TY  - JOUR
AV  - public
VL  - 12
UR  - http://doi.org/10.1109/ACCESS.2024.3393981
N2  - 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.
Y1  - 2024/04//
TI  - Enhancing Urban Traffic Management Through Real-Time Anomaly Detection and Load Balancing
ID  - uneatlantico12371
SP  - 63683
SN  - 2169-3536
EP  - 63700
JF  - IEEE Access
A1  - Driss Laanaoui, My
A1  - Lachgar, Mohamed
A1  - Mohamed, Hanine
A1  - Hamid, Hrimech
A1  - Gracia Villar, Santos
A1  - Ashraf, Imran
KW  - Urban traffic management
KW  -  real-time anomaly detection
KW  -  intelligent transportation systems
KW  - 
            traffic density prediction
ER  -