A Comparative Study on Machine Learning Algorithms for Congestion Control in VANET

Kezia, M; Anusuya, K. V.

doi:10.1109/iciiet55458.2022.9967614

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…These algorithms include deep learning frameworks with multi-layer Extreme Learning Machine (ELM) [67], fusionbased intelligent traffic congestion control system for vehicular networks using machine learning techniques [65] and SVM, KNN, and CNN algorithms [68] [69]. Also, regression, naive Bayes, decision trees, and random forest algorithms are further methods [70]. These algorithms are used to manage traffic congestion, estimate injury severity from traffic collisions, and anticipate traffic flow.…”

Section: Artificial Intelligence In Traffic Managementmentioning

confidence: 99%

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Advancements in Intelligent Transportation Systems ITS and Roadside Unit RSU Design

2023

IJANSER

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This conference paper provides a comprehensive review of the latest research trends in Intelligent Transportation Systems (ITS) and Roadside Unit (RSU) design. ITS and RSUs play a pivotal role in modern transportation infrastructure, facilitating safer, more efficient, and sustainable mobility. This paper highlights key developments, emerging technologies, and research directions within these domains. We delve into topics such as vehicle-to-infrastructure (V2I) communication, edge computing for RSUs, AI-driven traffic management, and the integration of 5G and IoT into ITS. By examining recent advancements, this paper aims to contribute to the ongoing discourse on the future of transportation systems.

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Section: Artificial Intelligence In Traffic Managementmentioning

confidence: 99%

“…These algorithms are used to manage traffic congestion, estimate injury severity from traffic collisions, and anticipate traffic flow. Traffic flow and congestion in metropolitan areas have improved because to the application of machine learning algorithms [66][67] [68][69] [70].…”

Section: Artificial Intelligence In Traffic Managementmentioning

confidence: 99%

Advancements in Intelligent Transportation Systems ITS and Roadside Unit RSU Design

2023

IJANSER

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“…Kezia and Anusuya [37] outlined to predict the local density of vehicles and the channel busy ratio, with the exclusion of beacon considerations. To achieve this, various machine learning techniques such as LR, KNN, Naive Bayes (NB), DT, and RF algorithms are utilized.…”

Section: Literature Reviewmentioning

confidence: 99%

Advances of vehicular ad hoc network using machine learning approach

Thian Meng,

Yogarayan,

Fatimah Abdul Razak

et al. 2023

IJEECS

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<span lang="EN-US">Vehicular Ad hoc Networks (VANETs) play a crucial role in Intelligent Transportation Systems (ITS), enabling seamless communication between vehicles and other entities. VANETs provide a wide range of services, allowing vehicles to communicate with each other and with roadside infrastructure. With the increasing amount of data generated by VANETs, machine learning approaches have emerged as valuable tools to address complex challenges in this domain. This paper presents a comprehensive literature review on the application of machine learning in VANETs. The paper discusses the potential challenges and future research directions in the field, emphasizing the need for more accessible machine learning solutions for VANETs. This review emphasizes the significant role of machine learning approach in advancing the capabilities of VANETs and shaping the future of intelligent transportation systems.</span>

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“…The work in Ref. [53] uses different machine learning algorithms to predict the vehicle density, and then adjusts the vehicle's transmission rate and power accordingly to ensure awareness and reduce channel load. In Ref.…”

Section: Power-and Message Rate-based Approachesmentioning

confidence: 99%

Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET

St. Amour,

Jaekel

2023

Electronics

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Vehicular ad hoc networks (VANETs) facilitate communication among vehicles and possess designated infrastructure nodes to improve road safety and traffic flow. As the number of vehicles increases, the limited bandwidth of the wireless channel used for vehicle-to-vehicle (V2V) communication can become congested, leading to packets being dropped or delayed. VANET congestion control techniques attempt to address this by adjusting different transmission parameters, including the data rate, message rate, and transmission power. In this paper, we propose a decentralized congestion control algorithm where each factor adjusts the data rate (bitrate) used to transmit its wireless packet congestion based on the current load on the channel. The channel load is estimated independently by each vehicle using the measured channel busy ratio (CBR). The simulation results demonstrate that the proposed approach outperforms existing data rate-based algorithms, in terms of both packet reception and overall channel load.

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A Comparative Study on Machine Learning Algorithms for Congestion Control in VANET

Cited by 6 publications

References 10 publications

Advancements in Intelligent Transportation Systems ITS and Roadside Unit RSU Design

Advancements in Intelligent Transportation Systems ITS and Roadside Unit RSU Design

Advances of vehicular ad hoc network using machine learning approach

Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET

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