Optimal traffic signal control under dynamic user equilibrium and link constraints in a general network

Yu, Hao; Ma, Rui; Zhang, H. Michael

doi:10.1016/j.trb.2018.02.009

Cited by 40 publications

(22 citation statements)

References 51 publications

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“…Recently, GA and ACO (ant colony optimization) have been investigated in several previous studies to optimize cycle length and other traffic performance measures [58][59][60][61]. Foy et al used GA to identify minimum delay estimates by optimizing the cycle length under multiple scenarios [62].…”

Section: Previous Studiesmentioning

confidence: 99%

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Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

et al. 2020

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Traffic signal control is an integral component of an intelligent transportation system (ITS) that play a vital role in alleviating traffic congestion. Poor traffic management and inefficient operations at signalized intersections cause numerous problems as excessive vehicle delays, increased fuel consumption, and vehicular emissions. Operational performance at signalized intersections could be significantly enhanced by optimizing phasing and signal timing plans using intelligent traffic control methods. Previous studies in this regard have mostly focused on lane-based homogenous traffic conditions. However, traffic patterns are usually non-linear and highly stochastic, particularly during rush hours, which limits the adoption of such methods. Hence, this study aims to develop metaheuristic-based methods for intelligent traffic control at isolated signalized intersections, in the city of Dhahran, Saudi Arabia. Genetic algorithm (GA) and differential evolution (DE) were employed to enhance the intersection’s level of service (LOS) by optimizing the signal timings plan. Average vehicle delay through the intersection was selected as the primary performance index and algorithms objective function. The study results indicated that both GA and DE produced a systematic signal timings plan and significantly reduced travel time delay ranging from 15 to 35% compared to existing conditions. Although DE converged much faster to the objective function, GA outperforms DE in terms of solution quality i.e., minimum vehicle delay. To validate the performance of proposed methods, cycle length-delay curves from GA and DE were compared with optimization outputs from TRANSYT 7F, a state-of-the-art traffic signal simulation, and optimization tool. Validation results demonstrated the adequacy and robustness of proposed methods.

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Section: Previous Studiesmentioning

confidence: 99%

“…The researchers found that ACO provide more reliable solutions compared to GA, particularly at large number of trials. Renfrew and Yu also proposed ACO to reduce vehicle waiting time at signalized intersection [60]. ACO outperformed fully actuated control signal, particularly under high traffic demand.…”

Section: Previous Studiesmentioning

confidence: 99%

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

et al. 2020

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“…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. A new aspect of this model was combining the double queue traffic flow model to the signal-controlled traffic network to record the traffic dynamics and queue spillback in real-time.…”

Section: Dynamic Milp and Non-linear Programming Based Approachesmentioning

confidence: 99%

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

Qadri

Gökçe

Öner

2020

Eur. Transp. Res. Rev.

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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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“…Different signal optimization tools have also been developed and applied in practice, such as TRANSYT, VISSIM, CMEM, VISGAOST and JUNCTION. General reviews can be found exemplary in (Sood & Deepanshu, 2017;Yu, Ma, & Zhang, 2018).…”

Section: Introductionmentioning

confidence: 99%

Improving SUMO's Signal Control Programs by Introducing Route Information

Flötteröd¹,

Behrisch²

EPiC Series in Engineering

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To overcome the data insufficiency and achieve reasonable simulation results this paper has proposed to use the Webster's delay model, together with given route information, to optimize traffic signal programs generated by SUMO. The pre-timed traffic signal cycle length and the green time allocation will be optimized for each given traffic signal program accordingly. The proposed approach takes also signal sharing among intersections into account. Two cases studies, i.e. one single intersection and one traffic signal controlled area, are conducted to evaluate the performance of the proposed approach. The simulation results show that there are apparent reductions in average trip duration, waiting time, time loss and departure delay with use of the proposed approach. The traffic efficiency can be improved consequently. In addition, some issues are pointed out as future works for extending the proposed approach.

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Optimal traffic signal control under dynamic user equilibrium and link constraints in a general network

Cited by 40 publications

References 51 publications

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

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

Improving SUMO's Signal Control Programs by Introducing Route Information

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