Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data

@article{Dai2020HybridSG,
  title={Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data},
  author={Rui Dai and Shenkun Xu and Qian Gu and Chenguang Ji and Kaikui Liu},
  journal={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  year={2020}
}
  • Rui Dai, Shenkun Xu, +2 authors Kaikui Liu
  • Published 2020
  • Computer Science, Mathematics
  • Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Traffic forecasting has recently attracted increasing interest due to the popularity of online navigation services, ridesharing and smart city projects. Owing to the non-stationary nature of road traffic, forecasting accuracy is fundamentally limited by the lack of contextual information. To address this issue, we propose the Hybrid Spatio-Temporal Graph Convolutional Network (H-STGCN), which is able to "deduce" future travel time by exploiting the data of upcoming traffic volume. Specifically… Expand
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