Optimizing the allocation of advanced life support ambulance parking in densely populated urban areas of Bangkok: A spatial and multi-criteria decision-making approach

Abstract

This research aims to develop guidelines for allocating Advanced Life Support (ALS) emergency ambulance parking spots in Bangkok, particularly in areas with high population density, to enhance the efficiency of emergency response within 8 minutes. The concept of spatial analysis is applied alongside the mathematical model Set Covering Problem (SCP) with the Greedy Algorithm and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which is a multi-criteria decision-making method to identify appropriate Emergency Medical Services (EMS) parking spots, utilizing the Value of a Statistical Life (VSL) as a variable to support policy decision-making. The results of the study indicate that integrating road network data and population density significantly enhances the efficiency of parking lot planning. When the number of parking lots increases from 13 to 48, the model can expand the service area within 8 minutes to 73.39% (Greedy) and 63.30% (TOPSIS) at an average speed of 30 km/h, increasing to 95.45% and 93.95%, respectively, at an average speed of 60 km/h. TOPSIS employs the population density criterion per area, which results in accurate parking lot allocation in densely populated regions. Although the Greedy Algorithm only utilizes the statistical life value criterion for its calculations, the area coverage rate is superior to that of the TOPSIS method when compared spatially using GIS. SCP is appropriate for enhancing the area coverage rate. In situations where budget constraints hinder investment at every point, TOPSIS is an effective solution for urgent parking lot allocation.