The DOI matches its Crossref record; 73 of the 73 references it cites were found in a registry or an index.
A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges
Open accessAbstract
In this modern era, traffic congestion has become a major source of severe negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate traffic congestion is through future traffic prediction. The research field of traffic prediction has evolved greatly ever since its inception in the late 70s. Earlier studies mainly use classical statistical models such as ARIMA and its variants. Recently, researchers have started to focus on machine learning models because of their power and flexibility. As theoretical and technological advances emerge, we enter the era of deep neural network, which gained popularity due to its sheer prediction power which can be attributed to the complex and deep structure. Despite the popularity of deep neural network models in the field of traffic prediction, literature surveys of such methods are rare. In this work, we present an up-to-date survey of deep neural network for traffic prediction. We will provide a detailed explanation of popular deep neural network architectures commonly used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field.
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Cites
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GAN（Generative Adversarial Nets）
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Show and tell: A neural image caption generator
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Learning to forget: Continual prediction with LSTM
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Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Yu, B., Yin, H., & Zhu, Z. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/505 · Cited by 3,728
Traffic Flow Prediction With Big Data: A Deep Learning Approach
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Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction
Zhang, J., Zheng, Y., & Qi, D. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10735 · Cited by 2,282
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Cited by
Graph neural network for traffic forecasting: A survey
Jiang, W., & Luo, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117921 · Cited by 1,316
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
Jiang, J., Han, C., Zhao, W. X., & Wang, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i4.25556 · Cited by 632
A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation
Yuan, H., & Li, G. (2021) · Data Science and Engineering · doi:10.1007/s41019-020-00151-z · Cited by 389
Shaygan, M., Meese, C., Li, W., Zhao, X. (., & Nejad, M. (2022) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2022.103921 · Cited by 267
Metadata
- Authors
- Tedjopurnomo, D. A., Bao, Z., Zheng, B., Choudhury, F., & Qin, A. K.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2020
- Pages
- 1-1
- ISSN
- 1041-4347 (ISSN-L)
- 1558-2191
- 2326-3865
- OpenAlex
- W3034944009
- Cited by
- 403
- References
- 73
- FWCI
- 21.74
- Type
- journal article
- Language
- en
- Open access
- green
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- Tedjopurnomo, D. A., Bao, Z., Zheng, B., Choudhury, F., & Qin, A. K. (2020). A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/tkde.2020.3001195
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