Ambulance Service Area Considering Disaster-Induced Disturbance on the Transportation Infrastructure

Abstract

The effectiveness of emergency medical services (EMS) depends on the existing infrastructure and allocation of medical resources. The response time for ambulances is in general considered a critical factor to the survival of EMS patients. EMS is a challenging task due to the spatial distribution of the population and geographical layout in the urban area. The spatial configuration of ambulance fleets and hospitals should be assessed to provide an efficient service. Additionally, EMS plays a critical role in disaster situations. In this research, the effect of disasters is considered as the disturbance to the EMS system. The objective of this research is to assess the service area of EMS after a disaster. In this study, the deviation of service area before and after the disaster on the transportation infrastructure is evaluated. Multi-layer perceptron (MLP) was used to predict EMS demand in usual conditions, and a loss estimation tool is used to model the disaster triggered demand. Given the usual and disaster triggered EMS demand, location of ad hoc EMS facilities were determined using cluster analysis, and the reachability of the facilities to individual demand points were evaluated using service area analysis. The proposed approach serves as an assessment methodology and also a planning tool for the preparedness of future disaster occurrences.

From flooding to finance: NHS ambulance‐assisted evacuations of care home residents in Norfolk and Suffolk, UK

“…Currently, the EEAST owns 85 ambulance stations and 387 emergency ambulances, and assuming that these ambulances are equally dispersed between stations, Norfolk and Suffolk possess 25 ambulance stations and approximately 113 ambulances to provide assistance at an average of approximately 20 patient evacuations each (in relation to the approximate 2,320 at‐risk residents) (EEAST, ). The even distribution of these stations (Figure ) promotes wide‐spread service area coverage per response‐time which would not occur under an aggregated distribution (Chen et al, ).…”

Globally Optimal Facility Locations for Continuous-Space Facility Location Problems

“…Esnaf and Küçükdeniz [37] presented a fuzzy clustering-based hybrid method for a multi-facility location problem where the capacity of each facility was unlimited. Chen, Yeh [38] considered the Euclidean distance in searching for potential locations of temporary emergency medical centers using a clustering-based algorithm. Varghese and Gladston Raj [39] applied the k-means clustering algorithm so solve a multifacility location-allocation problem.…”

Network based temporary facility location for the Emergency Medical Services considering the disaster induced demand and the transportation infrastructure in disaster response

“…Pre-disaster planning relies on the assumptions on how the transport network may degrade ( Bell et al, 2014 ). To seek for appropriate locations where the post-disaster service facilities should be located, the temporal and spatial demand and the service time on the transportation network should be considered ( Boyacı, B., Geroliminis, 2012;Chen et al, 2015b ). Pre-positioning strategies, considering the transportation network availability after a disaster, to increase preparedness for natural disasters, should also be proposed ( Rawls and Turnquist, 2010 ).…”

“…The k -medoids and the ε-Link clustering algorithms are the proposed algorithms for network-based cluster analysis in the field of computer science ( Yiu and Mamoulis, 2004 ). The EMS demands have previously been clustered by considering the Euclidean distance in seeking of potential locations of temporary EMS facilities ( Chen et al, 2015b ), and should be extended to consider network distance with MIP or network-based clustering techniques. The MIP or network-based clustering, considering the utility cost of the transportation infrastructure in disaster response, has not been exploited thoroughly for EMS.…”