ItemConference Paper

Advancing Spatio-Temporal Predictive Modelling in Intelligent Transportation Systems: A Comprehensive Survey of Machine Learning and Deep Learning Approaches

Title
Advancing Spatio-Temporal Predictive Modelling in Intelligent Transportation Systems: A Comprehensive Survey of Machine Learning and Deep Learning Approaches
Creator
Singh, Madan; Abdullah, Azween Bin
Description
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.
Source
Lecture Notes in Networks and Systems;Volume;1906 LNNS;pp.297-311
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Deep Learning; Graph Neural Networks; Intelligent Transportation Systems; Short-term Forecasting; Spatio-temporal Modelling; Traffic Prediction
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-303220993-1;
Format
online
Language
English
Type
Conference paper

Citation

Singh, Madan; Abdullah, Azween Bin, “Advancing Spatio-Temporal Predictive Modelling in Intelligent Transportation Systems: A Comprehensive Survey of Machine Learning and Deep Learning Approaches,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/25411.

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