2017
DOI: 10.3390/ijgi6070227
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Abstract: Abstract:This study proposes an innovative integrated method for evaluating the evacuation and rescue capabilities of open spaces through a case study in Wuhan, China. A dual-scenario network analysis model was set up to calculate travel time among communities, open spaces, and rescue facilities during peak and non-peak hours. The distribution of traffic flow was derived on the basis of a gravity model and used to construct supply-demand indexes (SDIs). SDIs such as evacuation (ESDI), rescue (RSDI), and compre… Show more

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Cited by 6 publications
(1 citation statement)
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References 40 publications
(47 reference statements)
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“…We applied the gravity model to evaluate GH accessibility and used the traffic network model to calculate the travel time. This method was originally developed and optimised by Weibull [22] and has gradually become widely used to analyse medical accessibility [23], commuting patterns [24], park accessibility [25], and evacuation sites [26]. The calculation method is as follows:Ai=j=1nSjdijβVjVj=k=1mDkdkjβ where A i represents the spatial accessibility of demand point i to all supply points, S j represents the service capacity of supply point j , D k represents the demand scale of demand point k , V j represents the demand scale influencing factor, d ij represents the travel impedance (distance or time) between demand point i and supply point j , β represents the coefficient of friction of travel (which is set as 1.5 in this study), and n and m represent the number of supply points and demand points, respectively.…”
Section: Methodsmentioning
confidence: 99%
“…We applied the gravity model to evaluate GH accessibility and used the traffic network model to calculate the travel time. This method was originally developed and optimised by Weibull [22] and has gradually become widely used to analyse medical accessibility [23], commuting patterns [24], park accessibility [25], and evacuation sites [26]. The calculation method is as follows:Ai=j=1nSjdijβVjVj=k=1mDkdkjβ where A i represents the spatial accessibility of demand point i to all supply points, S j represents the service capacity of supply point j , D k represents the demand scale of demand point k , V j represents the demand scale influencing factor, d ij represents the travel impedance (distance or time) between demand point i and supply point j , β represents the coefficient of friction of travel (which is set as 1.5 in this study), and n and m represent the number of supply points and demand points, respectively.…”
Section: Methodsmentioning
confidence: 99%