Predicting trucking accidents with truck drivers 'safety climate perception across companies: A transfer learning approach
<|reference_start|>Predicting trucking accidents with truck drivers 'safety climate perception across companies: A transfer learning approach: There is a rising interest in using artificial intelligence (AI)-powered safety analytics to predict accidents in the trucking industry. Companies may face the practical challenge, however, of not having enough data to develop good safety analytics models. Although pretrained models may offer a solution for such companies, existing safety research using transfer learning has mostly focused on computer vision and natural language processing, rather than accident analytics. To fill the above gap, we propose a pretrain-then-fine-tune transfer learning approach to help any company leverage other companies' data to develop AI models for a more accurate prediction of accident risk. We also develop SafeNet, a deep neural network algorithm for classification tasks suitable for accident prediction. Using the safety climate survey data from seven trucking companies with different data sizes, we show that our proposed approach results in better model performance compared to training the model from scratch using only the target company's data. We also show that for the transfer learning model to be effective, the pretrained model should be developed with larger datasets from diverse sources. The trucking industry may, thus, consider pooling safety analytics data from a wide range of companies to develop pretrained models and share them within the industry for better knowledge and resource transfer. The above contributions point to the promise of advanced safety analytics to make the industry safer and more sustainable.<|reference_end|>
@article{sun2024predicting,
    title={Predicting trucking accidents with truck drivers 'safety climate
  perception across companies: A transfer learning approach},
    author={Kailai Sun, Tianxiang Lan, Say Hong Kam, Yang Miang Goh, and
  Yueng-Hsiang Huang},
    journal={arXiv preprint arXiv:2402.12417},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12417},
    primaryClass={cs.LG cs.AI}
}