the simulation of 20,000 agents on a 40 km 2 transport network area including 70 traffic lights [15].…”

Section: Mobility and Optimization Problemsmentioning

confidence: 99%

“…In the rest of this article, we consider the context of Armas et al works [15] in order to formally define the traffic lights optimization problem. We introduce the foundements of basic optimization algorithms and identify features that would lead to the design of fast-convergence algorithms.…”

Section: Traffic Light Optimization Problemmentioning

confidence: 99%

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

et al. 2019

Applied Soft Computing

Self Cite

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.

Combinatorial Surrogate-Assisted Optimization for Bus Stops Spacing Problem

Lecture Notes in Computer Science

et al. 2020

The distribution of transit stations constitutes an ubiquitous task in large urban areas. In particular, bus stops spacing is a crucial factor that directly affects transit ridership travel time. Hence, planners often rely on traffic surveys and virtual simulations of urban journeys to design sustainable public transport routes. However, the combinatorial structure of the search space in addition to the time-consuming and black-box traffic simulations require computationally expensive efforts. This imposes serious constraints on the number of potential configurations to be explored. Recently, powerful techniques from discrete optimization and machine learning showed convincing to overcome these limitations. In this preliminary work, we build combinatorial surrogate models to approximate the costly traffic simulations. These so-trained surrogates are embedded in an optimization framework. More specifically, this article is the first to make use of a fresh surrogate-assisted optimization algorithm based on the mathematical foundations of discrete Walsh functions in order to solve the real-world bus stops spacing optimization problem. We conduct our experiments with the sialac benchmark in the city of Calais, France. We compare state-of-the-art approaches and we highlight the accuracy and the optimization efficiency of the proposed methods.