Integrated tripartite modules for intelligent traffic light system

Kareem, Emad I Abdul; Hoomod, Haider K.

doi:10.11591/ijece.v12i3.pp2971-2985

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…The automobile is a mechanical system [1] generally intrinsically non-linear by its dynamic characteristics, driven by a human being, now embeds electronic and computer devices. These devices give vehicles the ability to become autonomous, and to be smart qualified.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control of Inter Distance in Convoy of Vehicles Using Model-Free Control and Algebraic Filter

Bouras,

Bekaik

2024

IJTDI

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Road accidents are the leading cause of death; this increase is usually due to speeding. For road safety, intelligent systems have been designed to keep a constant speed and a safe distance between vehicles in a convoy. This article focuses on the synthesis of an interdistance control system using intelligent methods and algorithms. The main idea presented in this article is to implement a model-free control for physical model "spring-damper" known as intelligent control based on an algebraic filter. Our comparative analysis extends beyond comparing the use of a simple derivative and an algebraic filter for intelligent control. We also take into account the effect of noise directly affecting the model's behaviour to demonstrate the robustness of our approach. Through MATLAB simulations, we highlight that our approach exhibits better robustness and stable tracking in the interdistance control system.

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Section: Introductionmentioning

confidence: 99%

Intelligent Control of Inter Distance in Convoy of Vehicles Using Model-Free Control and Algebraic Filter

Bouras,

Bekaik

2024

IJTDI

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“…It intends to measure traffic density by examining the number of vehicles in each lane. Kareem and Hoomod [42] presents an integrated three-part module for intelligent traffic signal systems. This paper presents five machine learning models: linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, K neighbors regressor; compared in the task of predicting traffic flow at intersections, with the purpose of applying them to an adaptive traffic light system, this method allows for a better traffic flow without completely changing the traffic light system structure.…”

Section: Introductionmentioning

confidence: 99%

Adaptive traffic lights based on traffic flow prediction using machine learning models

Moumen,

Abouchabaka,

Rafalia

2023

IJECE

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<span lang="EN-US">Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.</span>

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“…Abdul Kareem and Hoomod [12] proposed a traffic light signal system with three modules: the intelligent visual monitoring module, the intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is capable of identifying the traffic volume on the road.…”

Section: Introductionmentioning

confidence: 99%

Traffic lights time strategy for T-junctions of toll road gate

Tristono,

Cahyono,

Aji

et al. 2023

IJRA

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Vehicles wishing to pass on the toll road must diverge from the traffic flow on public roads. The toll road movement consists of low vehicles (LV) and heavy vehicles (HV). The public road movement is a mixed traffic flow consisting of LV, HV, motorcycles, and unmotorized vehicles. Traffic lights are used at the T-junction of the toll road gate for travel safety management. The traffic lights that implement a fixed-time strategy should be optimized for efficiency. This study aims to review the safety of travel management at T-junctions for the toll road gate when adaptive traffic lights are used. The structural complexity of mathematical modeling with Petri net is used to analyze and measure the feasibility study. Results illustrate that the structural complexity of the traffic lights that implement a fixed time strategy equals 0.387. It is equal to 0.489 for the adaptive traffic lights. The structural complexity of adaptive traffic lights is 25% higher than conventional systems that implement a fixed-time strategy. The adaptive traffic lights time strategy is feasible for travel safety for road users. The travel time is efficient and comfortable because the delay is low. Furthermore, traffic lights can adjust to the demand of vehicles queuing.

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Integrated tripartite modules for intelligent traffic light system

Cited by 3 publications

References 25 publications

Intelligent Control of Inter Distance in Convoy of Vehicles Using Model-Free Control and Algebraic Filter

Intelligent Control of Inter Distance in Convoy of Vehicles Using Model-Free Control and Algebraic Filter

Adaptive traffic lights based on traffic flow prediction using machine learning models

Traffic lights time strategy for T-junctions of toll road gate

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