Optimal Ambulance Positioning for Road Accidents with Deep Embedded Clustering

Timely emergency response is critical to minimizing casualties and saving lives in road traffic accidents. However, urban congestion, uneven ambulance distribution, and lack of predictive resource positioning often delay critical response times. This study proposes a deep learning-based optimization model for the strategic placement of ambulances using Deep Embedded Clustering (DEC). Accident data is first preprocessed and clustered based on spatial coordinates, frequency, and severity of incidents. The model leverages an autoencoder network to reduce dimensionality and capture latent spatial patterns, followed by clustering to identify optimal ambulance standby locations. Comparative analysis against conventional K-Means and DBSCAN methods demonstrates that the DEC-based approach yields more compact and meaningful clusters, reducing average response distance and maximizing coverage. The proposed system can guide civic authorities and emergency planners in proactively allocating ambulances to accident-prone zones, thus improving responsiveness and resource utilization in urban and semi-urban regions.