A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System

This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC

SIALAC Benchmark: On the design of adaptive algorithms for traffic lights problems

International audienceOptimizing 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

Traffic Optimization Through Waiting Prediction and Evolutive Algorithms

Traffic optimization systems require optimization procedures to optimize traffic light timing settings in order to improve pedestrian and vehicle mobility. Traffic simulators allow obtaining accurate estimates of traffic behavior by applying different timing configurations, but require considerable computational time to perform validation tests. For this reason, this project proposes the development of traffic optimizations based on the estimation of vehicle waiting times through the use of different prediction techniques and the use of this estimation to subsequently apply evolutionary algorithms that allow the optimizations to be carried out. The combination of these two techniques leads to a considerable reduction in calculation time, which makes it possible to apply this system at runtime. The tests have been carried out on a real traffic junction on which different traffic volumes have been applied to analyze the performance of the system

Анализ мирового опыта в применении искусственного интеллекта в системах управления дорожным движением различного уровня

При функционировании автоматизированных систем управления дорожным движением и их трансформации в интеллектуальные транспортные системы предъявляются современные требования к совокупному параметру – безопасности, который характеризует качество и эффективность дорожного движения, особенно при организации высокоскоростного нагруженного движения механических транспортных средств. В статье рассмотрены вопросы по применению для этих целей различных математических методов при моделировании транспортных процессов и систем, в том числе с учетом развития алгоритмов искусственного интеллекта, чтобы при принятии решений по управлению движением обладать достоверными прогнозными показателями, полученными по адекватным моделям. Выполнено сравнение моделей и даны рекомендации по их применимости и получаемым результатам для целей управления дорожным движением