On the Simulation of Traffic Signals Operation

Sadoun, Balqies

doi:10.1177/0037549708094369

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…(1) When permissible, all vehicles traverse the parking line at a steady speed [3]. (2) Vehicles maintain a constant speed before initiating braking and after starting [4].…”

Section: Model Assumptionsmentioning

confidence: 99%

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Optimizing traffic light signal timing using a yellow interval model: Methodology and empirical evaluation

Wang

2024

ACE

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All over the world, especially in urban areas, the growing population and prosperity of the car industry have resulted in a steady increment in vehicles on the road, leading that already-built car-transportation infrastructure can not be commensurate with the traffic demand. Consequently, this will cause traffic congestion and thus a higher rate of traffic accidence. In such circumstances, the ability of traffic management becomes indispensable and crucial to ameliorate or even solve this problem. Although various advanced approaches can provide effective traffic management, the cost can be prohibitive to execute them pervasively. Many algorithms, especially machine learning related, require a mass of input data and a high hash rate. As a result, massive sensor systems, advanced computers, and elaborate programming will be unavoidable, which are costly. Two models in this paper, respectively, yellow light interval and traffic-light signalization, are based on summarizing and improving on previous models and are easy to understand and apply. The model focuses on timing the traffic light to maximize the number of vehicles passing through a traffic intersection in a given time. Many authors in their paper on traffic-light signalization hardly consider the effect of yellow light intervals. This paper provides a relatively comprehensive model of traffic-light signalization. Although the model in this paper is inferior to those advanced models utilized in effectiveness, this model can be applied simpler than others.

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“…(1) When permissible, all vehicles traverse the parking line at a steady speed [3]. (2) Vehicles maintain a constant speed before initiating braking and after starting [4].…”

Section: Model Assumptionsmentioning

confidence: 99%

“…(2) Vehicles maintain a constant speed before initiating braking and after starting [4]. (3) The terrain is considered to be completely horizontal. (4) The dynamic friction coefficient between the tire and the road surface remains constant [5].…”

Section: Model Assumptionsmentioning

confidence: 99%

Optimizing traffic light signal timing using a yellow interval model: Methodology and empirical evaluation

Wang

2024

ACE

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“…Our work tackles the interaction between different kinds of road users. Traffic light optimisations were presented, working on synthetical graphs [4,26]. The optimisation of traffic lights in these works is only done in synthetical scenarios and not on the road maps of real cities.…”

Section: Related Workmentioning

confidence: 99%

From GIS to Mixed Traffic Simulation in Urban Scenarios

Dallmeyer¹,

Lattner²,

Timm³

2011

Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques

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Simulations are widely used for modeling, analysis, planning, and optimisation of traffic flows and phenomena. For realistic traffic simulations within urban scenarios, the following tasks have to be solved: (1) modeling of the road structure; (2) specification of the behaviour on the road. In our days, very detailed road models for almost any major city exist in Geographic Information Systems (GIS). In the last two decades, the Nagel-Schreckenberg model (NaSch) has been established as de facto standard for car behaviour in freeway traffic due to its efficient and realistic simulations. Within urban scenarios, NaSch lacks of flexibility to integrate heterogeneous road users like cars and bicycles. The tasks mentioned before are addressed in this paper, i.e., we propose an approach for modeling and specification of urban mixed traffic simulations. As a first step (1), an extended graph as basis for traffic simulation has to be designed. For a concrete scenario, it will be automatically generated on basis of OpenStreetmap cartographical material. The specification of road user behaviour (2) has been influenced by the NaSch model. However, the model has been extended to cover the lack of NaSch in urban scenarios: A non cell-based approach is chosen for traffic movement. Furthermore, the routing of traffic users is based on either probability or A* based routing. In this paper, details on the modeling and specification are presented and experimental results are provided.

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“…It was shown that the 3-phase system had an upper edge over the 4-phase system in terms of improvement in queue length, mean utilization and waiting time. Sadoun [4] also recognized the undesirable consequences of delays for both the environment and drivers. Various probability distributions such as Poisson, Exponential and Uniform were considered and then the optimum red-green timings required for flow of traffic were computed.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Road Traffic Modeling through a Novel Real Time Traffic Simulator

Singh,

Aggarwal

2023

Curr. Appl. Sci. Technol.

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Dynamic traffic control is a challenging task that involves meeting rising traffic demands and cutting down on intersection delays. The existing yellow/red/green light fixed transition periods used by traffic controllers make it impossible for them to adapt to changing real-time traffic conditions at intersections. Furthermore, it would be impractical to hire traffic officers for every intersection throughout the day due to a lack of personnel, and even if sufficient personnel are available, it would be a very expensive set up. A fuzzy based traffic model was designed and simulated in real time conditions using the developed traffic simulator algorithm to control the traffic jamming at road intersections. The developed fuzzy model was based on three fuzzy inputs and its performance was measured for 13 cases of varying road width. The developed model outperformed the traditional fixed-time delay model in all the cases and the level of improvement was further increased when the congestion was high. Narrower roads were more congested and the improvement with fuzzy systems as compared to its fixed time delay counterparts was as high as 26%. This research findings clearly support the use of fuzzy logic for handling the most challenging problem of traffic congestion in densely populated regions.

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On the Simulation of Traffic Signals Operation

Cited by 6 publications

References 10 publications

Optimizing traffic light signal timing using a yellow interval model: Methodology and empirical evaluation

Optimizing traffic light signal timing using a yellow interval model: Methodology and empirical evaluation

From GIS to Mixed Traffic Simulation in Urban Scenarios

An Efficient Road Traffic Modeling through a Novel Real Time Traffic Simulator

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