The vehicular networks has spurred research into intelligent traffic
management systems to alleviate congestion and enhance safety.
However, existing approaches often face challenges in capturing the
complex dynamics of urban traffic flow efficiently. In this study, we
propose an innovative framework integrating Deep Radial Basis
Function (DRBF) networks into vehicular networks for intelligent
traffic management. Our approach aims to address the limitations of
conventional methods by leveraging the representational power of deep
learning while incorporating the flexibility of radial basis function
networks. The problem addressed in this research lies in the
inadequacy of traditional traffic management systems to adapt to the
dynamic nature of urban traffic flow. Existing methods often rely on
simplistic models or predefined rules, which may fail to capture the
intricate patterns and interactions among vehicles on the road.
Consequently, these systems may struggle to provide real-time and
accurate traffic management solutions, leading to increased congestion
and safety hazards. To bridge this research gap, we propose the
integration of DRBF networks, which offer a unique combination of
deep learning capabilities and radial basis function interpolation. This
hybrid architecture enables the model to learn complex spatial and
temporal dependencies from vehicular network data while maintaining
computational efficiency and interpretability. By training the DRBF
network on historical traffic data and real-time sensor inputs, our
methodology can effectively predict traffic flow, identify congestion
hotspots, and optimize route recommendations in urban environments.
Experimental results on real-world traffic datasets demonstrate the
effectiveness of the proposed approach in enhancing traffic
management performance. Compared to traditional methods, our
DRBF-based framework achieves higher accuracy in traffic flow
prediction and generates more efficient routing strategies, leading to
reduced travel times and improved overall traffic conditions.
S. Vijayarangam1, N. Sivakumar2, W. Agitha3, Mohamed Mallick4
Sri Indu College of Engineering and Technology, India1, Varuvan Vadivelan Institute of Technology, India2, DMI College of Engineering, India3, Samsung, Bengaluru, India4
Vehicular Networks, Deep Learning, Traffic Management, Radial Basis Function, Intelligent Transportation Systems
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| Published By :
ICTACT
Published In :
ICTACT Journal on Communication Technology
( Volume: 15 , Issue: 1 , Pages: 3104 - 3111 )
Date of Publication :
March 2024
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132
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