Optimal Ambulance Positioning For Road Accidents With Deep Embedded Clustering

Abstract

Road accidents remain a major cause of injuries and fatalities worldwide, making them a critical issue for public safety and emergency management. One effective strategy to minimize delays in providing medical assistance is to preposition ambulances at optimal locations instead of deploying them only after receiving emergency calls. This research introduces a deep learning–based clustering framework aimed at identifying the most appropriate locations for ambulance deployment. The occurrence of traffic accidents is strongly influenced by geographic conditions and spatial distribution patterns, and preserving these relationships during model development is important for generating accurate real-time predictions. To achieve this, the study utilizes a representation learning approach called Cat2Vec, which helps retain important categorical and spatial patterns within the dataset. The proposed system is evaluated and compared with several commonly used clustering techniques, including K-Means, Gaussian Mixture Models (GMM), and Agglomerative Clustering. Furthermore, the study proposes a novel scoring metric designed to evaluate algorithm performance by considering both travel distance and emergency response time. Experimental results indicate that the model achieves 95% accuracy when validated using k-fold cross-validation, along with a distance score of 7.581. These findings demonstrate that the proposed framework significantly improves the effectiveness of ambulance placement strategies when compared with traditional clustering approaches, highlighting its potential for enhancing emergency response planning and reducing delays in critical medical services.