Locational Planning for Emergency Management and Response: An Artificial Intelligence Approach

Locational Planning for Emergency Management and Response: An Artificial Intelligence Approach

Yorgos N. Photis George Grekousis 

Department of Planning and Regional Development, University of Thessaly, Greece

30 September 2012
| Citation



The effi ciency 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 signifi cance for state or local offi cials 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 semi- structured. 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 Artifi cial Neural Network (ANN). More specifi cally, the ANN provides the basis for a spatiotemporal clustering of demand, defi nition of the relevant centres, formulation of possible future states of the system and fi nally, defi nition 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.


Emergency planning, fuzzy logic, neural networks, spatiotemporal location analysis


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