Reducing a congestion with introduce the greedy algorithm on traffic light control

Siswipraptini, Puji Catur; Martono, Wisnu Hendro; Hartanti, Dian

doi:10.1088/1742-6596/974/1/012013

Cited by 5 publications

(2 citation statements)

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“…Figure 1 shows the stages to determine the red/green period of the smart traffic light using Fuzzy Mamdani method. According to research conducted by D. Hartanti et al about crossroads using algorithms and data structures programmed in the Arduino control device system, with the input of maximum speed and length of vehicles it is known that traffic density is seen to increase in the morning and evening [17], Simulation experiments on scale models in detecting the length of the vehicle queue using the help of infrared sensors placed in each intersection path, then applying the Greedy algorithm to help speed up the movement of the green light duration for the path that needs [18], simulation applications of movement and traffic control are used for monitoring queue length and duration of green light in traffic [19]. In a study conducted by Siswipraptini et al [20], TSP (Travelling Salesman Problem) is used to minimize the total length of the trip and the detection results are used for input to the traffic regulation system.…”

Section: Methodsmentioning

confidence: 99%

Optimization of smart traffic lights to prevent traffic congestion using fuzzy logic

Hartanti¹,

Aziza²,

Siswipraptini³

2019

TELKOMNIKA

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One of the main causes of traffic congestion, especially at intersections, is because traffic lights have not been able to show the right time according to the existing traffic conditions. Time settings based on peak/off-peak traffic lights are not enough to handle unexpected situations. The fuzzy mamdani method makes decisions with several stages, the criteria used are the number of vehicles, the length of the queue and the width of the road to be able to optimize the time settings based on the real-time conditions required so that unwanted green signals (when there is no queue) can be avoided. The purpose of this research is to create a simulator to optimize traffic time management, so that the timers on each track have the intelligence to predict the right time, so that congestion at the intersection can be reduced by adding up to 15 seconds of green light from the previous time in the path of many vehicles.

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Section: Methodsmentioning

confidence: 99%

Optimization of smart traffic lights to prevent traffic congestion using fuzzy logic

Hartanti¹,

Aziza²,

Siswipraptini³

2019

TELKOMNIKA

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“…Smart home is one of the applications of the internet of things (IoT), although IoT is also used in other areas such as in transport and traffic management [9]- [12]. The implementation of IoT for smart homes has become one of the most discussed IoT-related research areas and previous research also indicated that there was a growing number of home/household devices connected to the Internet via smartphones [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

IoT for smart home system

Siswipraptini

Aziza

Sangadji

et al. 2021

IJEECS

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<p>This paper examines the integration of smart home and solar panel system that is controlled and monitored using IoT (internet ofthings). To enable the smart home system to monitor the activity within the house and act according to the current conditions, it is equipped with several sensors, actuators and smart appliances. All of these devices have to be connected to a communication network, so they can communicate and provide services forthe smart home’s in habitants. The smart home system was first introduced to provide comfort and convenience, but later it should also address many other things, e.g. the importance of the efficient use of energy or electricity and hybrid use of energy sources. A solar panel is added to the smart home prototype and its addition is studied. Adaptive linear neural network is implemented in the prototype as an algorithm for predicting decisions based on the current conditions. The construction of the proposed integrated systemis carried out through several procedures, i.e. the implementation of the adaptive linear neural network (ADALINE) as the neural network method, the design of the prototype and the testing process. This prototype integrates functionalities of several household appliances into one application controlled by an Android-based framework.</p>

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A capacity model of signalized intersection with dedicated lanes for automated vehicles

Jiang

Xiao

et al. 2023

Transportation Letters

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Reducing a congestion with introduce the greedy algorithm on traffic light control

Cited by 5 publications

References 0 publications

Optimization of smart traffic lights to prevent traffic congestion using fuzzy logic

Optimization of smart traffic lights to prevent traffic congestion using fuzzy logic

IoT for smart home system

A capacity model of signalized intersection with dedicated lanes for automated vehicles

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