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

Authors Xiaowei Gao , James Haworth , Dingyi Zhuang , Huanfa Chen , Xinke Jiang

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Author Details

Xiaowei Gao
  • SpaceTimeLab, University College London (UCL), UK
James Haworth
  • SpaceTimeLab, University College London (UCL), UK
Dingyi Zhuang
  • Department of Urban Studies and Planning, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
Huanfa Chen
  • The Bartlett Centre for Advanced Spatial Analysis, University College London (UCL), UK
Xinke Jiang
  • School of Computer Science, Peking University (PKU), Beijing, China

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Xiaowei Gao, James Haworth, Dingyi Zhuang, Huanfa Chen, and Xinke Jiang. Uncertainty Quantification in the Road-Level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN) (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 33:1-33:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.33

Abstract

Urban road-based risk prediction is a crucial yet challenging aspect of research in transportation safety. While most existing studies emphasize accurate prediction, they often overlook the importance of model uncertainty. In this paper, we introduce a novel Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) for road-level traffic risk prediction, with a focus on uncertainty quantification. Our case study, conducted in the Lambeth borough of London, UK, demonstrates the superior performance of our approach in comparison to existing methods. Although the negative binomial distribution may not be the most suitable choice for handling real, non-binary risk levels, our work lays a solid foundation for future research exploring alternative distribution models or techniques. Ultimately, the STZINB-GNN contributes to enhanced transportation safety and data-driven decision-making in urban planning by providing a more accurate and reliable framework for road-level traffic risk prediction and uncertainty quantification.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
Keywords
  • Traffic Risk Prediction
  • Uncertainty Quantification
  • Zero-Inflated Issues
  • Road Safety

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