Early Forecast of Traffic Accident Impact Based on a Single-Snapshot Observation (Student Abstract)

Guangyu Meng, Qisheng Jiang, Kaiqun Fu, Beiyu Lin, Chang-Tien Lu, Zhiqian Chen

AAAI 2022 pp. 13015-13016

doi:10.1609/AAAI.V36I11.21644 /aaai/2022/meng2022aaai-early/

Abstract

Predicting and quantifying the impact of traffic accidents is necessary and critical to Intelligent Transport Systems (ITS). As a state-of-the-art technique in graph learning, current graph neural networks heavily rely on graph Fourier transform, assuming homophily among the neighborhood. However, the homophily assumption makes it challenging to characterize abrupt signals such as traffic accidents. Our paper proposes an abrupt graph wavelet network (AGWN) to model traffic accidents and predict their time durations using only one single snapshot.

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Cite

Text

Meng et al. "Early Forecast of Traffic Accident Impact Based on a Single-Snapshot Observation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21644

Markdown

[Meng et al. "Early Forecast of Traffic Accident Impact Based on a Single-Snapshot Observation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/meng2022aaai-early/) doi:10.1609/AAAI.V36I11.21644

BibTeX

@inproceedings{meng2022aaai-early,
  title     = {{Early Forecast of Traffic Accident Impact Based on a Single-Snapshot Observation (Student Abstract)}},
  author    = {Meng, Guangyu and Jiang, Qisheng and Fu, Kaiqun and Lin, Beiyu and Lu, Chang-Tien and Chen, Zhiqian},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {13015-13016},
  doi       = {10.1609/AAAI.V36I11.21644},
  url       = {https://mlanthology.org/aaai/2022/meng2022aaai-early/}
}