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 Conference Paper Faculty Publications- Conference Papers 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 Binu, Katherin; Sahni, Prem; Vidushi; Choudhary, Arun Kumar Title A name given to the resource Compressed Spatio Temporal Graph Neural Networks for Multivariate Time-Series Forecasting 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 Proceedings of 1st IEEE Uttar Pradesh Section Women in Engineering International Conference on Electrical, Electronics and Computer Engineering, UPWIECON 2025;pp.601-606 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/UPWIECON67212.2025.11390294" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/UPWIECON67212.2025.11390294</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036518485?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036518485?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 Binu K., School of Sciences, Christ University, Karnataka, Bengaluru, India; Sahni P., School of Sciences, Christ University, Karnataka, Bengaluru, India; Vidushi, School of Sciences, Christ University, Karnataka, Bengaluru, India; Choudhary A.K., Ministry of New and Renewable Energy, Government of India, New Delhi, India Description An account of the resource Precise traffic flow forecasting is crucial for efficient transportation management and traffic congestion alleviation. Existing models typically fail to process the intricate spatial- temporal relationships in traffic data and thus incur compromised prediction performance. In this work, we introduce a Compressed Spatial-Temporal Enhanced Graph Neural Network (Comp-STEMGNN) to overcome these limitations. Our model combines 1D convolution-based temporal compression with graph neural networks to compress redundant time-series information without compromising vital patterns. Graph convolutional layers and temporal convolutional blocks extract the spatial and temporal relationships and facilitate efficient learning from enormous sensor networks. Experimental comparisons on benchmark traffic datasets show that Comp-STEMGNN outperforms existing approaches in forecasting accuracy while enjoying substantial computational complexity reduction. These findings identify its potential in real-time traffic forecasting and intelligent transportation systems. 2025 IEEE. Subject The topic of the resource Comp-STEMGNN; Graph convolution; Road traffic forecasting; Spatio-temporal graph neural networks Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833156628-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online