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Peer-to-Peer Networking and Applications
Erschienen in: Peer-to-Peer Networking and Applications 3/2021

17.09.2020

Machine learning models and techniques for VANET based traffic management: Implementation issues and challenges

verfasst von: Sahil Khatri, Hrishikesh Vachhani, Shalin Shah, Jitendra Bhatia, Manish Chaturvedi, Sudeep Tanwar, Neeraj Kumar

Erschienen in: Peer-to-Peer Networking and Applications | Ausgabe 3/2021

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Abstract

Low latency in communication among the vehicles and RSUs, smooth traffic flow, and road safety are the major concerns of the Intelligent Transportation Systems. Vehicular Ad hoc Network (VANET) has gained attention from various research communities for such a matters. These systems need constant monitoring for proper functioning, opening the doors to apply Machine Learning algorithms on enormous data generated from different applications in VANET (for example, crowdsourcing, pollution control, environment monitoring, etc.). Machine Learning is an approach where the system automatically learns and improves itself based on previously processed data. These algorithms provide efficient supervised and unsupervised learning of these collected data, which effectively implements VANET’s objective. We highlighted the safety, communication, and traffic-related issues in VANET systems and their implementation in-feasibility and explored how machine learning algorithms can overcome these issues. Finally, we discussed future direction and challenges, along with a case study depicting a VANET based scenario.
Vorheriger Artikel Surface and high-altitude combined rainfall forecasting using convolutional neural network
Nächster Artikel Improved differentiable neural architecture search for single image super-resolution

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Metadaten
Titel
Machine learning models and techniques for VANET based traffic management: Implementation issues and challenges
verfasst von
Sahil Khatri
Hrishikesh Vachhani
Shalin Shah
Jitendra Bhatia
Manish Chaturvedi
Sudeep Tanwar
Neeraj Kumar
Publikationsdatum
17.09.2020
Verlag
Springer US
Erschienen in
Peer-to-Peer Networking and Applications / Ausgabe 3/2021
Print ISSN: 1936-6442
Elektronische ISSN: 1936-6450
DOI
https://doi.org/10.1007/s12083-020-00993-4

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