Using Big Data to optimize dynamic ambulance availability maps: bridging the gap in emergency services

Abstract In this study, we explore the use of Big Data to dynamically optimize ambulance availability and response times in emergency medical services (EMS). Integrating Big Data principles with the Open Source Routing Machine (OSRM) routing engine, a novel algorithm was used to calculate travel times across an irregular grid, creating real-time, colour-coded time-accessibility maps. In contrast with traditional static models, this approach updates dynamically, accounting for road conditions, accidents and other disruptions to minimize delays. By excluding unusable segments from calculations, the algorithm ensures rapid recalculations, maintaining EMS coverage in evolving conditions. Testing showed significant improvements in response time estimation and resource allocation, particularly in urban environments with complex road networks. This real-time mapping tool offers EMS dispatchers an enhanced decision-support system, potentially saving lives by reducing ambulance response times and improving service availability across diverse areas.