Driss Laanaoui, My and Lachgar, Mohamed and Mohamed, Hanine and Hamid, Hrimech and Gracia Villar, Santos and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, santos.gracia@uneatlantico.es, UNSPECIFIED (2024) Enhancing Urban Traffic Management Through Real-Time Anomaly Detection and Load Balancing. IEEE Access, 12. pp. 63683-63700. ISSN 2169-3536
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Abstract
Efficient traffic management has become a major concern within the framework of smart city projects. However, the increasing complexity of data exchanges and the growing importance of big data makes this task more challenging. Vehicular ad hoc networks (VANETs) face various challenges, including the management of massive data generated by different entities in their environment. In this context, a proposal is put forth for a real-time anomaly detection system with parallel data processing, thereby speeding up data processing. This approach accurately computes vehicle density for each section at any given time, enabling precise traffic management and the provision of information to vehicles regarding traffic density and the safest route to their destination. Furthermore, a machine learning-based prediction system has been developed to mitigate congestion problems and reduce accident risks. Simulations demonstrate that the proposed solution effectively addresses transportation issues while maintaining low latency and high precision.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Urban traffic management, real-time anomaly detection, intelligent transportation systems, traffic density prediction |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 28 May 2024 09:58 |
| Last Modified: | 28 May 2024 09:58 |
| URI: | http://repositorio.funiber.org/id/eprint/12371 |
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