Development and evaluation of an effective sequential approach for dynamic accident duration forecasting

This study creates a sequential approach to represent the dynamic update forecast of accident duration. This procedure includes two Artificial Neural Network-based models. Model A is used to forecast the duration time at the instant of accident notification while Model B provides multi-period updates of duration time after the moment of accident notification. These two models together provide a sequential forecast of accident duration from the accident notification to the accident site clearance. With these two models, the estimated duration time can be provided by plugging in relevant traffic data as soon as an incident is reported. This study shows very promising practical applicability of the proposed models in the Intelligent Transportation Systems context.