Spatiotemporal Correlation Modelling for Machine Learning-based Traffic State Predictions: State-of-the-art and Beyond

Spatiotemporal Correlation Modelling for Machine Learning-based Traffic State Predictions: State-of-the-art and Beyond

10.1080/01441647.2023.2171151

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Bibliographic Details
Main Authors: Haipeng Cui, Qiang Meng, Teng Teck-Hou, Xiaobo Yang
Other Authors: CIVIL AND ENVIRONMENTAL ENGINEERING
Format: Article
Published: Taylor & Francis 2023
Online Access:https://scholarbank.nus.edu.sg/handle/10635/242100
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Institution: National University of Singapore
id sg-nus-scholar.10635-242100
record_format dspace
spelling sg-nus-scholar.10635-2421002024-04-03T08:57:39Z Spatiotemporal Correlation Modelling for Machine Learning-based Traffic State Predictions: State-of-the-art and Beyond Haipeng Cui Qiang Meng Teng Teck-Hou Xiaobo Yang CIVIL AND ENVIRONMENTAL ENGINEERING 10.1080/01441647.2023.2171151 Transport Reviews 43 4 780-804 2023-06-19T05:12:19Z 2023-06-19T05:12:19Z 2023-01-31 Article Haipeng Cui, Qiang Meng, Teng Teck-Hou, Xiaobo Yang (2023-01-31). Spatiotemporal Correlation Modelling for Machine Learning-based Traffic State Predictions: State-of-the-art and Beyond. Transport Reviews 43 (4) : 780-804. ScholarBank@NUS Repository. https://doi.org/10.1080/01441647.2023.2171151 0144-1647 https://scholarbank.nus.edu.sg/handle/10635/242100 Taylor & Francis Taylor & Francis
institution National University of Singapore
building NUS Library
continent Asia
country Singapore
Singapore
content_provider NUS Library
collection ScholarBank@NUS
description 10.1080/01441647.2023.2171151
author2 CIVIL AND ENVIRONMENTAL ENGINEERING
author_facet CIVIL AND ENVIRONMENTAL ENGINEERING
Haipeng Cui
Qiang Meng
Teng Teck-Hou
Xiaobo Yang
format Article
author Haipeng Cui
Qiang Meng
Teng Teck-Hou
Xiaobo Yang
spellingShingle Haipeng Cui
Qiang Meng
Teng Teck-Hou
Xiaobo Yang
Spatiotemporal Correlation Modelling for Machine Learning-based Traffic State Predictions: State-of-the-art and Beyond
author_sort Haipeng Cui
title Spatiotemporal Correlation Modelling for Machine Learning-based Traffic State Predictions: State-of-the-art and Beyond
title_short Spatiotemporal Correlation Modelling for Machine Learning-based Traffic State Predictions: State-of-the-art and Beyond
title_full Spatiotemporal Correlation Modelling for Machine Learning-based Traffic State Predictions: State-of-the-art and Beyond
title_fullStr Spatiotemporal Correlation Modelling for Machine Learning-based Traffic State Predictions: State-of-the-art and Beyond
title_full_unstemmed Spatiotemporal Correlation Modelling for Machine Learning-based Traffic State Predictions: State-of-the-art and Beyond
title_sort spatiotemporal correlation modelling for machine learning-based traffic state predictions: state-of-the-art and beyond
publisher Taylor & Francis
publishDate 2023
url https://scholarbank.nus.edu.sg/handle/10635/242100
_version_ 1795375016182808576

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