Enhancing Spatio-Temporal Traffic Prediction through Hybrid Deep Learning Architectures and Attention Mechanisms

Aditi Singh

Enhancing Spatio-Temporal Traffic Prediction through Hybrid Deep Learning Architectures and Attention Mechanisms

Authors: Aditi Singh

Published: 2025-04-28 19:17:33.540

Abstract

Accurate and reliable traffic prediction is crucial for intelligent transportation systems (ITS), enabling proactive traffic management, route optimization, and reduced congestion. This paper presents a novel hybrid deep learning architecture that leverages the strengths of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs) enhanced with attention mechanisms for improved spatio-temporal traffic prediction. The CNNs extract spatial features from traffic data, the RNNs model the temporal dependencies, and the GNNs capture the intricate relationships within the road network. Attention mechanisms are integrated to dynamically weigh the importance of different spatial and temporal features. The proposed model is evaluated on a real-world traffic dataset, demonstrating superior performance compared to state-of-the-art methods in terms of prediction accuracy, particularly during peak hours and under varying traffic conditions. The results highlight the effectiveness of the hybrid architecture and attention mechanisms in capturing complex spatio-temporal dependencies inherent in traffic flow, contributing to more efficient and responsive ITS.

Keywords

Traffic Prediction, Spatio-Temporal Data, Deep Learning, Attention Mechanisms, Convolutional Neural Networks, Recurrent Neural Networks, Graph Neural Networks, Hybrid Models, Real-Time Analysis, Traffic Management

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How to Cite

Aditi Singh , (2025-04-28 19:17:33.540). Enhancing Spatio-Temporal Traffic Prediction through Hybrid Deep Learning Architectures and Attention Mechanisms. JANOLI International Journal of Artificial Intelligence and its Applications, Volume EOCMPeqBj5R9ZDur0Rlk, Issue 2.

ISSN: 3048-6815

JANOLI International Journal of
Artificial Intelligence and its Applications ( JIJAIA )