Traffic Incident Duration Prediction using BERT Representation of Text

Owing to the diverse nature of traffic incidents, accepting and storing relevant data in the form of natural language is more convenient than in constrained value fields. Textual information in such cases can be rich enough for traffic incident analysis and modelling even in the absence of certain fixed set of parameters. However limited studies considered the complexity in processing such information to predict traffic incident duration. In this paper, we propose to represent the textual data from incident reports using BERT word embeddings. These text representations are then inputted into various regressors such as LSTM, XGBoost, RF and SVR to predict traffic incident duration. To demonstrate the significance of this approach, the method is compared with the state-of-the-art approach using LDA representation. Dataset used for the experiment is the Caltrans Performance Measurement System (PeMS). Result analysis indicates that the BERT- LSTM hybrid model is effective in capturing the contextual meaning of textual incident reports to predict the traffic incident duration and outperforms LDA topic modelling with MAE around 11.16 minutes.