자연어처리 기반의 도로교통사고 판결예측 시스템: 교통사고상황 텍스트 정보 활용을 중심으로

AI-ready brief

Traffic accident scenarios are inherently complex, requiring substantial time and effort to analyze textual information pertaining to the causes, circumstances, and outcomes for legal adjudication. Such complexity also introduces a potential for human errors. Therefore, it is necessary to devise an efficient system for analyzing such textual data.

Author abstract

Traffic accident scenarios are inherently complex, requiring substantial time and effort to analyze textual information pertaining to the causes, circumstances, and outcomes for legal adjudication. Such complexity also introduces a potential for human errors. Therefore, it is necessary to devise an efficient system for analyzing such textual data. In this study, we propose a natural language processing (NLP)-based system that can predict judicial verdicts in traffic accident cases. The proposed system leverages NLP techniques to extract and analyze infor- mation related to traffic laws and regulations from textual descriptions of traffic accident scenarios, subsequently predicting sentencing information such as probation status and fines. The proposed system mainly consists of three parts: 1) data collection and preprocessing, 2) construction of a word dictionary and embedding, and 3) verdict prediction. First, traffic accident-related judgments are collected from various legal databases. Subsequently, key information such as imprisonment terms, probation status, and fines is extracted from the collected judgments, and a domain-specific word dictionary tailored to traffic accident cases is built using embedding techniques. Finally, the legal verdicts are predicted using a deep learning model. In our experiment, we validated the feasibility and applicability of the proposed system by implementing and applying it to approximately 37,406 traffic accident-related judgments, resulting in accuracies of 96.13%, 94.85%, and 95.49% for imprisonment, probation status, and fines, respectively.

AI retrieval note

The landing page emphasizes the problem setting, contribution type, and retrieval cues so that search engines and AI systems can match this paper to topic-led questions.

Questions this page answers

Retrieval cues

Citation-ready BibTeX

@article{noh2024q1748v,
  title   = {자연어처리 기반의 도로교통사고 판결예측 시스템: 교통사고상황 텍스트 정보 활용을 중심으로},
  author  = {민현식 and 윤준영 and 노병준},
  year    = {2024},
  journal = {대한교통학회논문지 42(4), pp.385-397, Aug 2024.}
}

Source links

No external article link is currently attached to this archive record.