Project 03
Traffic incidents in Calgary cause delays, economic losses, and safety hazards. This project applies spatial clustering and temporal classification to 60K+ traffic incidents to identify high-risk hotspots and predict peak-incident periods.
Interactive app
Interactive map of Calgary with DBSCAN-identified incident clusters and density overlays
Hourly, daily, and seasonal patterns with rush-hour flags and weekend indicators
Detailed breakdown of each spatial cluster by incident type, severity, and timing
Predict whether a given time window will experience peak incident volume
Performance
Approach
Fetched real-time traffic incident data from Calgary Open Data. Applied DBSCAN with haversine distance for spatial clustering alongside KMeans. Engineered cyclical time features, rush-hour flags, and weekend indicators. Trained Random Forest and Gradient Boosting classifiers for temporal peak prediction, evaluated with accuracy, precision, recall, F1, and 5-fold cross-validated F1.