The increasing rate of road accidents worldwide results not only in
significant loss of life but also imposes billions financial burdens on
societies. Current research in traffic crash frequency modeling and analysis
has predominantly approached the problem as classification tasks, focusing
mainly on learning-based classification or ensemble learning methods. These
approaches often overlook the intricate relationships among the complex
infrastructure, environmental, human and contextual factors related to traffic
crashes and risky situations. In contrast, we initially propose a large-scale
traffic crash language dataset, named CrashEvent, summarizing 19,340 real-world
crash reports and incorporating infrastructure data, environmental and traffic
textual and visual information in Washington State. Leveraging this rich
dataset, we further formulate the crash event feature learning as a novel text
reasoning problem and further fine-tune various large language models (LLMs) to
predict detailed accident outcomes, such as crash types, severity and number of
injuries, based on contextual and environmental factors. The proposed model,
CrashLLM, distinguishes itself from existing solutions by leveraging the
inherent text reasoning capabilities of LLMs to parse and learn from complex,
unstructured data, thereby enabling a more nuanced analysis of contributing
factors. Our experiments results shows that our LLM-based approach not only
predicts the severity of accidents but also classifies different types of
accidents and predicts injury outcomes, all with averaged F1 score boosted from
34.9% to 53.8%. Furthermore, CrashLLM can provide valuable insights for
numerous open-world what-if situational-awareness traffic safety analyses with
learned reasoning features, which existing models cannot offer. We make our
benchmark, datasets, and model public available for further exploration.

本研究在大规模交通事故语言数据集 CrashEvent 的基础上，利用大型语言模型 CrashLLM 提出了一种新的交通事故特征学习方法，从复杂的、非结构化数据中解析和学习，预测交通事故的类型、严重程度和受伤人数，并通过学习的推理特征为交通安全分析提供有价值的洞见。