The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis
Open accessAbstract
Crowd flow prediction is of great importance in a wide range of applications from urban planning, traffic control to public safety. It aims to predict the inflow (the traffic of crowds entering a region in a given time interval) and outflow (the traffic of crowds leaving a region for other places) of each region in the city with knowing the historical flow data. In this paper, we propose DeepSTN+, a deep learning-based convolutional model, to predict crowd flows in the metropolis. First, DeepSTN+ employs the ConvPlus structure to model the longrange spatial dependence among crowd flows in different regions. Further, PoI distributions and time factor are combined to express the effect of location attributes to introduce prior knowledge of the crowd movements. Finally, we propose an effective fusion mechanism to stabilize the training process, which further improves the performance. Extensive experimental results based on two real-life datasets demonstrate the superiority of our model, i.e., DeepSTN+ reduces the error of the crowd flow prediction by approximately 8%∼13% compared with the state-of-the-art baselines.
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Cites
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Going deeper with convolutions
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction
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Cited by
Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution
Li, F., Feng, J., Yan, H., Jin, G., Yang, F., Sun, F., Jin, D., & Li, Y. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3532611 · Cited by 555
Self-citationYuan, X., Chen, J., Yang, J., Zhang, N., Yang, T., Han, T., & Taherkordi, A. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3157056 · Cited by 178
Fine-Grained Urban Flow Prediction
Liang, Y., Ouyang, K., Sun, J., Wang, Y., Zhang, J., Zheng, Y., Rosenblum, D., & Zimmermann, R. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3449792 · Cited by 89
A Privacy-Preserving Crowd Flow Prediction Framework Based on Federated Learning during Epidemics
Wang, W., Yang, G., Bao, L., Ma, K., & Zhou, H. (2022) · Security and Communication Networks · doi:10.1155/2022/8712597 · Cited by 5
Metadata
- Authors
- Lin, Z., Feng, J., Lu, Z., Li, Y., & Jin, D.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2019-07-17
- Volume
- 33
- Issue
- 01
- Pages
- 1020-1027
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2904813135
- Cited by
- 268
- References
- 20
- FWCI
- 53.15
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Lin, Z., Feng, J., Lu, Z., Li, Y., & Jin, D. (2019). DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v33i01.33011020
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