eprintid: 12371 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/01/23/71 datestamp: 2024-05-30 20:51:06 lastmod: 2024-05-30 20:51:07 status_changed: 2024-05-30 20:51:06 type: article metadata_visibility: show creators_name: Driss Laanaoui, My creators_name: Lachgar, Mohamed creators_name: Mohamed, Hanine creators_name: Hamid, Hrimech creators_name: Gracia Villar, Santos creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: santos.gracia@uneatlantico.es creators_id: title: Enhancing Urban Traffic Management Through Real-Time Anomaly Detection and Load Balancing ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: Urban traffic management, real-time anomaly detection, intelligent transportation systems, traffic density prediction 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. date: 2024-04 publication: IEEE Access volume: 12 pagerange: 63683-63700 id_number: doi:10.1109/ACCESS.2024.3393981 refereed: TRUE issn: 2169-3536 official_url: http://doi.org/10.1109/ACCESS.2024.3393981 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Artículos y libros Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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. metadata Driss Laanaoui, My; Lachgar, Mohamed; Mohamed, Hanine; Hamid, Hrimech; Gracia Villar, Santos y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, SIN ESPECIFICAR (2024) Enhancing Urban Traffic Management Through Real-Time Anomaly Detection and Load Balancing. IEEE Access, 12. pp. 63683-63700. ISSN 2169-3536 document_url: http://repositorio.uneatlantico.es/id/eprint/12371/1/Enhancing_Urban_Traffic_Management_Through_Real-Time_Anomaly_Detection_and_Load_Balancing.pdf