Locational planning for emergency management and response: An artificial intelligence approach

Abstract

The efficiency of emergency service systems is measured in terms of their ability to deploy units and personnel in a timely and effective manner upon an event's occurrence. When dealing with public sector institutions, this refl ects the significance for state or local officials to determine the optimal locations for emergency stations and vehicles. The typical methodology to deal with such a task is through the application of the appropriate location-allocation model. In such a case, however, the spatial distribution of demand although stochastic in nature and layout, when aggregated at the appropriate level, appears to be spatially structured or semistructured. Aiming to exploit the above incentive, a different approach will be examined in this paper. The spatial tracing and location analysis of emergency incidents is achieved through the utilisation of an Artificial Neural Network (ANN). More specifically, the ANN provides the basis for a spatiotemporal clustering of demand, definition of the relevant centres, formulation of possible future states of the system and finally, definition of locational strategies for the improvement of the provided services. The proposed methodological approach is applied to Athens Metropolitan Area and the adopted dataset constitutes of the incidents that were reported and confronted by the city's Fire Department during the year 2008. © 2012 WIT Press.