Sequential forecast of incident duration using Artificial Neural Network models

This study creates an adaptive procedure for sequential forecasting of incident duration. This adaptive procedure includes two adaptive Artificial Neural Network-based models as well as the data fusion techniques to forecast incident duration. Model A is used to forecast the duration time at the time of incident notification, while Model B provides multi-period updates of duration time after the incident notification. These two models together provide a sequential forecast of incident duration from the point of incident notification to the incident road clearance. Model inputs include incident characteristics, traffic data, time gap, space gap, and geometric characteristics. The model performance of mean absolute percentage error for forecasted incident duration at each time point of forecast are mostly under 40%, which indicates that the proposed models have a reasonable forecast ability. With these two models, the estimated duration time can be provided by plugging in relevant traffic data as soon as an incident is reported. Thereby travelers and traffic management units can better understand the impact of the existing incident. Based on the model effect assessments, this study shows that the proposed models are feasible in the Intelligent Transportation Systems (ITS) context.