Inspiration in human reasoning logic: Automating the inference and analysis of traffic accident information via macro-micro integration
It aims to extract structured and inferable data from high-dimensional, large-sample, and unstructured complex accident text data, including temporal, geographical, positional, weather, and road condition features. It then identifies keywords, reasoning logic, and semantic relationships, enabling effective contextual analysis. This not only aids in determining accident causes and responsibilities but also accurately assesses accident severity. The findings of this study hold significant importance for the automated processing and conversion of text data, particularly in the context of highly complex and variable accident text data.
Authors
- Yaqin Qin (ORCID: https://orcid.org/0000-0002-0145-4288)
- Yan Zhang (ORCID: https://orcid.org/0000-0002-1124-4065)
- Yulan Xia (ORCID: https://orcid.org/0000-0002-2528-8229)
- Guangquan Lu (ORCID: https://orcid.org/0000-0003-0198-100X)
- Jiming Xie (ORCID: https://orcid.org/0000-0001-5424-3508)
- Ke Li (ORCID: https://orcid.org/0000-0002-1736-8261)
- Bijun Wang (ORCID: https://orcid.org/0000-0002-2716-7113)
Institutions
- Kunming University of Science and Technology (CN)
- University of Shanghai for Science and Technology (CN)
- Beihang University (CN)
Publication Details
- Journal
- Traffic Injury Prevention
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1080/15389588.2025.2486572
- Citations
- 2
- Primary Topic
- Anomaly Detection Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 4.96
Funders
- National Natural Science Foundation of China