An efficient intelligent transportation system for traffic flow prediction using meta-temporal hyperbolic quantum...

Area of Science:

• Artificial Intelligence
• Transportation Engineering
• Data Science

Background:

• Intelligent Transportation Systems (ITS) require accurate traffic flow prediction for urban mobility.
• Conventional Graph Neural Networks struggle with scalability, long-range dependencies, and real-time efficiency in complex road networks.
• Existing models face challenges in adapting to dynamic traffic conditions and large-scale network management.

Purpose of the Study:

• To develop an innovative deep learning framework, Meta Temporal Hyperbolic Quantum Graph Neural Networks (MTH-QGNN), to enhance ITS performance.
• To address the limitations of conventional methods in real-time traffic prediction for extensive road networks.
• To improve the scalability, adaptability, and predictive accuracy of traffic flow models.

Main Methods:

• Integration of hyperbolic embeddings for hierarchical route representation.
• Application of meta-learning for rapid adaptation across diverse urban environments.
• Utilization of Quantum Graph Neural Networks (QGNNs) for efficient graph processing in large networks.
• Incorporation of Neural Ordinary Differential Equations (NODEs) to model traffic dynamics and enhance precision.

Main Results:

• The MTH-QGNN model achieved a mean RMSE of 4.5 and MAE of 3.5 on the Los-loop and SZ-taxi datasets, indicating minimal prediction error.
• Consistent accuracy above 80% and R² values exceeding 83% demonstrate robust predictive trustworthiness.
• The model effectively captures complex spatiotemporal traffic patterns, surpassing performance thresholds.

Conclusions:

• The proposed MTH-QGNN framework offers a significant advancement in real-time traffic flow prediction for Intelligent Transportation Systems.
• The integration of hyperbolic embeddings, meta-learning, QGNNs, and NODEs provides a scalable and adaptive solution for urban traffic management.
• MTH-QGNN demonstrates superior performance in accuracy and efficiency, paving the way for enhanced urban mobility.