Towards a secure hybrid adaptive gateway discovery mechanism for intelligent transportation systems

Boukerche, Azzedine; Aljeri, Noura; Abrougui, Kaouther; Wang, Yan

doi:10.1002/sec.1586

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…The intelligent transportation system is an essential part of the smart city [1]- [3]. A thorough understanding of how these systems work and the development of effective traffic management and control strategies are required to optimize their management.…”

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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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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The impact of emerging and disruptive technologies on freight transportation in the digital era: current state and future trends

Dong

Akram

Andersson

et al. 2021

IJLM

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PurposeWith various challenges in the digital era, stakeholders are expressing growing interests in understanding the impact of emerging and disruptive technologies on freight transportation. This paper provides a systematic literature review of the current state of affairs as well as future trends and aims to support stakeholders' decision-making in logistics management in the era of disruptive technologies.Design/methodology/approachSeveral recent and representative articles from academic, industrial and governmental perspectives were investigated to set the scene for this research and to serve as a baseline for electing nine emerging technologies, which were then used to conduct a systematic literature review covering the literature within the area during the past twelve years.Findings3D printing, artificial intelligence, automated robots, autonomous vehicles, big data analytics, blockchain, drones, electric vehicles and the Internet of Things were identified as the emerging technologies. The current state of existing research and potential future opportunities were analyzed.Research limitations/implicationsSince the potential literature body is almost impossible to fully cover, a tradeoff between the number of emerging technologies and the related literature reviewed has been performed. However, the paper provides a novel approach to select the emerging and disruptive technologies and a systematic literature review to fill the identified research gap in the related literature.Practical implicationsThe research support various stakeholders to better capture the current status of and the future opportunities in freight transportation and gain a clearer understanding of the disruptive technologies as well as to guide them in how to deploy these initiatives in future decision-making.Originality/valueBy providing a systematic literature review on the trends, themes and research opportunities in the era of disruptive technologies, the papers bring about broad and comprehensive review on the impact of disruptive technologies on logistics and transportation as well as opportunities to support management decision support in the logistics industry.

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Detection and recognition method of monocular vision traffic safety information for intelligent vehicles

Jin¹,

2020

IFS

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Intelligent vehicle technology has become a research hot issue in recent ten years, the reason is that intelligent vehicles can not only be used as a flexible weapon platform in the military. And in life, it is also a system that provides convenience and security for people. For example, driverless cars and advanced driver assistance systems (ADAS). Information processing is the key to the degree of intelligence, and the detection and recognition of traffic safety information based on monocular vision is the core of information processing, it’s also the bottleneck problem. Because of the complexity and diversity of the environment have brought great challenges to this problem. In this paper, the existing lane detection methods in structured and semi-structured roads do not specifically consider the problem of weak line detection, two models are proposed. Fuzzy LDA enhancement model is used to enhance the contrast of lane area, another brightness contrast saliency model can be used for robust Lane extraction. Then, two models are applied to lane detection, a two-stage lane detection method is proposed and a blind area vehicle detection method is designed. Firstly, the vehicle area is roughly extracted based on road gray statistics, and then the typical vehicle features are screened finely. Finally, the extracted features and SVM classifiers are used to confirm the candidate regions. Experiments show that: The proposed method can detect the vehicle in the blind area very well and is insensitive to the shape distortion and size change of the vehicle.

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Towards a secure hybrid adaptive gateway discovery mechanism for intelligent transportation systems

Cited by 3 publications

References 36 publications

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

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

The impact of emerging and disruptive technologies on freight transportation in the digital era: current state and future trends

Detection and recognition method of monocular vision traffic safety information for intelligent vehicles

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