Crash Causing Information Extraction via Text Mining Techniques: Implementation of the Chinese State-related Crash Narratives

Crash data is the foundation of traffic safety analysis. In China, the accident reporting form (ARF) only allows reporting one cause for each crash based on the prespecified crash cause code, which may lead to inaccuracy in recording crash data, especially for state-related crashes. This study investigated the directly contributory factors behind the state-related crashes through the development of natural language processing (NLP) and deep learning models based on 1,625 state-related crash narratives. Based on the directly causal factors described in the crash narratives, the state-related crashes were labeled by speed-related, turning-related and other causes. Then the crash narratives were vectorized for model training and frequent analysis. The text-CNN, LSTM, and GRU, and SVM models were applied to reclass the vectorized crash. The results showed the text-CNN model demonstrated the best performance in text classification, with an AUC value of 0.90 for micro-average curves. The results from this study can engage the usage of crash narratives and help identify the actual causative reason behind some inaccurate crash value designation.