Optimization of mixed cycle length traffic signals

Kesur, Khewal Bhupendra

doi:10.1002/atr.1190

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…Kesur [6] used a modified genetic algorithm to verify that an individual cycle for each intersection is more efficient than a mixed cycle in the aspect of network management, even though there is a difference in the number of vehicles passing at each intersection. This result was contrary to existing research results, which state that using individual cycles in a network is an unreasonable method for the network management.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Signal Control Algorithm for Signalized Intersections under a V2I Communication Environment

Han¹,

Lee

Park

et al. 2019

Journal of Advanced Transportation

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This study aims to develop an optimal signal control algorithm for signalized intersections using individual vehicle’s trajectory data under the vehicle-to-infrastructure (V2I) communication environment. The optimal signal control algorithm developed in this study consists of three modules, namely, a phase group length computation module, a split distribution module, and a phase sequence assignment module. A set of analyses using a microscopic simulation model, VISSIM, was conducted for evaluating the effectiveness of the V2I-based optimal signal control algorithm proposed in this study. The analysis results show that the performance of the V2I-based optimal signal control algorithm is superior to the actuated as well as the fixed signal control methods in an isolated intersection and a 2X3 signalized intersection network. In addition, this study investigated the minimum market penetration rate of V2I equipped vehicles for which the V2I-based optimal signal control algorithm is applicable.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Signal Control Algorithm for Signalized Intersections under a V2I Communication Environment

Han¹,

Lee

Park

et al. 2019

Journal of Advanced Transportation

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“…Thus, Webster's formulas are feasible and applicable at the saturated intersections. Further, by selecting the largest cycle length of the network as the common cycle length, Webster's formulas can be also used in the network [27].…”

Section: Signal Timingmentioning

confidence: 99%

“…if , < min, then (24) , ← min, (25) end if (26) end for (27) for stages sharing movements and their related stages do (28) if , < ∑ , and , == min, then…”

Section: Algorithmsmentioning

confidence: 99%

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Design of Signal Timing Plan for Urban Signalized Networks including Left Turn Prohibition

Tang

Friedrich

2018

Journal of Advanced Transportation

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Urban road networks may benefit from left turn prohibition at signalized intersections regarding capacity, for particular traffic demand patterns. The objective of this paper is to propose a method for minimizing the total travel time by prohibiting left turns at intersections. With the flows obtained from the stochastic user equilibrium model, we were able to derive the stage generation, stage sequence, cycle length, and the green durations using a stage-based method which can handle the case that stages are sharing movements. The final output is a list of the prohibited left turns in the network and a new signal timing plan for every intersection. The optimal list of prohibited left turns was found using a genetic algorithm, and a combination of several algorithms was employed for the signal timing plan. The results show that left turn prohibition may lead to travel time reduction. Therefore, when designing a signal timing plan, left turn prohibition should be considered on a par with other left turn treatment options.

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“…Recently, various research works have been conducted to solve the traffic flow‐relation problems, such as the traffic assignment models , development of traffic flow model and control , traffic signal coordination or optimization , and traffic flow prediction . For the traffic assignment model, as the origin–destination demand is time varying at the peak periods of congestion, dynamic assignment models are needed .…”

Section: Introductionmentioning

confidence: 99%

Applying multiple kernel learning and support vector machine for solving the multicriteria and nonlinearity problems of traffic flow prediction

Lam

2012

J of Advced Transportation

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This article proposes to develop a prediction model for traffic flow using kernel learning methods such as support vector machine (SVM) and multiple kernel learning (MKL). Traffic flow prediction is a dynamic problem owing to its complex nature of multicriteria and nonlinearity. Influential factors of traffic flow were firstly investigated; five-point scale and entropy methods were employed to transfer the qualitative factors into quantitative ones and rank these factors, respectively. Then, SVM and MKL-based prediction models were developed, with the influential factors and the traffic flow as the input and output variables. The prediction capability of MKL was compared with SVM through a case study. It is proved that both the SVM and MKL perform well in prediction with regard to the accuracy rate and efficiency, and MKL is more preferable with a higher accuracy rate when under proper parameters setting. Therefore, MKL can enhance the decision-making of traffic flow prediction. Copyright experiences, knowledge, expertise, and historical data, which could result in that the outcomes differ significantly from one person to another. This kind of limitation has weakened the accuracy and efficiency of solving the traffic flow problem. Meanwhile, traffic flow prediction is regarded as a dynamic problem incorporating various influential factors such as toll fee level and competition from other services. Hence, the selection, classification, and ranking of the variables should be concerned with. Additionally, the linear models in existing research studies cannot handle the problem with the nature of nonlinearity, and thus nonlinear models are needed to solve the problem. Furthermore, the qualitative factors should be dealt with and transferred into quantitative ones, so as to standardize all the factors. And because not all the variables are likely to be considered equally significant, scaling of data is needed, and data should be normalized before being employed for model training, after which advanced methods are required to be applied to give weight to these variables.Recently, various research works have been conducted to solve the traffic flow-relation problems, such as the traffic assignment models [2,3], development of traffic flow model and control [4][5][6][7], traffic signal coordination or optimization [8,9], and traffic flow prediction [10,11,13]. For the traffic assignment model, as the origin-destination demand is time varying at the peak periods of congestion, dynamic assignment models are needed [2]. Szeto et al. [3] propose a multiclass dynamic traffic assignment problem that considers the random evolution of traffic states. Previous research works of the prediction mainly concentrated on linear and nonlinear models. Wild [35] conducted studies on a forecasting method on the basis of classified historical patterns for traffic volume, where the developed forecasting procedure has some advantages compared with conventional approaches. While a combination approach based on principal component analysis and...

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Optimization of mixed cycle length traffic signals

Cited by 14 publications

References 11 publications

Optimal Signal Control Algorithm for Signalized Intersections under a V2I Communication Environment

Optimal Signal Control Algorithm for Signalized Intersections under a V2I Communication Environment

Design of Signal Timing Plan for Urban Signalized Networks including Left Turn Prohibition

Applying multiple kernel learning and support vector machine for solving the multicriteria and nonlinearity problems of traffic flow prediction

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