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

• Published in: Peer-to-peer networking and applications Vol. 14; no. 3; pp. 1778 - 1805
• Main Authors: Khatri, Sahil, Vachhani, Hrishikesh, Shah, Shalin, Bhatia, Jitendra, Chaturvedi, Manish, Tanwar, Sudeep, Kumar, Neeraj
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.05.2021; Springer Nature B.V
• Subjects: Algorithms; Communications Engineering; Communications traffic; Computer Communication Networks; Data collection; Engineering; Environmental monitoring; Information Systems and Communication Service; Intelligent transportation systems; Machine learning; Mobile ad hoc networks; Networks; Pollution control; Pollution monitoring; Signal,Image and Speech Processing; Special Issue on P2P Computing for Deep Learning; Traffic flow; Traffic management; Traffic safety; VANET; Safety; Machine learning; Traffic; Communication
• ISSN: 1936-6442; 1936-6450
• DOI: 10.1007/s12083-020-00993-4

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.