Incident Duration Prediction

Accurately predicting the duration of traffic incidents is crucial for efficient traffic management and resource allocation. Current research heavily utilizes machine learning, particularly advanced tree-based models like XGBoost and LightGBM, along with deep learning architectures like LSTM networks, to predict incident durations using diverse data sources including incident reports, traffic flow data, and weather information. These models aim to improve prediction accuracy, often by incorporating techniques like outlier removal and optimizing for both short-term and long-term incident classifications. Improved prediction capabilities offer significant benefits, enabling proactive deployment of emergency services and potentially reducing congestion and travel times.