  Due to the stochastic nature of events, predicting the duration of a traffic
incident presents a formidable challenge. Accurate duration estimation can
result in substantial advantages for commuters in selecting optimal routes and
for traffic management personnel in addressing non-recurring congestion issues.
In this study, we gathered accident duration, road conditions, and
meteorological data from a database of traffic accidents to check the
feasibility of a traffic accident duration pipeline without accident contextual
information data like accident severity and textual description. Multiple
machine learning models were employed to predict whether an accident's impact
on road traffic would be of a short-term or long-term nature, and then
utilizing a bimodal approach the precise duration of the incident's effect was
determined. Our binary classification random forest model distinguished between
short-term and long-term effects with an 83% accuracy rate, while the LightGBM
regression model outperformed other machine learning regression models with
Mean Average Error (MAE) values of 26.15 and 13.3 and RMSE values of 32.91 and
28.91 for short and long-term accident duration prediction, respectively. Using
the optimal classification and regression model identified in the preceding
section, we then construct an end-to-end pipeline to incorporate the entire
process. The results of both separate and combined approaches were comparable
with previous works, which shows the applicability of only using static
features for predicting traffic accident duration. The SHAP value analysis
identified weather conditions, wind chill and wind speed as the most
influential factors in determining the duration of an accident.
