BibTeX Export for Uncertainty Quantification in the Road-Level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN) (Short Paper)

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@InProceedings{gao_et_al:LIPIcs.GIScience.2023.33,
  author =	{Gao, Xiaowei and Haworth, James and Zhuang, Dingyi and Chen, Huanfa and Jiang, Xinke},
  title =	{{Uncertainty Quantification in the Road-Level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN)}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{33:1--33:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.33},
  URN =		{urn:nbn:de:0030-drops-189286},
  doi =		{10.4230/LIPIcs.GIScience.2023.33},
  annote =	{Keywords: Traffic Risk Prediction, Uncertainty Quantification, Zero-Inflated Issues, Road Safety}
}

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