Machine learning models and techniques for VANET based traffic management: Implementation issues and challenges
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 mon...
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| Published in | Peer-to-peer networking and applications Vol. 14; no. 3; pp. 1778 - 1805 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.05.2021
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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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. |
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| AbstractList | 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. |
| Author | Chaturvedi, Manish Kumar, Neeraj Shah, Shalin Tanwar, Sudeep Bhatia, Jitendra Khatri, Sahil Vachhani, Hrishikesh |
| Author_xml | – sequence: 1 givenname: Sahil surname: Khatri fullname: Khatri, Sahil organization: Department of Computer Engineering, Vishwakarma Government Engineering College – sequence: 2 givenname: Hrishikesh surname: Vachhani fullname: Vachhani, Hrishikesh organization: Department of Computer Engineering, Vishwakarma Government Engineering College – sequence: 3 givenname: Shalin surname: Shah fullname: Shah, Shalin organization: Department of Computer Engineering, Vishwakarma Government Engineering College – sequence: 4 givenname: Jitendra surname: Bhatia fullname: Bhatia, Jitendra organization: Department of Computer Engineering, Vishwakarma Government Engineering College – sequence: 5 givenname: Manish surname: Chaturvedi fullname: Chaturvedi, Manish organization: Department of Computer Science and Engineering, Indian Institute of Infrastructure Technology Research And Management (IITRAM) – sequence: 6 givenname: Sudeep surname: Tanwar fullname: Tanwar, Sudeep organization: Department of Computer Science and Engineering, Institute of Technology, Nirma University – sequence: 7 givenname: Neeraj orcidid: 0000-0002-3020-3947 surname: Kumar fullname: Kumar, Neeraj email: neeraj.kumar@thapar.edu organization: Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala India. Department of Computer Science and Information Engineering, Asia University, Taiwan. King Abdul Aziz University |
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| SubjectTerms | 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 |
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| Title | Machine learning models and techniques for VANET based traffic management: Implementation issues and challenges |
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