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Short-Term Traffic Prediction: A Bayesian Fusion of Graph Attention Network and SARIMA Model With Quantum Networking
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Author(s): S. Jothishwar (SRM Institute of Science and Technology, India), Visnu Dharsini S. (SRM Institute of Science and Technology, India), S. Dinesh (SRM Institute of Science and Technology, India)and K. Jayasurya (SRM Institute of Science and Technology, India)
Copyright: 2025
Pages: 14
Source title:
Multidisciplinary Applications of AI and Quantum Networking
Source Author(s)/Editor(s): Christo Ananth (Samarkand State University, Uzbekistan), Osamah Ibrahim Khalaf (Al-Nahrain University, Iraq)and Jose Anand (KCG College of Technology, India)
DOI: 10.4018/979-8-3693-9336-9.ch024
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Abstract
Traffic prediction accuracy is pivotal for fruitful urban transportation management. Existing methodologies often struggle to record complex spatio-temporal features of traffic patterns, which results in sub-optimal congestion management. This approach integrates Bayesian inference with a graph attention network (GAN) and a SARIMA model to comprehensively record complex traffic dynamics. The GAN focuses on spatial-dependencies in data, whereas SARIMA considers temporal patterns. Bayesian inference facilitates seamless integration of predictions from both models, enhancing overall forecasting accuracy. To further enhance the accuracy and efficiency of traffic prediction and management, quantum networking is integrated with the Bayesian fusion of GAN and SARIMA. To evaluate, metrics like mean absolute error, root mean squared error, and mean absolute percentage error are calculated. The research contributes to advancing traffic forecasting methodologies and holds promise for enhancing short-term traffic management strategies.
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