This study delves into understanding the complexities of Emergency Medical Services (EMS) response times using a machine learning approach. Analyzing over one million EMS missions in Stockholm from 2017 to 2022, the study explores various factors affecting response time, including call handling time, travel time, weather conditions, and more. By employing both traditional statistical methods and advanced machine learning models, the research uncovers correlations and patterns in EMS operations. Preliminary key findings include the influence of factors like call types, weather, and geographical location on response times. The study emphasizes the importance of considering these factors in resource allocation and operational planning to enhance EMS efficiency and patient care. Overall, it offers valuable insights into optimizing EMS systems for better emergency response.
Dynamic EMS allocation 