Optimal Ambulance Positioning for Road Accidents with Deep Embedded Clustering

Abstract

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.

Introduction

The paper addresses the challenge of optimizing ambulance placement in accident-prone areas, aiming to enhance emergency medical services (EMS) response times and coverage. Road traffic accidents are a leading global cause of death and injury, and the timely dispatch of ambulances significantly affects survival rates. Traditional methods of ambulance deployment are often reactive and fail to adapt to changing urban dynamics, relying on heuristic or fixed routing strategies. Additionally, conventional clustering techniques like K-Means or DBSCAN don't capture the complex, high-dimensional features influencing accident occurrences.

This study proposes a deep learning-driven framework that utilizes Deep Embedded Clustering (DEC) to optimize ambulance positioning by analyzing spatial and contextual accident data. DEC combines autoencoder-based dimensionality reduction with clustering, enabling the model to uncover non-linear structures and more effectively identify high-density accident zones. By leveraging these insights, the model strategically places ambulances in accident-prone areas to minimize response times and maximize coverage.

Key Contributions:

• A deep learning-based framework using DEC to cluster accident-prone zones.

• Autoencoder-based dimensionality reduction to extract latent features in accident data.

• A comparative evaluation against traditional clustering methods (K-Means, DBSCAN) showing improved efficiency.

• Visualization tools to support EMS planners in interpreting optimal ambulance locations.

Literature Survey:

The paper discusses several traditional approaches to ambulance placement, such as operations research models like MCLP and LSCP, which optimize coverage but lack adaptability. Clustering methods like K-Means and DBSCAN have been used but are limited in handling non-linear and high-dimensional data. The introduction of Deep Embedded Clustering (DEC), which combines clustering and feature learning, marks a significant advancement over previous methods.

Proposed Methodology:

The model works in several stages:

1. Data Acquisition and Preprocessing: Collects and normalizes accident data (location, frequency, time, etc.).

2. Dimensionality Reduction with Autoencoder: Compresses high-dimensional data into a lower-dimensional space, capturing essential patterns.

3. Deep Embedded Clustering (DEC): Optimizes clustering by learning both feature representations and cluster centroids. A soft clustering assignment is computed, and the model iteratively minimizes the Kullback-Leibler (KL) divergence to fine-tune the clusters.

4. Visualization: The clusters are visualized on geographic maps to interpret and deploy optimal ambulance locations.

Baseline Comparisons:

The DEC-based model is compared with traditional methods like K-Means, DBSCAN, and Agglomerative Clustering. Performance is evaluated using metrics like the Silhouette Score and Davies–Bouldin Index to assess clustering compactness and separation.

Implementation:

The framework was implemented in Python using Keras and TensorFlow for the autoencoder and DEC models. It also employed scikit-learn for baseline clustering and evaluation. The dataset consists of geospatial accident data, which undergoes preprocessing (data cleaning, normalization, feature selection) to prepare it for model training.

Conclusion

Efficient ambulance deployment plays a pivotal role in reducing emergency response times and improving survival outcomes in road traffic accidents. This study presents a data-driven approach for optimizing ambulance positioning using Deep Embedded Clustering (DEC). By integrating autoencoder-based feature learning with cluster optimization, the proposed framework effectively identifies high-risk accident zones and recommends strategic ambulance standby locations. Experimental evaluation using real-world accident datasets demonstrated that the DEC model outperforms traditional clustering techniques such as K-Means, DBSCAN, and Agglomerative Clustering in terms of cluster compactness, separation, and coverage. The resulting clusters were both spatially meaningful and operationally interpretable, making them suitable for direct deployment by urban planners and emergency response agencies. The approach also supports flexible scalability by adjusting the number of clusters based on available ambulance resources. Furthermore, visualization of cluster centers offers a practical tool for understanding accident density and guiding emergency resource allocation. Overall, the proposed method contributes a novel and effective strategy for enhancing emergency preparedness and responsiveness through deep learning-based spatial intelligence.

References

[1] J. Xie, R. Girshick, and A. Farhadi, “Unsupervised Deep Embedding for Clustering Analysis,” in Proc. 33rd Int. Conf. Machine Learning (ICML), New York, USA, 2016, pp. 478–487. [2] V. Chandran, P. Swaminathan, and N. Mehta, “Optimizing Ambulance Location Using K-Means Clustering and GIS,” in Proc. 2020 Int. Conf. Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2020, pp. 557–562. [3] J. Wu, C. Zhu, and H. Wang, “Spatiotemporal Clustering for Emergency Vehicle Dispatch Optimization,” IEEE Access, vol. 8, pp. 110453–110465, 2020. [4] J. MacQueen, “Some Methods for Classification and Analysis of Multivariate Observations,” in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, 1967, pp. 281–297. [5] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” in Proc. 2nd Int. Conf. Knowledge Discovery and Data Mining (KDD), 1996, pp. 226–231. [6] L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. New York: Wiley, 2009.

Copyright

Copyright © 2025 Dr. K. SubbaRao, Sreeramula Rechal Christy Indiyana, Koliparti Kiranmai, Juturi Vamsi Krishna, Narahari Hima Bindhu, Thammisetty Snehardh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.