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.

References

[1]

Application of Subset Autoregressive Integrated Moving Average Model for Short-Term Freeway Traffic Volume Forecasting - Sangsoo Lee, Daniel B. Fambro, 1999.

[2]

Albertengo, G., and Hassan, W. Short Term Urban Traffic Forecasting Using Deep Learning. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W7 (Sept. 2018), 3--10.

[3]

Ao, B., Wang, Y., Yu, L., Brooks, R. R., and Iyengar, S. On precision bound of distributed fault-tolerant sensor fusion algorithms. ACM Computing Surveys (CSUR) 49, 1 (2016), 5.

[4]

Cai, P., Wang, Y., Lu, G., Chen, P., Ding, C., and Sun, J. A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting. Transportation Research Part C: Emerging Technologies 62 (Jan. 2016), 21--34.

[5]

Cui, Z., Ke, R., and Wang, Y. Deep Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction. 9.

[6]

Dauwels, J., Aslam, A., and et al. Predicting traffic speed in urban transportation subnetworks for multiple horizons. In 2014 International Conference on Control Automation Robotics & Vision (Singapore, Dec. 2014), IEEE, pp. 547--552.

[7]

Han, D., Chen, J., and Sun, J. A parallel spatiotemporal deep learning network for highway traffic flow forecasting. International Journal of Distributed Sensor Networks 15, 2 (Feb. 2019).

[8]

KanestrÃÿm, P. y. Traffic flow forecasting with deep learning.

[9]

Li, Y., and Shahabi, C. A brief overview of machine learning methods for short-term traffic forecasting and future directions. SIGSPATIAL Special 10, 1 (June 2018), 3--9.

Digital Library

[10]

Lv, Y., Duan, Y., Kang, W., Li, Z., and Wang, F.-Y. Traffic Flow Prediction With Big Data: A Deep Learning Approach. IEEE Transactions on Intelligent Transportation Systems 16, 2 (Apr. 2015).

[11]

Lv, Z., Xu, J., Zheng, K., Yin, H., Zhao, P., and Zhou, X. LC-RNN: A Deep Learning Model for Traffic Speed Prediction. 7.

[12]

Manoharan, S. Short Term Traffic Flow Prediction Using Deep Learning Approach. Master's thesis, Dublin, National College of Ireland, Dec. 2016.

[13]

Parnami, A., Bavi, P., Papanikolaou, D., Akella, S., Lee, M., and Krishnan, S. Deep Learning Based Urban Analytics Platform: Applications to Traffic Flow Modeling and Prediction. 9.

[14]

Polson, N. G., and Sokolov, V. O. Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies 79 (June 2017), 1--17.

[15]

Shafqat, W., Malik, S., Byun, Y.-c., and Kim, D.-H. A Short-Term Traffic Flow Prediction Based on Recurrent Neural Networks for Road Transportation Control in ITS. 5.

[16]

Su, H., Zhang, L., and Yu, S. Short-term Traffic Flow Prediction Based on Incremental Support Vector Regression. In Third International Conference on Natural Computation (ICNC 2007) (Aug. 2007), vol. 1, pp. 640--645.

[17]

Sun, T., Wang, Y., Li, D., Gu, Z., and Xu, J. Wcs: Weighted component stitching for sparse network localization. IEEE/ACM Transactions on Networking (TON) 26, 5 (2018), 2242--2253.

[18]

Wang, P.-w., Yu, H.-b., Xiao, L., and Wang, L. Online Traffic Condition Evaluation Method for Connected Vehicles Based on Multisource Data Fusion, 2017.

[19]

Wang, Y., Sun, T., Rao, G., and Li, D. Formation tracking in sparse airborne networks. IEEE Journal on Selected Areas in Communications 36, 9 (2018), 2000--2014.

Digital Library

[20]

Wu, Y., Tan, H., Qin, L., Ran, B., and Jiang, Z. A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C: Emerging Technologies 90 (May 2018), 166--180.

[21]

Yang, G., Wang, Y., Yu, H., Ren, Y., and Xie, J. Short-Term Traffic State Prediction Based on the Spatiotemporal Features of Critical Road Sections. Sensors (Basel, Switzerland) 18, 7 (July 2018).

[22]

Yisheng, L., Duan, Y., Kang, W., Li, Z., and Wang, F.-Y. Traffic Flow Prediction With Big Data: A Deep Learning Approach. IEEE Transactions on Intelligent Transportation Systems 16 (Jan. 2014), 865--873.

[23]

Zhang, D., and Kabuka, M. R. Combining Weather Condition Data to Predict Traffic Flow: A GRU Based Deep Learning Approach. In 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing (Mar. 2018), pp. 1216--1219.

[24]

Zhang, J., Zheng, Y., Sun, J., and Qi, D. Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning. IEEE Transactions on Knowledge and Data Engineering (2019), 1--1.

[25]

Zhang, S., Yao, Y., Hu, J., Zhao, Y., Li, S., and Hu, J. Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks. Sensors (Basel, Switzerland) 19, 10 (May 2019).

[26]

Zhang, W., Yu, Y., Qi, Y., Shu, F., and Wang, Y. Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning. Transportmetrica A: Transport Science 15, 2 (Nov. 2019), 1688--1711.

[27]

Zheng, Z., Yang, Y., Liu, J., Dai, H., and Zhang, Y. Deep and Embedded Learning Approach for Traffic Flow Prediction in Urban Informatics. IEEE Transactions on Intelligent Transportation Systems (2019), 1--13.

[28]

Zou, Z., Gao, P., and Yao, C. City-Level Traffic Flow Prediction via LSTM Networks. ICAIP '18, ACM, pp. 149--153. event-place: Chengdu, China.

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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
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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
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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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New York, NY, United States

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
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
https://dl.acm.org/doi/10.1109/TITS.2023.3252029
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
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Zheng GChai WDuanmu JKatos V(2023)Hybrid deep learning models for traffic prediction in large-scale road networksInformation Fusion10.1016/j.inffus.2022.11.01992:C(93-114)Online publication date: 1-Apr-2023
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