Document Open Access Logo

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

PDF
Thumbnail PDF

File

LIPIcs.GIScience.2023.33.pdf
  • Filesize: 4.45 MB
  • 6 pages

Document Identifiers

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

Cite AsGet BibTex

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

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

References

  1. Rodrigo de Medrano and José L Aznarte. A new spatio-temporal neural network approach for traffic accident forecasting. Applied Artificial Intelligence, 35(10):782-801, 2021. Google Scholar
  2. Genwang Liu, Haolin Jin, Jiaze Li, Xianbiao Hu, and Jian Li. A bayesian deep learning method for freeway incident detection with uncertainty quantification. Accident Analysis & Prevention, 176:106796, 2022. Google Scholar
  3. Alameen Najjar, Shun’ichi Kaneko, and Yoshikazu Miyanaga. Combining satellite imagery and open data to map road safety. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31, 2017. Google Scholar
  4. Weizhu Qian, Dalin Zhang, Yan Zhao, Kai Zheng, and James JQ Yu. Uncertainty quantification for traffic forecasting: A unified approach. arXiv preprint arXiv:2208.05875, 2022. Google Scholar
  5. Honglei Ren, You Song, Jingwen Wang, Yucheng Hu, and Jinzhi Lei. A deep learning approach to the citywide traffic accident risk prediction. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 3346-3351. IEEE, 2018. Google Scholar
  6. Qingyi Wang, Shenhao Wang, Dingyi Zhuang, Haris Koutsopoulos, and Jinhua Zhao. Uncertainty quantification of spatiotemporal travel demand with probabilistic graph neural networks. arXiv preprint arXiv:2303.04040, 2023. Google Scholar
  7. Yuankai Wu, Dingyi Zhuang, Aurelie Labbe, and Lijun Sun. Inductive graph neural networks for spatiotemporal kriging. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 4478-4485, 2021. Google Scholar
  8. Yang Zhang, Xiangyu Dong, Lanyu Shang, Daniel Zhang, and Dong Wang. A multi-modal graph neural network approach to traffic risk forecasting in smart urban sensing. In 2020 17th Annual IEEE international conference on sensing, communication, and networking (SECON), pages 1-9. IEEE, 2020. Google Scholar
  9. Zhengyang Zhou, Yang Wang, Xike Xie, Lianliang Chen, and Hengchang Liu. Riskoracle: a minute-level citywide traffic accident forecasting framework. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 1258-1265, 2020. Google Scholar
  10. Zhengyang Zhou, Yang Wang, Xike Xie, Lianliang Chen, and Chaochao Zhu. Foresee urban sparse traffic accidents: A spatiotemporal multi-granularity perspective. IEEE Transactions on Knowledge and Data Engineering, 34(8):3786-3799, 2020. Google Scholar
  11. Zhengyang Zhou, Yang Wang, Xike Xie, Lei Qiao, and Yuantao Li. Stuanet: Understanding uncertainty in spatiotemporal collective human mobility. In Proceedings of the Web Conference 2021, pages 1868-1879, 2021. Google Scholar
  12. Dingyi Zhuang, Shenhao Wang, Haris Koutsopoulos, and Jinhua Zhao. Uncertainty quantification of sparse travel demand prediction with spatial-temporal graph neural networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 4639-4647, 2022. Google Scholar
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact