ItemArticle

An efficient intelligent transportation system for traffic flow prediction using meta-temporal hyperbolic quantum graph neural networks

Title
An efficient intelligent transportation system for traffic flow prediction using meta-temporal hyperbolic quantum graph neural networks
Creator
Rajagopal, Manikandan; Sivasakthivel, Ramkumar; Anitha, G.; Arunachalam, Krishna Prakash; Loganathan, K.; Abbas, Mohamed; Kalathil, Shaeen; Rao, K. Srinivas
Description
Intelligent Transportation Systems (ITS) necessitate scalable, real-time, and adaptive traffic flow prediction models to enhance urban mobility and alleviate congestion. Conventional Graph Neural Network methodologies encounter difficulties in managing extensive road networks, long-range temporal relationships, and computing efficiency for real-time applications. An innovative deep learning framework named Meta Temporal Hyperbolic Quantum Graph Neural Networks that integrates hyperbolic embeddings, meta learning, quantum graph, Neural Ordinary Differential Equation (NODEs) to improve the ITS Performance. Across many cities, meta learning facilitates swift adaptation with minimum retraining whereas hyperbolic graph embeddings efficiently depict hierarchical route configurations The usage of Quantum Graph Neural Networks (QGNNs) enhances graph-based scheming, enabling real-time traffic flow to forecast for extensive networks. Also, NODEs summarize ongoing traffic progress, enhancing precision under dynamic sceneries. Datasets like Los-loop and SZ-taxi datasets are validated by experiments which highlights the impact of the proposed MTH-QGNN model, acquiringamean value RMSE of 4.5 and MAE of 3.5, ensuring minimal prediction error. MTH-QGNN model constantly sustained accuracy above 80% and R2 values exceeding 83%, representing robust predictive trustworthiness. MTH-QGNN effectively captures complex spatiotemporal traffic patterns with a variance score above threshold value. The Author(s) 2025.
Source
Scientific Reports;Volume;15;Issue;1;Article No.;27476;
Date
01-01-2025
Publisher
Nature Research
Subject
Graph neural networks (GNNs); Hyperbolic graph embeddings; Intelligent transportation systems (ITS); Meta-learning; Neural ODEs; Quantum computing; Traffic flow prediction
Coverage
Rajagopal M., Christ University, Karnataka, Bangalore, India; Sivasakthivel R., Christ University, Karnataka, Bangalore, India; Anitha G., Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Tamil Nadu, Chennai, India; Arunachalam K.P., Departamento de Ciencias de la Construcci, Facultad de Ciencias de la Construcci Ordenamiento Territorial, Universidad Tecnolica Metropolitana, Santiago, 8330383, Chile; Loganathan K., Department of Mathematics and Statistics, Manipal University Jaipur, Rajasthan, Jaipur, 303007, India; Abbas M., Central Labs, King Khalid University, P.O. Box 960, AlQuraa, Abha, Saudi Arabia, Electrical Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia; Kalathil S., Department of Electrical Engineering, College of Engineering, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Rao K.S., Department of Computer Science and Engineering, MLR Institute of Technology, Telangana, Hyderabad, India
Rights
All Open Access; Gold Open Access; Green Open Access
Relation
ISSN: 20452322;
Format
online
Language
English
Type
Article

Citation

Rajagopal, Manikandan; Sivasakthivel, Ramkumar; Anitha, G.; Arunachalam, Krishna Prakash; Loganathan, K.; Abbas, Mohamed; Kalathil, Shaeen; Rao, K. Srinivas, “An efficient intelligent transportation system for traffic flow prediction using meta-temporal hyperbolic quantum graph neural networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22523.

Output Formats