Fuzzy Multiobjective Traffic Light Signal Optimization

Pour, Nasser Shahsavari; Asadi, Hamed; Kheradmand, Mansoor

doi:10.1155/2013/249726

Cited by 5 publications

(5 citation statements)

References 13 publications

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“…Many techniques, such as queue theory [18,19], fuzzy logic control [20][21][22], and genetic algorithm [23], have been exploited in traffic light control. Queue theory method deploys sensors at each intersection, and sensors collect the queue size to calculate the green light time.…”

Section: Smart Traffic Light Systemmentioning

confidence: 99%

Smart Traffic Lights over Vehicular Named Data Networking

Al-qutwani

Wang

2019

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The existing traffic light system fails to deal with the increase in vehicular traffic requirements due to fixed time programming. Traffic flow suffers from vehicle delay and congestion. A new networking technology called vehicular ad hoc networking (VANET) offers a novel solution for vehicular traffic management. Nowadays, vehicles communicate with each other (V2V), infrastructure (V2I), or roadside units (V2R) using IP-based networks. Nevertheless, IP-based networks demonstrate low performance with moving nodes as they depend on communication with static nodes. Currently, the research community is studying a new networking architecture based on content name called named data networking (NDN) to implement it in VANET. NDN is suitable for VANET as it sends/receives information based on content name, not content address. In this paper, we present one of VANET’s network applications over NDN, a smart traffic light system. Our system solves the traffic congestion issue as well as reducing the waiting time of vehicles in road intersections. This system replaces the current conventional system with virtual traffic lights (VTLs). Instead of installing traffic lights at every intersection, we utilize a road side unit (RSU) to act as the intersection controller. Instead of a light signal, the RSU collects the orders of vehicles that have arrived or will arrive at the intersection. After processing the orders according to the priority policy, the RSU sends an instant message for every vehicle to pass the intersection or wait for a while. The proposed system mimics a human policeman intersection controlling. This approach is suitable for autonomous vehicles as they only receive signals from the RSU instead of processing many images. We provide a map of future work directions for enhancing this solution to take into account pedestrian and parking issues.

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Section: Smart Traffic Light Systemmentioning

confidence: 99%

Smart Traffic Lights over Vehicular Named Data Networking

Al-qutwani

Wang

2019

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“…The approaches in [13,21,26,27] used fuzzy logic for traffic signal control. A few of them are summarize below:…”

Section: B Fuzzy Logic Controlmentioning

confidence: 99%

Smart Cities: Study and Comparison of Traffic Light Optimization in Modern Urban Areas Using Artificial Intelligence

Kabrane¹,

Krit²,

Elmaimouni³

2018

IJARCSSE

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Abstract-In large cities, the increasing number of vehicles private, society, merchandise, and public transport, has led to traffic congestion. Users spend much of their time in endless traffic congestion. To solve this problem, several solutions can be envisaged. The interest is focused on the system of road signs: The use of a road infrastructure is controlled by a traffic light controller, so it is a matter of knowing how to make the best use of the controls of this system (traffic lights) so as to make traffic more fluid. The values of the commands computed by the controller are determined by an algorithm which is ultimately, only solves a mathematical model representing the problem to be solved. The objective is to make a study and then the comparison on the optimization techniques based on artificial intelligence1 to intelligently route vehicle traffic. These techniques make it possible to minimize a certain function expressing the congestion of the road network. It can be a function, the length of the queue at intersections, the average waiting time, also the total number of vehicles waiting at the intersection

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“…There are also some studies that utilize neural networks, 36–38 cellular automata, 39 fuzzy control 40 and fuzzy-neuro methods 41 for urban traffic signal control, but they diverge considerably in their settings, and for that reason are not included.…”

Section: Literature Surveymentioning

confidence: 99%

Traffic signal optimization with Particle Swarm Optimization for signalized roundabouts

2015

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At complex intersections, traffic congestion causes pollution and leads to accidents, high commute times and many other problems. Correct traffic signal timing can help to reduce the congestion and it can improve the traffic flow. We present a new microscopic traffic simulation-optimization model with Particle Swarm Optimization (PSO) for traffic signal timing and demonstrate the model with an application to a major roundabout with 28 traffic signal heads in Izmir, Turkey, using real data. A VISSIM (Verkehr In Städten–SIMulationsmodell) model of the Osman Kibar Roundabout, based on actual traffic flow data, is developed. A PSO model was used to search for traffic signal timings that would minimize mean travel time through the roundabout, and the microsimulation model (VISSIM) was used to evaluate the solutions from PSO. The changes in the traffic signal timings resulted in a 55.9% decrease in average delay time per vehicle passing through the roundabout, and a 9.3% increase in the number of vehicles passing through the roundabout per unit time in the best-case scenario.

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Fuzzy Multiobjective Traffic Light Signal Optimization

Cited by 5 publications

References 13 publications

Smart Traffic Lights over Vehicular Named Data Networking

Smart Traffic Lights over Vehicular Named Data Networking

Smart Cities: Study and Comparison of Traffic Light Optimization in Modern Urban Areas Using Artificial Intelligence

Traffic signal optimization with Particle Swarm Optimization for signalized roundabouts

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