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Smart city article details
| Title | Mist: Enhancing Traffic Predictions With A Mixing Spatio-Temporal Neural Network |
|---|---|
| ID_Doc | 37135 |
| Authors | He Z.; Gong M.; Zhang J.-D.; Liu X.; Chow C.-Y.; Li N.; Sun X. |
| Year | 2024 |
| Published | 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024 |
| DOI | http://dx.doi.org/10.1145/3678717.3691229 |
| Abstract | Accurately predicting traffic conditions is vital for smart city development, yet it remains challenging due to the intricate spatiotemporal dependencies in road networks. Existing works often propose intra-mixing deep learning-based prediction models for individual nodes and share parameters among them or spatial intermixing deep learning-based models for traffic predictions. However, these approaches may neglect essential principles of information exchange in traffic flow or capture useless or even erroneous spatiotemporal dependencies. To address these limitations, we propose a Mixing Spatio-Temporal neural network (MiST) for enhancing traffic predictions. In MiST, we propose (i) a temporal encoder that embeds the traffic data along with periodic features, (ii) a spatial encoder that embeds the positional information in graph and hypergraph spectral domains, as well as spatial node identities, and (iii) a mixing spatio-temporal encoder that merges the diverse features provided by the temporal and spatial encoders. Our empirical evaluations on real-world traffic prediction tasks, including flow and speed predictions, validate the superiority of MiST, underscoring its innovative contribution to traffic prediction methodologies. © 2024 Copyright held by the owner/author(s). |
| Author Keywords | Graph wavelet networks; Hypergraph wavelet networks; Mixing spatio-temporal neural networks; Traffic predictions |
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