Abstract
The rapid expansion of cities brings in new challenges for the urban firefighting security, while the increasing fire frequency poses serious threats to the life, property, and safety of individuals living in cities. Firefighting in cities is a challenging task, and the optimal spatial arrangement of fire stations is critical to firefighting security. However, existing researches lack any consideration of the negative effects of the spatial randomness of fire outbreaks and delayed response time due to traffic jams upon the site selection. Based on the set cover location model integrated with the spatiotemporal big data, this paper combines the fire outbreak point with the traffic situation. The presented site selection strategy manages to ensure the arrival of the firefighting task force at random simulated fire outbreak points within the required time, under the constraints of the actual city planning and traffic situation. Taking Nanjing city as an example, this paper collects multi-source big data for the comprehensive analysis, including the full data of the fire outbreak history from June 2014 to June 2018, the traffic jam data based on the Amap, and the investigation data of the firefighting facilities in Nanjing. The regularity behind fire outbreaks is analyzed, the factors related to fire risks are identified, and the risk score is calculated. The previous fire outbreak points are put through the clustering analysis, the spatial distribution probability at points in each cluster is calculated according to the clustering score, and the random fire outbreak points are generated via the Monte Carlo simulation. Meanwhile, the objective emergency response time is set as five minutes. The average vehicle speed for each road in the urban area is calculated, and the actual traffic network model is built to compute the travel time from massive randomly-distributed simulated fire points. The problem is solved by making the travel time for all simulated demand points below five minutes. At last, the site selection result based on our model is adjusted and validated, according to the planned land use. The presented method incorporates the view of the spatiotemporal big data and provides a new idea and technical method for the modification and efficiency improvement of the fire station site selection model, contributing to a service cover ratio increase from 58% to 90%.
Highlights
The Monte Carlo simulation is performed to map the random fire outbreak points, and these massive randomly distributed simulated fire outbreak points are inputted into the actual traffic network model, which is transferred into the location set cover model constrained by the land use plan to solve the site selection problem (Figure 1)
Given that the fire outbreak probabilities are different for de-for demand points of varied the varied zones, research proposes a site selection method mand points of the fire fire riskrisk zones, thisthis research proposes a site selection method that manages to realize the arrival of firefighting taskforces within the target that manages to realize the arrival of firefighting taskforces within the target time time to allto all random simulated fire outbreak points under the constraints of the administrative random simulated fire outbreak points under the constraints of the administrative reguregulatory planactual and actual situation
This study first introduces the basic model of emergency site selection based on actual traffic conditions and the simulation method based on random demand in space
Summary
The consequent direct property loss in 2020 was 4.0 billion yuan, which is 83% higher than that in 2012 These fires seriously threaten the safety and property of individual lives, and they severely influence normal economic activities. In this context, it is of great importance to enhance urban firefighting efficiency. After meshing the urban area of Nanjing, the fire risk value distribution across the grid cells is identified via superposition. The research findings provide important guidance on the actual site selection of fire stations of Nanjing, and the presented method, a novel method for urban fire station site selection in the big data era, is practical to provide references for analogous specialized planning of other cities. Fire station site selection based on fire risk evaluation can greatly improve the effectiveness and service rate of fire stations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
