Adaptive Traffic Controller Based On Pre-Timed System

Kurniawan, Freddy; Sajati, Haruno; Dinaryanto, Okto

doi:10.12928/telkomnika.v14i1.2798

Cited by 6 publications

(7 citation statements)

References 11 publications

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“…Data on the road density and line width are the ANFIS inputs for determining the duration of a given green light. In [28] proposes a pretimed system to accommodate traffic variations. The system determines the traffic density from the intensity histogram standard deviation of the image representing the vehicle detection area of a lane and modifies the green time of each intersection.…”

Section: Smart Traffic Lights For Congestion Reductionmentioning

confidence: 99%

Recent development of smart traffic lights

Yusuf

Arifin

Zulkifli

2021

IJ-AI

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<span id="docs-internal-guid-288f4dcc-7fff-1e8c-0350-5032593b6e4f"><span>Increased traffic flow causes congestion, especially in large cities. Even though congestion is not unusual, traffic jams still result in very high economic and social losses. Several factors cause congestion, one of which is traffic lights. Therefore, a mechanism is needed so that traffic lights can intelligently and adaptively manage signal time allocation according to traffic flow conditions. A traffic light with this type of mechanism is known as a smart traffic light. Smart traffic light cycle settings can be grouped based on the traffic density, scenarios for emergency vehicles, and the interests of pedestrians. This paper analyzes the methods and technologies used in the development of smart traffic light technology from the perspective of these three situations as well as the development of smart traffic light technology in the future.</span></span>

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Section: Smart Traffic Lights For Congestion Reductionmentioning

confidence: 99%

Recent development of smart traffic lights

Yusuf

Arifin

Zulkifli

2021

IJ-AI

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“…The camera was mounted and tilted downward at a fixed location on the left side of the intersection lane. The detail information regarding the camera position has been described on the previous work [11].…”

Section: Experimental Apparatus and Proceduresmentioning

confidence: 99%

“…In the next research, the proposed method will be real-time implemented in a portable device such as a microprocessor based system to feed traffic density data to an adaptive traffic controller based on pre-timed system [11]. As presented in previous research [17], the systems manage the vehicle flow based on signaltiming plans allocated to several types of days.…”

Section: B Traffic Density Estimationmentioning

confidence: 99%

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Image Processing Technique for Traffic Density Estimation

Kurniawan¹,

Sajati²,

Dinaryanto³

2017

IJET

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Abstract-Various techniques for the traffic density estimation in heavy traffic have been developed widely. However, most of them suffer from any drawbacks, especially for traffic fulfilling all kinds of vehicles. In the present study, a new technique of traffic density estimation using a macroscopic approach has been developed. This technique used a background construction and a traffic density estimation algorithm. The first algorithm detects parts of the image containing no moving vehicle in front of or behind the moving vehicle using inter-frame difference. After that, an edge detector detects the edge of image part and classifies whether the image can be used as a part of the background image or not. Meanwhile the second algorithm estimates the traffic density by calculating a ratio between the road area covered by vehicle and the total road area. As a result, this technique has a higher accuracy for determining the traffic density compared with the previous techniques.

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“…The results seem insignificant when applied to an intersection in the city of Semarang. F. Kurniawan, et al, [14] developed the systems that can modify the green time interval of each section of the intersection based on the traffic volume. The appropriate green time interval time of the traffic light can improve the performance of the intersection.…”

Section: Introductionmentioning

confidence: 99%

Modification of the Norwegian Traffic Light States as the Method to Reduce the Travel Delay

Cahyono¹,

Sutomo²,

Aji³

et al. 2019

csit

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Traffic lights have a vital role as regulatory systems to control the vehicles flowed in urban networks. This research is based on the real case. The traffic lights are installed at a massive intersection of an urban network consisting of four sections. The systems control implements the modification of the Norwegian traffic lights states. The behavior of traffic lights states were modeled using Petri net method. For the model verification and validation, the invariants and simulation were applied. The purpose of the implementation of this control system was to reduce travel delays. The intersection performance level was good while the average travel delay on all sections was low. The method used for the testing was the comparison of the simulation results due to the settings that apply the standard system to the imitation of the reality of the system using modifications of the Norwegian traffic lights states. The control system was able to reduce the travel delays slightly. The average of Level of Service (LoS) index of the roads for all sections was at level D. It improved the performance of the intersection, but not yet significant. In addition to setting traffic lights, the presence of flyovers is urgent to improve travel delays.

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Adaptive Traffic Controller Based On Pre-Timed System

Abstract: Abstract

Cited by 6 publications

References 11 publications

Recent development of smart traffic lights

Recent development of smart traffic lights

Image Processing Technique for Traffic Density Estimation

Modification of the Norwegian Traffic Light States as the Method to Reduce the Travel Delay

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