  This research presents a comprehensive approach to predicting the duration of
traffic incidents and classifying them as short-term or long-term across the
Sydney Metropolitan Area. Leveraging a dataset that encompasses detailed
records of traffic incidents, road network characteristics, and socio-economic
indicators, we train and evaluate a variety of advanced machine learning models
including Gradient Boosted Decision Trees (GBDT), Random Forest, LightGBM, and
XGBoost. The models are assessed using Root Mean Square Error (RMSE) for
regression tasks and F1 score for classification tasks.
  Our experimental results demonstrate that XGBoost and LightGBM outperform
conventional models with XGBoost achieving the lowest RMSE of 33.7 for
predicting incident duration and highest classification F1 score of 0.62 for a
30-minute duration threshold. For classification, the 30-minute threshold
balances performance with 70.84% short-term duration classification accuracy
and 62.72% long-term duration classification accuracy. Feature importance
analysis, employing both tree split counts and SHAP values, identifies the
number of affected lanes, traffic volume, and types of primary and secondary
vehicles as the most influential features.
  The proposed methodology not only achieves high predictive accuracy but also
provides stakeholders with vital insights into factors contributing to incident
durations. These insights enable more informed decision-making for traffic
management and response strategies. The code is available by the link:
https://github.com/Future-Mobility-Lab/SydneyIncidents
