A review on computational intelligence methods for controlling traffic signal timing

Araghi, Sahar; Khosravi, Abbas; Creighton, Douglas

doi:10.1016/j.eswa.2014.09.003

Cited by 104 publications

(47 citation statements)

References 50 publications

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“…Significant contributions of researchers towards the optimization of the traffic signal are available in the literature. Araghi et al (2015) have reviewed many computational methods for controlling the traffic signal. They implemented four controllers: Fuzzy logic system, Neural Network, Q-learning and Fixed-time and evaluated the performance for different scenarios [10].…”

Section: Related Workmentioning

confidence: 99%

Multi-Agent Based Traffic Signal Optimization under Fully Actuated Control

Rani¹,

Shaw²

2019

IJITEE

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This paper presents a multi-agent based distributed traffic control model to optimize the traffic signal for multiple intersections. Previous works in the area of traffic signal control suffer from a number of inadequacies, including the use of fixed cycle length, centralized mode of operations and dependency on historical data. Considering these, the aim of this work is to control the traffic signal timings by adjusting the phase sequence in order to minimize the delay in traffic at the intersections. To model the traffic network, a three-tier multi-agent system has been adopted in distributed mode. In addition, a fully actuated signal control algorithm is designed and it utilizes state-space equations to formulate the queue length at the green light phase and red light phase. The proposed model is simulated with SUMO simulator and a comparative analysis has been made between adaptive control method, multi-agent method based on collective learning and multi-agent based fully actuated control method on a similar platform. The results spectacle the proposed traffic control model outperforms that of other existing control methods in all condition; hence it can be deployed to control the tremendous traffic on the road network and to optimize the traffic signal in more effective manner.

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Section: Related Workmentioning

confidence: 99%

Multi-Agent Based Traffic Signal Optimization under Fully Actuated Control

Rani¹,

Shaw²

2019

IJITEE

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“…Mahler et al [24] introduce the prediction model of signal phase and use historical data and realtime phase data to execute a planning algorithm in order to calculate the optimal speed, thereby enhancing energy efficiency. Araghi et al [25] study three traffic light control methods, including Q-learning, neural network and fuzzy logic system, each of which is significantly superior to conventional fixed signal control methods.…”

Section: B Traffic Control Systemsmentioning

confidence: 99%

Routing Algorithm and Traffic Light Control based on Vehicular Delay-Tolerant Networks

Huang¹,

Sha²,

Zhang³

et al. 2016

jcm

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There are problems of low delivery radio and high transmission delay in Vehicular Delay-Tolerant Networks (a.b. VDTN. To address these problems, this paper proposes a Vehicular Delay-tolerant Network routing algorithm based on Contention (a.b. VDNC). VDTN algorithm consists of three strategies: the intersection selection strategy based on the Manhattan distance and traffic information, the first competitive strategy based on the direction of movement and the second competitive strategy based on the position information. Moreover, traffic light also plays an important role to ease traffic congestion and reduce transmission delay. So an Adaptive Traffic-light Control algorithm based on Green-Computing (a.b. ATCG) is proposed. ATCG algorithm involves the following two parts: the calculation of the optimal sequence and that of the recommended speed. Simulation results show that compared to other traditional DTN (Delay-Tolerant Networks) protocols, VDNC algorithm has a higher delivery rate and lower transmission delay. And meanwhile, ATCG algorithm enables vehicles pass through the intersection with fewer stoppages and shorter waiting time. At the same time, the carbon dioxide emission is also able to achieve a minimum for the purpose of environmental protection. Consequently, the collaboration between VDNC and ATCG can further improve the traffic efficiency in VDTN. Index Terms-Delay tolerant network, intelligent traffic light control, green computing, routing algorithm, vehicular networks, wireless sensor network I. INTRODUCTION Due to the disadvantages of current vehicular traffic such as the curing time of traffic light, the behindhand traffic control method, vehicle signal disconnection between vehicles and vehicles as well as vehicles and base-stations and, serious urban vehicle congestion, researchers pay more attention to Intelligent

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“…There are many control forms for different types of road intersections. 1 The traffic efficiency of intersections may be affected by the control types. 2 At present, adaptive control, inductive control, and signal control are three main control methods, and signal control is still the main control mode of intersections; thus, designing a reasonable signal timing scheme is crucial to intersections.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of urban road intersection signal timing based on particle swarm optimization algorithm

Jia

Lin

Luo

et al. 2019

Advances in Mechanical Engineering

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Currently, signal control mode is the main control method of urban road intersections. Given that the traffic efficiency of road intersections is mainly affected by signal timing schemes, it is important to optimize signal timing at road intersections. Therefore, signal timing optimization methods of urban road intersections are explored in this work. When optimizing the timing of the signal at the intersections, the selection of optimization targets play an important role. At present, there are multiple objectives considered while designing signal timing scheme, including capacity, delays, and automobile exhaust. However, from the perspective of the traveler, they are more concerned about their own delay while passing intersections. In this work, we propose a novel multi-objective signal timing optimization model with goals of per capita delay, vehicle emissions, and intersection capacity. Considering the problem characteristics of the target problem, a meta-heuristic algorithm combining difference operator, which is based on Particle Swarm Optimization Algorithm, is developed. To test the validity of proposed approach, we applied it to real-world intersection signal timing problems in China. The results show that the optimized signal timing scheme obtained by the proposed algorithm is better than the realistic one. Also, the effectiveness of the developed algorithm is demonstrated by comparing it with other efficient algorithms.

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A review on computational intelligence methods for controlling traffic signal timing

Cited by 104 publications

References 50 publications

Multi-Agent Based Traffic Signal Optimization under Fully Actuated Control

Multi-Agent Based Traffic Signal Optimization under Fully Actuated Control

Routing Algorithm and Traffic Light Control based on Vehicular Delay-Tolerant Networks

Multi-objective optimization of urban road intersection signal timing based on particle swarm optimization algorithm

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