Developing an XGBoost based model to predict the probability of truck crashes driven by macro operation and insurance data
This research innovatively establishes a macro data-driven truck accident prediction model alleviating the difficulty of data collection as well as guaranteeing the prediction accuracy.
Authors
- Liwei Gao (ORCID: https://orcid.org/0000-0001-7652-7456)
- Yiping Wu (ORCID: https://orcid.org/0000-0002-5163-0884)
- Peng Song (ORCID: https://orcid.org/0000-0002-1748-5580)
- Hongpeng Zhang (ORCID: https://orcid.org/0000-0002-8488-9059)
- Ji M (ORCID: https://orcid.org/0009-0002-2961-7904)
- Xinwei Sun (ORCID: https://orcid.org/0009-0005-0344-5472)
- Jun Ma (ORCID: https://orcid.org/0000-0002-4077-7928)
Institutions
- Beijing University of Technology (CN)
- Northwestern Mutual Life Insurance (United States) (US)
- Research and Technological Institute of Biological Industry (RU)
Publication Details
- Journal
- Traffic Injury Prevention
- Published
- 2025-09-15
- DOI
- https://doi.org/10.1080/15389588.2025.2545002
- Citations
- 1
- Primary Topic
- Autonomous Vehicle Technology and Safety
- Type
- article
- Field-Weighted Citation Impact
- 0.68