Enhancing Traffic Accident Classifications: Application of NLP Methods for City Safety

TL;DR

This paper explores NLP techniques to improve traffic accident classification accuracy by analyzing both structured data and free-text descriptions, revealing the importance of textual information and inconsistencies in labeling.

Contribution

It introduces a refined classification approach utilizing NLP methods, particularly transformer models, to enhance accident categorization reliability and address label ambiguities.

Findings

Textual descriptions are highly informative for classification.

NLP methods reveal inconsistencies in accident labels.

Text data improves classification accuracy more than tabular data.

Abstract

A comprehensive understanding of traffic accidents is essential for improving city safety and informing policy decisions. In this study, we analyze traffic incidents in Munich to identify patterns and characteristics that distinguish different types of accidents. The dataset consists of both structured tabular features, such as location, time, and weather conditions, as well as unstructured free-text descriptions detailing the circumstances of each accident. Each incident is categorized into one of seven predefined classes. To assess the reliability of these labels, we apply NLP methods, including topic modeling and few-shot learning, which reveal inconsistencies in the labeling process. These findings highlight potential ambiguities in accident classification and motivate a refined predictive approach. Building on these insights, we develop a classification model that achieves high accuracy in assigning accidents to their respective categories. Our results demonstrate that textual descriptions contain the most informative features for classification, while the inclusion of tabular data provides only marginal improvements. These findings emphasize the critical role of free-text data in accident analysis and highlight the potential of transformer-based models in improving classification reliability.