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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1285–1291, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1285-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1285–1291, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1285-2020

  21 Aug 2020

21 Aug 2020

OPTIMUM PATH DETERMINATION TO FACILITATE FIRE STATION RESCUE MISSIONS USING ANT COLONY OPTIMIZATION ALGORITHMS (CASE STUDY: CITY OF KARAJ)

N. Zarrinpanjeh1, F. Dadrass Javan2,4, A. Naji3, H. Azadi4, P. De Maeyer4, and F. Witlox4 N. Zarrinpanjeh et al.
  • 1Department of Geomatics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
  • 2Department of Surveying and Geomatics Engineering, University College of Engineering, University of Tehran, Tehran, Iran
  • 3Department of Geomatics Engineering, Ramsar Branch, Islamic Azad University, Ramsar, Iran
  • 4Department of Geography, Ghent University, Ghent, Belgium

Keywords: Ant Colony Optimization, Optimum Path Generation, Fire Station, Urban Road Network

Abstract. The successful conduct of a rescue mission in urban areas is directly related to the timely deployment of equipment and personnel to the incident location which justifies the quest for optimum path selection for emergency purposes. In this study, it is attempted to use Ant Colony Optimization (ACO) to find the optimum paths between fire stations and incident locations. It is also attempted to build up an evaluation tool using ACO to detect critical road segments that the overall accessibility to fire station services throughout the urban area is constituted upon their excellent functionality. Therefore, an ACO solution is designed to find optimum paths between the fire station and some randomly distributed incident locations. Regarding different variants of ACO, the algorithm enjoys the Simple Ant Colony Optimization deployment strategy combined with Ant Algorithm Transition rules. Iteration best pheromone updating is also used as the pheromone reinforcement strategy. The cost function used to optimize the path considers the shortest Euclidean distance on the network. The results explicitly state that the proposed method is successful to create the optimum path in 95.45 percent of all times, compared to Dijkstra deterministic approaches. Moreover, the pheromone map as an indicator of the criticality of road elements is generated and discussed. Visual inspection shows that the pheromone map is verified as the road criticality map concerning fire station access to the region and therefore pre-emptive measures can be defined by analyzing the generated pheromone map.