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Open Access

A Large-Scale Spatio-Temporal Multimodal Fusion Framework for Traffic Prediction

Technical Consulting Department, Shanghai EchoBlend Internet Technology Co. Ltd., Shanghai 201111, China
Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong 999077, China
Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong 999077, China
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Abstract

Traffic prediction is crucial for urban planning and transportation management, and deep learning techniques have emerged as effective tools for this task. While previous works have made advancements, they often overlook comprehensive analyses of spatio-temporal distributions and the integration of multimodal representations. Our research addresses these limitations by proposing a large-scale spatio-temporal multimodal fusion framework that enables accurate predictions based on location queries and seamlessly integrates various data sources. Specifically, we utilize Convolutional Neural Networks (CNNs) for spatial information processing and a combination of Recurrent Neural Networks (RNNs) for final spatio-temporal traffic prediction. This framework not only effectively reveals its ability to integrate various modal data in the spatio-temporal hyperspace, but has also been successfully implemented in a real-world large-scale map, showcasing its practical importance in tackling urban traffic challenges. The findings presented in this work contribute to the advancement of traffic prediction methods, offering valuable insights for further research and application in addressing real-world transportation challenges.

Keywords

spatio-temporaltraffic predictionmultimodal fusionlearning representation

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Big Data Mining and Analytics
Volume 7 Issue 3,
September 2024
Pages 621-636
DOI: 10.26599/BDMA.2024.9020020
Cite this article:
Zhou B, Liu J, Cui S, et al. A Large-Scale Spatio-Temporal Multimodal Fusion Framework for Traffic Prediction. Big Data Mining and Analytics, 2024, 7(3): 621-636. https://doi.org/10.26599/BDMA.2024.9020020

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Received: 16 October 2023
Revised: 23 February 2024
Accepted: 22 March 2024
Published: 28 August 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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