A Review of Research on Traffic Flow Prediction Methods Based on Deep Learning
Pages 166 - 170
Abstract
On the basis of collating and analyzing the previous literature on traffic flow prediction based on deep learning methods, a total of 982 pieces of literature from March 2014 to March 2024 were analyzed by CiteSpace software, and the visual analysis of the number of publications, authors, literature sources, research institutions, as well as keyword co-occurrences, timelines, and highlighted words were carried out. The study shows that from 2018, scholars' research literature on traffic flow prediction has shown a surge in annual publication volume, and prediction methods from LSTM to CNN to graph convolutional networks, and prediction durations from short-time prediction to long-time prediction have been studied in depth. Finally, the future research direction is predicted by the highlighting of keywords.
References
[1]
C. A. X. Sun, H. Zhang, and P. X. Yin, “Machine learning and IoTs for forecasting prediction of smart road traffic flow,” Soft Computing, vol. 27, no. 1, pp. 323-335, Jan, 2023.
[2]
A. Boukerche, and J. H. Wang, “Machine Learning-based traffic prediction models for Intelligent Transportation Systems,” Computer Networks, vol. 181, pp. 21, Nov, 2020.
[3]
J. H. Zheng, and M. F. Huang, “Traffic Flow Forecast Through Time Series Analysis Based on Deep Learning,” Ieee Access, vol. 8, pp. 82562-82570, 2020.
[4]
K. H. Poon, P. K. Y. Wong, and J. C. P. Cheng, “Long-time gap crowd prediction using time series deep learning models with two-dimensional single attribute inputs,” Advanced Engineering Informatics, vol. 51, pp. 14, Jan, 2022.
[5]
Y. X. Bao, J. S. Huang, Q. Q. Shen, Y. Cao, W. P. Ding, Z. Q. Shi, and Q. Shi, “Spatial-Temporal Complex Graph Convolution Network for Traffic Flow Prediction,” Engineering Applications of Artificial Intelligence, vol. 121, pp. 16, May, 2023.
[6]
Y. S. Gong, Z. B. Li, J. Zhang, W. Liu, and Y. Zheng, “Online Spatio-Temporal Crowd Flow Distribution Prediction for Complex Metro System,” Ieee Transactions on Knowledge and Data Engineering, vol. 34, no. 2, pp. 865-880, Feb, 2022.
[7]
J. X. Yao, and Y. T. Ye, “The effect of image recognition traffic prediction method under deep learning and naive Bayes algorithm on freeway traffic safety,” Image and Vision Computing, vol. 103, pp. 8, Nov, 2020.
[8]
R. Wang, M. F. Li, Q. W. Guo, Y. P. Xiao, and Z. Y. Yang, “Road network pixelization: A traffic flow imputation method based on image restoration techniques,” Expert Systems with Applications, vol. 237, pp. 15, Mar, 2024.
[9]
S. Reza, H. S. Oliveira, J. J. M. Machado, and J. Tavares, “Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System,” Sensors, vol. 21, no. 22, pp. 36, Nov, 2021.
[10]
X. Ma, Z. Dai, Z. He, J. Ma, and Y. Wang, “Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction,” Sensors, vol. 17, no. 4, pp. 818, 2017.
[11]
K. Ramana, G. Srivastava, M. R. Kumar, T. R. Gadekallu, J. C. W. Lin, M. Alazab, and C. Iwendi, “A Vision Transformer Approach for Traffic Congestion Prediction in Urban Areas,” Ieee Transactions on Intelligent Transportation Systems, vol. 24, no. 4, pp. 3922-3934, Apr, 2023.
[12]
X. Zhou, R. M. Ke, H. Yang, and C. X. Liu, “When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges,” Applied Sciences-Basel, vol. 11, no. 20, pp. 28, Oct, 2021.
[13]
M. Zahid, Y. Z. Chen, A. Jamal, and M. Q. Memon, “Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers,” Sensors, vol. 20, no. 3, pp. 22, Feb, 2020.
[14]
M. Anjaneyulu, and M. Kubendiran, “Short Term Traffic Flow Prediction Using Hybrid Deep Learning,” Cmc-Computers Materials & Continua, vol. 75, no. 1, pp. 1641-1656, 2023.
[15]
M. Méndez, M. G. Merayo, and M. Núñez, “Long-term traffic flow forecasting using a hybrid CNN-BiLSTM model,” Engineering Applications of Artificial Intelligence, vol. 121, 2023.
[16]
Z. M. Wang, X. Su, and Z. M. Ding, “Long-Term Traffic Prediction Based on LSTM Encoder-Decoder Architecture,” Ieee Transactions on Intelligent Transportation Systems, vol. 22, no. 10, pp. 6561-6571, Oct, 2021.
[17]
X. Y. Qi, G. Mei, J. Z. Tu, N. Xi, and F. Piccialli, “A Deep Learning Approach for Long-Term Traffic Flow Prediction With Multifactor Fusion Using Spatiotemporal Graph Convolutional Network,” Ieee Transactions on Intelligent Transportation Systems, vol. 24, no. 8, pp. 8687-8700, Aug, 2023.
[18]
E. Dogan, “ANALYSIS AND COMPARISON OF LONG SHORT-TERM MEMORY NETWORKS SHORT-TERM TRAFFIC PREDICTION PERFORMANCE,” Scientific Journal of Silesian University of Technology-Series Transport, vol. 107, pp. 19-32, 2020.
[19]
Y. Zhang, and D. R. Xin, “Dynamic Optimization Long Short-Term Memory Model Based on Data Preprocessing for Short-Term Traffic Flow Prediction,” Ieee Access, vol. 8, pp. 91510-91520, 2020.
[20]
S. Reza, M. C. Ferreira, J. J. M. Machado, and J. Tavares, “Traffic State Prediction Using One-Dimensional Convolution Neural Networks and Long Short-Term Memory,” Applied Sciences-Basel, vol. 12, no. 10, pp. 18, May, 2022.
[21]
Du Fei, "Research on Short-Term Traffic Flow Prediction Based on IGA-LSTM," M.S., 2021.
[22]
Wang, Chu, "Research on long-time traffic flow prediction method based on spatio-temporal graph neural network," M.S., 2023.
[23]
S. Y. Xie, "Research on Traffic Flow Prediction Based on Convolutional Neural Networks with Two-Way Multi-Scale Spatio-Temporal Maps," M.S., 2022.
[24]
A. Abdelraouf, M. Abdel-Aty, and N. Mahmoud, “Sequence-to-Sequence Recurrent Graph Convolutional Networks for Traffic Estimation and Prediction Using Connected Probe Vehicle Data,” Ieee Transactions on Intelligent Transportation Systems, vol. 24, no. 1, pp. 1395-1405, Jan, 2023.
[25]
M. M. Chang, Z. M. Ding, Z. L. Zhao, and Z. Cai, “Heterogeneous Modular Traffic Prediction Based on Multilayer Graph Convolutional Network,” Ieee Transactions on Intelligent Transportation Systems, pp. 13, 2024 Feb, 2024.
[26]
Y. Chen, and X. Q. Chen, “A novel reinforced dynamic graph convolutional network model with data imputation for network-wide traffic flow prediction,” Transportation Research Part C-Emerging Technologies, vol. 143, pp. 17, Oct, 2022.
[27]
Y. Zhao, M. X. Li, H. Y. Wen, and S. X. Wen, “Dual flow fusion graph convolutional network for traffic flow prediction,” International Journal of Machine Learning and Cybernetics, pp. 13, 2024 Mar, 2024.
[28]
Baolin Ye, Benao Dai, Mingjian Zhang, Huimin Gao, and Weimin Wu, "A Review of Traffic Flow Prediction Methods Based on Graph Convolutional Networks," Journal of Nanjing University of Information Engineering (Natural Science Edition), pp. 1-26, 2023.
[29]
Yan Xu, Xiaoliang Fan, and Longbiao Chen, "An algorithm for urban traffic posture prediction based on graph convolutional neural networks," Journal of Zhejiang University (Engineering Edition), vol. 54, no. 06, pp. 1147-1155, 2020.
[30]
M. Al Duhayyim, A. A. Albraikan, F. N. Al-Wesabi, H. M. Burbur, M. Alamgeer, A. M. Hilal, M. A. Hamza, and M. Rizwanullah, “Modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions,” Cmc-Computers Materials & Continua, vol. 71, no. 2, pp. 3953-3968, 2022.
[31]
H. F. Al-Selwi, A. A. Aziz, F. Bin Abas, A. Kayani, and N. M. Noor, “Attention Based Spatial-Temporal GCN with Kalman filter for Traffic Flow Prediction,” International Journal of Technology, vol. 14, no. 6, pp. 1299-1308, Oct, 2023.
[32]
A. Boukerche, and J. H. Wang, “A performance modeling and analysis of a novel vehicular traffic flow prediction system using a hybrid machine learning-based model,” Ad Hoc Networks, vol. 106, pp. 10, Sep, 2020.
[33]
L. L. Chen, L. B. Chen, and H. Zhang, “Traffic Flow Prediction Based on Interactive Dynamic Spatio-Temporal Graph Convolution with a Probabilistic Sparse Attention Mechanism,” Transportation Research Record, pp. 17, 2024 Feb, 2024.
[34]
Q. Chen, W. Wang, X. Huang, and H. N. Liang, “Attention-based Recurrent Neural Network for Traffic Flow Prediction,” Journal of Internet Technology, vol. 21, no. 3, pp. 831-839, 2020.
[35]
W. B. Du, S. W. Chen, Z. S. Li, X. B. Cao, and Y. S. Lv, “A Spatial-Temporal Approach for Multi-Airport Traffic Flow Prediction Through Causality Graphs,” Ieee Transactions on Intelligent Transportation Systems, pp. 13, 2023 Sep, 2023.
Index Terms
- A Review of Research on Traffic Flow Prediction Methods Based on Deep Learning
Recommendations
Traffic Flow Prediction With Big Data: A Deep Learning Approach
Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. ...
A Survey of Hybrid Deep Learning Methods for Traffic Flow Prediction
ICAIP '19: Proceedings of the 2019 3rd International Conference on Advances in Image ProcessingTraffic 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 ...
Vessel Traffic Flow Prediction Using LSTM Encoder-Decoder
SPML '22: Proceedings of the 2022 5th International Conference on Signal Processing and Machine LearningAccurate vessel traffic flow prediction is of vital indispensable for the enhancement of capability of navigation, the optimal allocation of port resources and the improvement of the navigation safety. In order to improve the accuracy of vessel traffic ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
May 2024
514 pages
ISBN:9798400709838
DOI:10.1145/3677892
Copyright © 2024 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 the author(s) 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].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 26 August 2024
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
DSAI 2024
DSAI 2024: 2024 International Conference on Digital Society and Artificial Intelligence
May 24 - 26, 2024
Qingdao, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 8Total Downloads
- Downloads (Last 12 months)8
- Downloads (Last 6 weeks)8
Reflects downloads up to 20 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format