research-article

A Survey of Hybrid Deep Learning Methods for Traffic Flow Prediction

Authors:

Yongcai WangAuthors Info & Claims

ICAIP '19: Proceedings of the 2019 3rd International Conference on Advances in Image Processing

Pages 133 - 138

https://doi.org/10.1145/3373419.3373429

Published: 24 January 2020 Publication History

Abstract

Traffic flow prediction using big data and deep learning attracts great attentions in recent years. Researchers show that DNN models can provide better traffic prediction accuracy than the traditional shallow models. Since the traffic flow reveals both spatial and temporal dependency characteristics, and may be impacted by weather, social event data etc., therefore, a set of hybrid DNN models have been presented recently in literature for further improving the traffic flow prediction performances. The hybrid models can capture dependency in multi-dimension and show better prediction performances than simple DNN models. This paper presents a thorough review and comparison of hybrid deep learning models for traffic flow prediction. We review the data sources used in hybrid deep learning and the various hybrid deep learning models built for trafficc flow prediction. The benefits of using hybrid models are summarized.

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Garcia ECalvet LCarracedo PSerrat CMiró PPeyman M(2024)Predictive Analyses of Traffic Level in the City of Barcelona: From ARIMA to eXtreme Gradient BoostingApplied Sciences10.3390/app1411443214:11(4432)Online publication date: 23-May-2024
https://doi.org/10.3390/app14114432
Jia WZhao SZhao K(2024)Human Mobility Prediction Based on Trend Iteration of Spectral ClusteringIEEE Transactions on Mobile Computing10.1109/TMC.2023.328813223:5(4196-4211)Online publication date: May-2024
https://doi.org/10.1109/TMC.2023.3288132
Liu YRasouli SWong MFeng THuang T(2024)RT-GCNInformation Fusion10.1016/j.inffus.2023.102078102:COnline publication date: 1-Feb-2024
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Index Terms

A Survey of Hybrid Deep Learning Methods for Traffic Flow Prediction
1. Networks
  1. Network services
    1. Network monitoring

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ICAIP '19: Proceedings of the 2019 3rd International Conference on Advances in Image Processing

November 2019

232 pages

ISBN:9781450376754

DOI:10.1145/3373419

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Southwest Jiaotong University

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Publication History

Published: 24 January 2020

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National Natural Science Foundation of China

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ICAIP 2019

ICAIP 2019: 2019 3rd International Conference on Advances in Image Processing

November 8 - 10, 2019

Chengdu, China

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Cited By

Garcia ECalvet LCarracedo PSerrat CMiró PPeyman M(2024)Predictive Analyses of Traffic Level in the City of Barcelona: From ARIMA to eXtreme Gradient BoostingApplied Sciences10.3390/app1411443214:11(4432)Online publication date: 23-May-2024
https://doi.org/10.3390/app14114432
Jia WZhao SZhao K(2024)Human Mobility Prediction Based on Trend Iteration of Spectral ClusteringIEEE Transactions on Mobile Computing10.1109/TMC.2023.328813223:5(4196-4211)Online publication date: May-2024
https://doi.org/10.1109/TMC.2023.3288132
Liu YRasouli SWong MFeng THuang T(2024)RT-GCNInformation Fusion10.1016/j.inffus.2023.102078102:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.inffus.2023.102078
Fang HChen CHwang FChang CChang C(2024)Metro Station functional clustering and dual-view recurrent graph convolutional network for metro passenger flow predictionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122550247:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122550
Chauhan NKumar N(2024)Confined attention mechanism enabled Recurrent Neural Network framework to improve traffic flow predictionEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108791136(108791)Online publication date: Oct-2024
https://doi.org/10.1016/j.engappai.2024.108791
Almukhalfi HNoor ANoor T(2024)Traffic management approaches using machine learning and deep learning techniques: A surveyEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108147133(108147)Online publication date: Jul-2024
https://doi.org/10.1016/j.engappai.2024.108147
Zheng LCao YJiang RTaura KShen YLi SYoshikawa M(2024)Enhancing Privacy of Spatiotemporal Federated Learning Against Gradient Inversion AttacksDatabase Systems for Advanced Applications10.1007/978-981-97-5552-3_31(457-473)Online publication date: 1-Oct-2024
https://doi.org/10.1007/978-981-97-5552-3_31
Zhang XWang QYe ZYing HYu D(2023)Federated Representation Learning With Data Heterogeneity for Human Mobility PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.325202924:6(6111-6122)Online publication date: 1-Jun-2023
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Zaman MSaha SAbdelwahed S(2023)Assessing the Suitability of Different Machine Learning Approaches for Smart Traffic Mobility2023 IEEE Transportation Electrification Conference & Expo (ITEC)10.1109/ITEC55900.2023.10186901(1-6)Online publication date: 21-Jun-2023
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Fontana ILangheinrich MGjoreski M(2023)GANs for Privacy-Aware Mobility ModelingIEEE Access10.1109/ACCESS.2023.326098111(29250-29262)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3260981
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