Using Genetic Algorithm for Traffic Light Control System with a Pedestrian Crossing

Turky, Ayad; Ahmad, Mohd Sharifuddin; Yusoff, Mohd Zamri; Hammad, Baraa Tareq

doi:10.1007/978-3-642-02962-2_65

Cited by 33 publications

(29 citation statements)

References 6 publications

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“…Pedestrians and bicyclists have to manually activate the timing system by pushing a button, affecting the overall efficiency of the traffic light control algorithms. Only a limited number of exceptions, for example, the work [13] considers pedestrian crossing in their traffic light control by using a genetic algorithm. In this algorithm, pedestrian metric is expressed in fitness function to evaluate effectiveness of candidate chromosome.…”

Section: Fig 1: System Architecture For Distributed Multi-agent Q Lementioning

confidence: 99%

Intelligent traffic light control using distributed multi-agent Q learning

Liu

Chen

2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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The combination of Artificial Intelligence (AI) and Internet-of-Things (IoT), which is denoted as AI powered Internet-of-Things (AIoT), is capable of processing huge amount of data generated from large number of devices and handling complex problems in social infrastructures. As AI and IoT technologies are becoming mature, in this paper, we propose to apply AIoT technologies for traffic light control, which is an essential component for intelligent transportation system, to improve the efficiency of smart city's road system. Specifically, various sensors such as surveillance cameras provide real-time information for intelligent traffic light control system to observe the states of both motorized traffic and non-motorized traffic. In this paper, we propose an intelligent traffic light control solution by using distributed multi-agent Q learning, considering the traffic information at the neighboring intersections as well as local motorized and non-motorized traffic, to improve the overall performance of the entire control system. By using the proposed multi-agent Q learning algorithm, our solution is targeting to optimize both the motorized and non-motorized traffic. In addition, we considered many constraints / rules for traffic light control in the real world, and integrate these constraints in the learning algorithm, which can facilitate the proposed solution to be deployed in real operational scenarios. We conducted numerical simulations for a real-world map with realworld traffic data. The simulation results show that our proposed solution outperforms existing solutions in terms of vehicle and pedestrian queue lengths, waiting time at intersections, and many other key performance metrics.

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Section: Fig 1: System Architecture For Distributed Multi-agent Q Lementioning

confidence: 99%

Intelligent traffic light control using distributed multi-agent Q learning

Liu

Chen

2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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“…In that approach, the computation of valid states was done before the algorithm began, and it highly depended on the scenario instance tackled. A GA was also used by Turky et al [36] to improve the performance of traffic lights and pedestrian crossing control in a single four-way, two-lane intersection.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Testing of Traffic Light Programs: Validation in Smart Mobility Scenarios

Ferrer

García-Nieto

Alba

et al. 2016

Mathematical Problems in Engineering

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In smart cities, the use of intelligent automatic techniques to find efficient cycle programs of traffic lights is becoming an innovative front for traffic flow management. However, this automatic programming of traffic lights requires a validation process of the generated solutions, since they can affect the mobility (and security) of millions of citizens. In this paper, we propose a validation strategy based on genetic algorithms and feature models for the automatic generation of different traffic scenarios checking the robustness of traffic light cycle programs. We have concentrated on an extensive urban area in the city of Malaga (in Spain), in which we validate a set of candidate cycle programs generated by means of four optimization algorithms: Particle Swarm Optimization for Traffic Lights, Differential Evolution for Traffic Lights, random search, and Sumo Cycle Program Generator. We can test the cycles of traffic lights considering the different states of the city, weather, congestion, driver expertise, vehicle's features, and so forth, but prioritizing the most relevant scenarios among a large and varied set of them. The improvement achieved in solution quality is remarkable, especially for CO 2 emissions, in which we have obtained a reduction of 126.99% compared with the experts' solutions.

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“…A first attempt corresponds to the study of Rouphail et al, where a genetic algorithm (GA) was coupled with the CORSIM [38] microsimulator for the timing optimization of nine intersections in the city of Chicago (USA). The results, in terms of total queue size, where limited due to the delayed convergence behavior of the GA. Turky et al [71] used a GA to improve the performance of traffic lights and pedestrians crossing control in a unique intersection with four-way two-lane junction. The algorithm solved the limitations of traditional fixed-time control for passing vehicles and pedestrians, and it employed a dynamic control system to monitor two sets of parameters.…”

Section: Introductionmentioning

confidence: 99%

A new nondominated sorting genetic algorithm based to the regression line For fuzzy traffic signal optimization problem

et al. 2017

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Traffic jam is a daily problem in nearly all major cities in the world and continues to increase with population and economic growth of urban areas. Traffic lights, as one of the key components at intersections, play an important role in control of traffic flow. Hence, study and research on phase synchronization and time optimization of the traffic lights could be an important step to avoid creating congestion and rejection queues in a urban network. Here, we describe the application of NSGA-II, a multi-objective evolutionary algorithm, to optimize both vehicle and pedestrian delays in an individual intersection. Results show that parameters found by improved NSGA-II can be superior to those defined by a traffic engineer with respect to several objectives, including total queue length of vehicles and pedestrians.In this paper, we improve NSGA-II algorithm based to the regression line to find a Pareto-optimal solution or a restrictive set of Pareto-optimal solutions based on our solution approaches to the problem, named PDNSGA (Non-dominated Sorting Genetic Algorithm based on Perpendicular Distance). In this paper, our purpose is to present a solution methodology to obtain all Pareto-optimal solutions to optimize traffic signal timing and enable the decision-makers to evaluate a greater number of alternative solutions. The proposed algorithm has the capability of searching Pareto front of the multi-objective problem domain. Further jobs should be concerned on the signal timing optimization method for the oversaturated coordinated intersections or small-scale road network and real-field applications with the traffic signal controller. The high speed of the proposed algorithm and its quick convergence makes it desirable for large scheduling with a large number of phases. Furthermore, we have used the mean deviation from the ideal point (MDI) measure to compare the performance of the MOGA, PDNSGA, NSGA-II, and WBGA by the ANOVA method. It is demonstrated that the our proposed algorithm (PDNSGA) gives better outputs than those of MOGA, NSGA-II, and WBGA in traffic signal optimization problem, statistically .

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Using Genetic Algorithm for Traffic Light Control System with a Pedestrian Crossing

Cited by 33 publications

References 6 publications

Intelligent traffic light control using distributed multi-agent Q learning

Intelligent traffic light control using distributed multi-agent Q learning

Intelligent Testing of Traffic Light Programs: Validation in Smart Mobility Scenarios

A new nondominated sorting genetic algorithm based to the regression line For fuzzy traffic signal optimization problem

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