# Applying Real-Time Travel Times to Estimate Fire Service Coverage Rate for High-Rise Buildings
## 3 Case Studies
### Results and Discussion  
By calling the Baidu Map web service API, travel times and travel distances between 5146 high-rise buildings and the corresponding fire trucks were calculated, and a total of 308,760 valid data were obtained. Through data mining, \(ECR(C)\), \(AT(C)\), \(AD(C)\), and \(AV(C)\) in different scenarios were obtained (see Table 1). Further calculation and analysis show that \(T\)_TCR_(\(C\), \(\Delta T\)) has a value of 20.43%, and \(T\)_AT_(\(C\), \(\Delta T\)) has a value of 421.95 s. On the whole, most of the high-rise buildings in Changsha cannot be effectively covered by fire services. It can be inferred that fire apparatus requirements for  
Figure 2: Distribution of high-rise buildings and fire stations in Changsha City. Yellow dots represent high-rise buildings. Red squares represent fire stations equipped with both a water fire truck and an aerial fire truck. Orange squares represent fire stations only equipped with a water fire truck. Blue squares represent fire stations only equipped with an aerial fire truck. Urban areas are in black ellipses.  
To formulate specific measures for spatial optimization of fire stations and fire apparatus, the estimation results were visualized (see Figure 7). \(ECR(C)\) has a maximum value at 2019-12-07 04:00 a.m. and a minimum value at 2019-12-06 18:00 p.m. In addition, \(ECR(C)\) at 2019-12-06 12:00 p.m. has a value of 20.46%, which is closest to the value of \(TERR(C,\Delta T)\). Therefore, the estimation results in these three typical scenarios are selected for data visualization. Visualizations were generated by mapping covered and uncovered points with different colors into the satellite map of Changsha. However, it may lead to overlap phenomena, due to a large number of points and the limited resolution of the image. In these cases, the covered points will be shown first. From Figure 7a-c, the covered or uncovered status of a point may be varied in different scenarios, mainly due to the dynamic traffic conditions. However, there are many points in some areas that always had uncovered statuses (see black ellipses in Figure 7a). These areas are mainly new urban areas with smooth traffic and few fire stations. Although there are many fire stations in the main urban areas, traffic congestion leads to  
Figure 4: Trend chart of \(AT(C)\).  
Figure 5: Trend chart of \(AD(C)\).  
Figure 3: Trend chart of \(ECR(C)\).  
Figure 6: Trend chart of \(AV(C)\).  
the reduction of the effective coverage of these fire stations. Therefore, many points in these areas can sometimes be covered, and sometimes cannot be covered. Therefore, a sufficient number of fire stations equipped with water fire trucks and aerial fire trucks should be built in the new urban areas. In urban areas susceptible to traffic congestion, fire stations should be built on the one hand, and measures should be taken to reduce traffic congestion on the other hand.  
Figure 7: Visualizations of estimation results in typical scenarios. (**a**) At 2019–12–07 04:00 a.m. (**b**) At 2019–12–06 12:00 a.m. (**c**) At 2019–12–06 18:00 p.m. Black ellipses represent areas which always had uncovered statuses.