A Computerized Feature Reduction Using Cluster Methods for Accident Duration Forecasting on Freeway

This study creates two Artificial Neural Network-based models and provides 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 accident is notified. To reduce data feature, cluster method can decreases the number of model inputs and preserves the relevant traffic characteristics with fewer inputs. This study shows proposed models are feasible ones in the Intelligent Transportation Systems (ITS) context.