Enhancing Traffic Accident Classifications: Application of NLP Methods for City Safety
This work addresses traffic accident classification for city safety planning, but is incremental in applying standard NLP methods to a specific dataset.
The study analyzed traffic accidents in Munich to identify patterns distinguishing accident types, finding that textual descriptions contained the most informative features for classification while tabular data provided only marginal improvements. The classification model achieved high accuracy in assigning accidents to categories, demonstrating the critical role of free-text data in accident analysis.
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.