Optimal network-level traffic signal control: A benders decomposition-based solution algorithm

Mohebifard, Rasool; Hajbabaie, Ali

doi:10.1016/j.trb.2019.01.012

Cited by 48 publications

(18 citation statements)

References 63 publications

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“…The search for 2015-2020 resulted in a total of 10 such papers [76,84,88,91,[101][102][103][104][105][106] utilizing an analytical approach. Only 3 papers [84,103,105] considered a network problem, and the remaining 7 considered a single network problem. But the size of the realistic problems and the amount of interaction that needs to be included in the model for them to be interesting, make use of analytical methods significantly less practical (promising) for TST problems.…”

Section: Analytical Vs Simulation-optimization and CI Methodsmentioning

confidence: 99%

“…References, [79][80][81][82][83] formulated the TST optimization problem as mixed-integer linear programming (MILP) whereas references [84,85] presented as the non-linear programming models…”

Section: Dynamic Milp and Non-linear Programming Based Approachesmentioning

confidence: 99%

“…Mohebifard et al [84] formulated the network-level TST optimization problem as a Mixed-Integer Non-Linear Program (MINLP) which was based on the Cell Transmission Model (CTM) and presented a customized methodology to solve it with a tight optimality gap. Yu et al [85] put forward a non-linear programming model for an optimal TSC setting.…”

Section: Dynamic Milp and Non-linear Programming 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.

104

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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: Analytical Vs Simulation-optimization and CI Methodsmentioning

confidence: 99%

Section: Dynamic Milp and Non-linear Programming Based Approachesmentioning

confidence: 99%

Section: Dynamic Milp and Non-linear Programming 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.

104

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“…We have used a single set of constraints for diverge, merge, and ordinary cells by considering summation of flows over all incoming links to or outgoing links from a cell in any constraint. Mohebifard and Hajbabaie () have provided a set of constraints for each cell type and captured the flow feasibility conditions with a set of mixed integer nonlinear constraints. The constraints ensure that flow of vehicles follow the CTM flow‐density diagram; however, at the expense of adding many binary variables to the mathematical program.…”

Section: Problem Formulationmentioning

confidence: 99%

A decomposition scheme for parallelization of system optimal dynamic traffic assignment on urban networks with multiple origins and destinations

Mehrabipour

Hajibabai

Hajbabaie

2019

Computer aided Civil Eng

Self Cite

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This paper presents a decomposition scheme to find near‐optimal solutions to a cell transmission model‐based system optimal dynamic traffic assignment problem with multiple origin‐destination pairs. A linear and convex formulation is used to define the problem characteristics. The decomposition is designed based on the Dantzig–Wolfe technique that splits the set of decision variables into subsets through the construction of a master problem and subproblems. Each subproblem includes only a single origin‐destination pair with significantly less computational burden compared to the original problem. The master problem represents the coordination between subproblems through the design of interactive flows between the pairs. The proposed methodology is implemented in two case study networks of 20 and 40 intersections with up to 25 origin‐destination pairs. The numerical results show that the decomposition scheme converges to the optimal solution, within 2.0% gap, in substantially less time compared to a benchmark solution, which confirms the computational efficiency of the proposed algorithm. Various network performance measures have been assessed based on different traffic state scenarios to draw managerial insights.

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“…In [9], authors made an attempt of topological formalization of the motor transport flow as a set (C) of vehicles, consisting of oppositely directed subsets (С 1 and С 2 ). The combined movement along some section of the highway is characterized by such physical features as dimension (G), dynamic gauge (DG), interval of movement (I) of vehicles.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Analytical study of multifractal invariant attributes of traffic flows

Skydan

Sheludchenko

Kukharets

et al. 2019

EEJET

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Optimal network-level traffic signal control: A benders decomposition-based solution algorithm

Cited by 48 publications

References 63 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

A decomposition scheme for parallelization of system optimal dynamic traffic assignment on urban networks with multiple origins and destinations

Analytical study of multifractal invariant attributes of traffic flows

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