Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses

TL;DR

This paper introduces CrashEvent, a large-scale traffic crash dataset, and proposes CrashLLM, a large language model-based approach that improves accident outcome predictions and enables nuanced what-if traffic safety analyses.

Contribution

The paper presents a novel large-scale crash dataset and leverages large language models for detailed accident outcome prediction and interpretability in traffic safety analysis.

Findings

F1 score improved from 34.9% to 53.8% with CrashLLM

CrashLLM predicts crash types, severity, and injuries effectively

Provides a new benchmark and dataset for traffic crash analysis

Abstract

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.