Heuristic Approach for Optimizing Emergency Medical Services in Road Safety within Large Urban Networks

Abstract

Emergency medical service (EMS) providers participating in vehicle crash-induced incident management aim to offer as wide and efficient coverage as possible to meet the demand for incident responses effectively; however, the design and provision of efficient and cost-effective services are tough issues faced by emergency management authorities. This paper introduces a double standard model (DSM), along with a genetic algorithm (GA) for assigning EMS fleet from vehicle locations to intersection vehicle crash sites such that crash demand sites can be covered in accordance with two service coverage standards. Specifically, all demand sites are required to receive single coverage according to the secondary coverage standard and at least a portion (α) of demand sites need to maintain single coverage as per the primary coverage standard. The proposed model is applied for top 200 intersections in the City of Chicago selected using intersection crash records for 2004–2010 according to crash frequency-based and severity-based scenarios. The top 200 intersections are split into high and low severity sites for model application. Using the EMS vehicle fleet size currently maintained by the Chicago Fire Department as 15 basic life support (BLS) and 60 advanced life support (ALS) ambulances, almost 100% of double vehicle coverage can be achieved. Extended model application is conducted by keeping 15 BLS ambulances unchanged and reducing the 60 ALS ambulances by 50% to 30. Results show that nearly 90% of double coverage according to the primary standard can still be reached.