Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Rajagopal, Manikandan; Sivasakthivel, Ramkumar; Anitha, G.; Arunachalam, Krishna Prakash; Loganathan, K.; Abbas, Mohamed; Kalathil, Shaeen; Rao, K. Srinivas Title A name given to the resource An efficient intelligent transportation system for traffic flow prediction using meta-temporal hyperbolic quantum graph neural networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Scientific Reports;Volume;15;Issue;1;Article No.;27476; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1038/s41598-025-10794-5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1038/s41598-025-10794-5</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105011957938?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105011957938?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Subject The topic of the resource Graph neural networks (GNNs); Hyperbolic graph embeddings; Intelligent transportation systems (ITS); Meta-learning; Neural ODEs; Quantum computing; Traffic flow prediction Publisher An entity responsible for making the resource available Nature Research Relation A related resource ISSN: 20452322; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online