Self-Supervised State Space Model for Real-Time Traffic Accident Prediction Using eKAN Networks
<|reference_start|>Self-Supervised State Space Model for Real-Time Traffic Accident Prediction Using eKAN Networks: Accurate prediction of traffic accidents across different times and regions is vital for public safety. However, existing methods face two key challenges: 1) Generalization: Current models rely heavily on manually constructed multi-view structures, like POI distributions and road network densities, which are labor-intensive and difficult to scale across cities. 2) Real-Time Performance: While some methods improve accuracy with complex architectures, they often incur high computational costs, limiting their real-time applicability. To address these challenges, we propose SSL-eKamba, an efficient self-supervised framework for traffic accident prediction. To enhance generalization, we design two self-supervised auxiliary tasks that adaptively improve traffic pattern representation through spatiotemporal discrepancy awareness. For real-time performance, we introduce eKamba, an efficient model that redesigns the Kolmogorov-Arnold Network (KAN) architecture. This involves using learnable univariate functions for input activation and applying a selective mechanism (Selective SSM) to capture multi-variate correlations, thereby improving computational efficiency. Extensive experiments on two real-world datasets demonstrate that SSL-eKamba consistently outperforms state-of-the-art baselines. This framework may also offer new insights for other spatiotemporal tasks. Our source code is publicly available at http://github.com/KevinT618/SSL-eKamba.<|reference_end|>
@article{tan2024self-supervised,
    title={Self-Supervised State Space Model for Real-Time Traffic Accident
  Prediction Using eKAN Networks},
    author={Xin Tan, Meng Zhao},
    journal={arXiv preprint arXiv:2409.05933},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05933},
    primaryClass={cs.LG}
}