ArticleInternational journal of health geographics2025
Optimizing ambulance location based on road accident data in Rwanda using machine learning algorithms.
Article in International journal of health geographics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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2 authors.
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Abstract
backgroundThe optimal placement of ambulances is critical for ensuring timely emergency medical responses, especially in regions with high accident frequencies. In Rwanda, where road accidents are a leading cause of injury and death, the strategic positioning of ambulances can significantly reduce response times and improve survival rates. The national records of Rwanda reveal a rising trend in the number of road accidents and deaths. In 2020, there were 4203 road traffic crashes throughout Rwanda with 687 deaths, data from 2021 demonstrated 8639 road traffic crashes with 655 deaths. Then in 2022 national statistics indicated 10,334 crushes with 729 deaths. The study used emergency response and road accident data collected by Rwanda Biomedical Centre in two fiscal years 2021-2022 and 2022-2023 consolidated with the administrative boundary of Rwandan sectors (shapefiles).
methodsThe main objective was to optimize ambulance locations based on road accident data using machine learning algorithms. The methodology of this study used the random forest model to predict emergency response time and k-means clustering combined with linear programming to identify optimal hotspots for ambulance locations in Rwanda.
resultsRandom forest yields an accuracy of 94.3%, and positively classified emergency response time as 926 fast and 908 slow. K-means clustering combined with an optimization technique has grouped accident locations into two clusters and identified 58 optimal hotspots (stations) for ambulance locations in different regions of Rwanda with an average distance of 1092.773 m of ambulance station to the nearest accident location.
conclusionMachine learning may identify hidden information that standard statistical approaches cannot, the developed model for random forest and k-means clustering combined with linear programming reveals a strong performance for optimizing ambulance location using road accident data.
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