Predicting the duration of traffic accidents is a critical component of traffic management and emergency response on expressways. Traffic accident information is inherently multi-mode data in terms of data types. However, most existing studies focus on single-mode data, and the influence of multi-mode data on the prediction performances of models has been the subject of only very limited quantitative analysis. The present work addresses these issues by proposing a heterogeneous deep learning architecture employing multi-modal features to improve the accuracy of predictions for traffic accident durations on expressways. Firstly, six unique data modes are obtained based on the structured data and the text data. Secondly, a hybrid deep learning approach is applied to build classification models with reduced prediction error. Finally, a rigorous analysis of the influence for multi-mode data on the accident duration prediction performances is conducted using a variety of deep learning models. The proposed method is evaluated using survey data collected from an expressway monitoring system in Shaanxi Province, China. The experimental results show that Word2Vec-BiGRU-CNN is a suitable and better model using text features for traffic accident duration prediction, as the F1-score is 0.3648. This study confirms that the newly established structured features extracted from text data substantially enhance the prediction effects of deep learning algorithms. However, these new features were a detriment to the prediction effects of conventional machine learning algorithms. Accordingly, these results demonstrate that the processing and extraction of text features is a complex issue in the field of traffic accident duration prediction.
Keywords: BiGRU-CNN; Feature fusion; Multi-mode data; Pre-trained model; Traffic accident duration; Traffic safety.
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