Predicting trucking accidents with truck drivers 'safety climate
  perception across companies: A transfer learning approach
Kailai Sun Tianxiang Lan Say Hong Kam Yang Miang Goh Yueng-Hsiang Huang
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.