The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning
Abstract
Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging because of two aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between locations along with temporal correlations among timestamps; 2) diversity of such spatio-temporal correlations, which vary from location to location and depend on the surrounding geographical information, e.g., points of interests and road networks. To tackle these challenges, we proposed a deep-meta-learning based model, entitled ST-MetaNet, to collectively predict traffic in all location at once. ST-MetaNet employs a sequence-to-sequence architecture, consisting of an encoder to learn historical information and a decoder to make predictions step by step. In specific, the encoder and decoder have the same network structure, consisting of a recurrent neural network to encode the traffic, a meta graph attention network to capture diverse spatial correlations, and a meta recurrent neural network to consider diverse temporal correlations. Extensive experiments were conducted based on two real-world datasets to illustrate the effectiveness of ST-MetaNet beyond several state-of-the-art methods.
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Cites
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Self-citationScheduled Sampling for Sequence Prediction with Recurrent Neural Networks
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Urban Computing: Concepts, Methodologies, and Applications
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Self-citationDeep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
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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
Jin, M., Koh, H. Y., Wen, Q., Zambon, D., Alippi, C., Webb, G. I., King, I., & Pan, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3443141 · Cited by 567
Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution
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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
Metadata
- Authors
- Pan, Z., Liang, Y., Wang, W., Yu, Y., Zheng, Y., & Zhang, J.
- Venue
- Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2019-07-25
- Pages
- 1720-1730
- OpenAlex
- W2950817888
- Cited by
- 559
- References
- 23
- FWCI
- 105.01
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Pan, Z., Liang, Y., Wang, W., Yu, Y., Zheng, Y., & Zhang, J. (2019). Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3292500.3330884
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