A k-nearest text similarity-BiGRU approach for duration prediction of traffic accidents on expressways - Jiaona Chen; Jin Zhang; Peng Wang; Yinli Jin (Discover Applied Sciences)
Abstract Accurate prediction of traffic accident duration is critical for alleviating congestion and optimizing traffic management. This study proposes a k-nearest text similarity-BiGRU(Bidirectional Gated Recurrent Unit) approach to predict the duration of traffic accidents using textual records. Firstly, text vectors are extracted through a TF-IDF (Term Frequency-Inverse Document Frequency) model to vectorize textual descriptions. Secondly, the text similarity is quantified based on the extracted text vectors. Therefore, the independent variables for predicting accident duration are obtained through a text similarity evaluation, which serves as the feature vector. Finally, BiGRU networks are utilized for the regression prediction of traffic accident duration using the constructed feature vectors. In addition, the proposed approach is evaluated by the RMSE (root mean square error) and MAPE (mean absolute percent error) utilizing survey information gathered from expressways of Shaanxi Province in China. A rigorous analysis of the prediction performances is compared with different regression methods. Experimental results demonstrate that the k-nearest text similarity-BiGRU outperforms other models. Moreover, the RMSE and MAPE of the TF-IDF model based on BiGRU are 47.77 and 43.74%, respectively. In general, the MAPE is much better than that of regression prediction models employed in previous studies. This work validates the feasibility of text-driven feature engineering for predicting the duration of traffic accidents, and provides a robust tool for traffic management systems.