  Traffic congestion caused by non-recurring incidents such as vehicle crashes
and debris is a key issue for Traffic Management Centers (TMCs). Clearing
incidents in a timely manner is essential for improving safety and reducing
delays and emissions for the traveling public. However, TMCs and other
responders face a challenge in predicting the duration of incidents (until the
roadway is clear), making decisions of what resources to deploy difficult. To
address this problem, this research developed an analytical framework and
end-to-end machine-learning solution for predicting incident duration based on
information available as soon as an incident report is received. Quality
predictions of incident duration can help TMCs and other responders take a
proactive approach in deploying responder services such as tow trucks,
maintenance crews or activating alternative routes. The predictions use a
combination of classification and regression machine learning modules. The
performance of the developed solution has been evaluated based on the Mean
Absolute Error (MAE), or deviation from the actual incident duration as well as
Area Under the Curve (AUC) and Mean Absolute Percentage Error (MAPE). The
results showed that the framework significantly improved incident duration
prediction compared to methods from previous research.
