Arterial traffic signal optimization using Particle Swarm Optimization in an integrated VISSIM-MATLAB simulation environment

Dabiri, Sina; Abbas, Montasir

doi:10.1109/itsc.2016.7795641

Cited by 29 publications

(17 citation statements)

References 15 publications

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“…Reference numbers [29][30][31][32][33][34] employ meta-heuristic methods for optimization along with a microsimulation tool. Among the meta-heuristics implementations, population-based methods were mostly employed, where PSO, ACO, and GA are the most heavily utilized methods.…”

Section: Metaheuristics Based Approachesmentioning

confidence: 99%

“…Gökçe et al [29], Dabiri and Abbas [30], Panovski and Zaharia [31], Chuo et al [35] utilized PSO for the fulfillment of their objectives. Among them, [29] is the only study that has been carried out for the signalized roundabout containing 28 signal heads, whereas [30,31] worked on optimizing the arterial traffic signals having three intersections and the issues related with traffic flow management in the urban areas respectively. Jintamuttha, et al [33] proposed a finite-interval model to achieve the objective regarding TST.…”

Section: Metaheuristics Based Approachesmentioning

confidence: 99%

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State-of-art review of traffic signal control methods: challenges and opportunities

Qadri

Gökçe

Öner

2020

Eur. Transp. Res. Rev.

105

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Introduction Due to the menacing increase in the number of vehicles on a daily basis, abating road congestion is becoming a key challenge these years. To cope-up with the prevailing traffic scenarios and to meet the ever-increasing demand for traffic, the urban transportation system needs effective solution methodologies. Changes made in the urban infrastructure will take years, sometimes may not even be feasible. For this reason, traffic signal timing (TST) optimization is one of the fastest and most economical ways to curtail congestion at the intersections and improve traffic flow in the urban network. Purpose Researchers have been working on using a variety of approaches along with the exploitation of technology to improve TST. This article is intended to analyze the recent literature published between January 2015 and January 2020 for the computational intelligence (CI) based simulation approaches and CI-based approaches for optimizing TST and Traffic Signal Control (TSC) systems, provide insights, research gaps and possible directions for future work for researchers interested in the field. Methods In analyzing the complex dynamic behavior of traffic streams, simulation tools have a prominent place. Nowadays, microsimulation tools are frequently used in TST related researches. For this reason, a critical review of some of the widely used microsimulation packages is provided in this paper. Conclusion Our review also shows that approximately 77% of the papers included, utilizes a microsimulation tool in some form. Therefore, it seems useful to include a review, categorization, and comparison of the most commonly used microsimulation tools for future work. We conclude by providing insights into the future of research in these areas.

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Section: Metaheuristics Based Approachesmentioning

confidence: 99%

Section: Metaheuristics Based Approachesmentioning

confidence: 99%

State-of-art review of traffic signal control methods: challenges and opportunities

Qadri

Gökçe

Öner

2020

Eur. Transp. Res. Rev.

105

View full text Add to dashboard Cite

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“…Otherwise, the signal plans tested well during workdays may not work well on holidays, because the behaviors of the drivers and the traffic flow differ from day to day. Therefore, to shorten the evaluations, the signal control plan is often designed through simulations of professional software, like VISSIM [72], [73]. As the simulations can also be time consuming, arterial traffic signal timing optimization is an ideal place to employ DDEAs.…”

Section: Arterial Traffic Signal Timing Optimizationmentioning

confidence: 99%

“…In this case, the problem dimension is 37, with four variables of θ , 32 variables of g, and one variable of C, respectively. In addition, C max and C min are set as 120 and 60 s, while g max and g min are configured as 40 and 10 s. The objective function is defined as the average travel time for each vehicle, which can be simulated by VISSIM [73]. As the timing of the signals in VISSIM has precision limits, the solution value of each dimension will be rounded off before simulations.…”

Section: Arterial Traffic Signal Timing Optimizationmentioning

confidence: 99%

Boosting Data-Driven Evolutionary Algorithm With Localized Data Generation

Zhan

Wang

et al. 2020

IEEE Trans. Evol. Computat.

119

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By efficiently building and exploiting surrogates, data-driven evolutionary algorithms (DDEAs) can be very helpful in solving expensive and computationally intensive problems. However, they still often suffer from two difficulties. First, many existing methods for building a single ad hoc surrogate are suitable for some special problems but may not work well on some other problems. Second, the optimization accuracy of DDEAs deteriorates if available data are not enough for building accurate surrogates, which is common in expensive optimization problems. To this end, this article proposes a novel DDEA with two efficient components. First, a boosting strategy (BS) is proposed for self-aware model managements, which can iteratively build and combine surrogates to obtain suitable surrogate models for different problems. Second, a localized data generation (LDG) method is proposed to generate synthetic data to alleviate data shortage and increase data quantity, which is achieved by approximating fitness through data positions. By integrating the BS and the LDG, the BDDEA-LDG algorithm is able to improve model accuracy and data quantity at the same time automatically according to the problems at hand. Besides, a tradeoff is empirically considered to strike a better balance between the effectiveness of surrogates and the time cost for building them. The experimental results show that the proposed BDDEA-LDG algorithm can generally outperform both traditional methods without surrogates and other state-of-the-art DDEA son widely used benchmarks and an arterial traffic signal timing real-world optimization problem. Furthermore, the proposed BDDEA-LDG algorithm can use only about 2% computational budgets of traditional methods for producing competitive results.

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“…where D is the total delay and q is the ow in vehicles per second. [28,29]. Although exact solutions may not be achieved by using these types of algorithms, near-optimal solutions can be obtained.…”

Section: Performance Criteriamentioning

confidence: 99%

A Differential Evolution Algorithm‐Based Traffic Control Model for Signalized Intersections

Çakıcı

Murat

2019

Advances in Civil Engineering

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Sustainable management of traffic flows at signalized intersections is an important issue in terms of traffic engineering. The minimization of lost time, emission, fuel consumption, etc., can be achieved by optimization-based intersection management. In this study, a new traffic signal control model is developed for the management of three-leg signalized intersections. In the proposed model, signal timing and signal phasing are optimized simultaneously using Differential Evolution (DE) algorithm which is one of the population-based metaheuristic algorithms. The effectiveness of the model is tested on sample traffic scenarios with VISSIM simulation software considering average vehicle delay performance criteria. Results show that the proposed approach may reduce the average vehicle delay between the rates of 28%–42% and 3%–38% comparing to the optimum fixed-time signal control and vehicle-actuated signal control for tested scenarios, respectively.

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Arterial traffic signal optimization using Particle Swarm Optimization in an integrated VISSIM-MATLAB simulation environment

Cited by 29 publications

References 15 publications

State-of-art review of traffic signal control methods: challenges and opportunities

State-of-art review of traffic signal control methods: challenges and opportunities

Boosting Data-Driven Evolutionary Algorithm With Localized Data Generation

A Differential Evolution Algorithm‐Based Traffic Control Model for Signalized Intersections

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