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 Singh, Madan; Abdullah, Azween Bin Title A name given to the resource Advancing Spatio-Temporal Predictive Modelling in Intelligent Transportation Systems: A Comprehensive Survey of Machine Learning and Deep Learning Approaches Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1906 LNNS;pp.297-311 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-20994-8_25" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-20994-8_25</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039028081?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039028081?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 Singh M., Christ (Deemed to be University), Delhi-NCR Campus, Uttar Pradesh, Ghaziabad, 201003, India, Faculty of Computing and Digital Technology, HELP University, Jalan Semantan, Bukit Damansara, Kuala Lumpur, 50490, Malaysia; Abdullah A.B., Christ (Deemed to be University), Delhi-NCR Campus, Uttar Pradesh, Ghaziabad, 201003, India, Faculty of Computing and Digital Technology, HELP University, Jalan Semantan, Bukit Damansara, Kuala Lumpur, 50490, Malaysia Description An account of the resource In an effort to alleviate traffic and improve urban mobility, intelligent transportation systems (ITS) relies heavily on forecasting traffic. In the paper, a comprehensive survey on spatial temporal predictive modelling techniques for forecasting traffic has been presented. The focus remains on advanced machine learning and deep learning that have been developed between 2017 and 2025. With the use of state of the art technologies to forecast both in real time scenarios (short-term) traffic prediction and long term forecasting, such as transformer based models, (RNN) recurrent neural networks, convolutional networks on grids and graphs, and (GNN) graph neural networks. Former approaches were examined for strengths and limitation to capture intricate temporal dynamics and spatial interdependencies. Through the above findings, a brand-new conceptual methodology that associates attention mechanisms and graph-based learning to increase prediction accuracy with computing efficiency has been proposed. The performance improvements of newer methods over the conventional methods are also shown through a comparison of the experimental findings. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Deep Learning; Graph Neural Networks; Intelligent Transportation Systems; Short-term Forecasting; Spatio-temporal Modelling; Traffic Prediction Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303220993-1; 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