## 4 Simulation and Calibration
### Data Processing and Analysis
In Figures 7-9, it can be clearly seen that after conversion, the traffic flow data of regional average flow, occupancy and speed without sudden fire disaster situation are distributed in a small area, relatively concentrated, and located on the positive half axis of speed. In general, the traffic status without sudden fire disasters is in a high-speed stable state. The regional average flow, occupancy and speed of traffic flow under sudden fire disasters are more discretization comparing to those without sudden fire disasters. Before sudden fire disasters happened, the traffic network was in a state of high speed and low occupancy (as shown by the red pentagon in Figures 7-9). During the sudden fire disaster, the traffic flow changes to low speed and high occupancy (as shown by the black circles in Figures 7-9). It can be seen from Figures 7-9, the dispersion degree and distribution of the point before and after the sudden fire disaster is different. The traffic data under sudden fire disasters are shown to be relatively scattered. The traffic flow is low speed and high occupancy. The traffic data without sudden fire disasters are relatively centralized. The traffic flow is relatively high speed and low occupancy. From the distribution shown in Figures 7-9, as the traffic data collection interval increase, the traffic data are more centralized and scatter ranged, the gap is more clear between with sudden fire disasters and without sudden fire disasters. According to the catastrophe theory, there is a catastrophe border to make the catastrophe change occur. Therefore, the catastrophe border can be identified by catastrophe fitting with the transformed data. Step 3: CCT-based transformed data fitting. The datasets of transformed data in different time intervals are fitted by using the potential function that goes with the cusp model of catastrophe theory as formula (8). The fitting panels are shown in Figures 10-12. In Figures 10-12, the \(X\)-axis is the value of regional average speed. The \(Y\)-axis is the value of regional average flow. The \(Z\)-axis is the value of regional average occupancy. The catastrophe border can be seen from the figures. The parameters of formula (8) in different time intervals are obtained by fitting the datasets of transformed data, as shown in Table 4.  
Putting the parameters into formula (10), the catastrophe border of regional average occupancy and regional average flow can be identified by the solution of formula (10), as shown in Table 5.  
From Table 5, the catastrophe border is 439 pcu/lane/h, 529 pcu/lane/h, 377 pcu/lane/h in the 5 s, 10 s, 15 s traffic date collection interval, respectively. That is to say, for the aim of prevent traffic congestion and disorder under sudden fire disasters, the traffic flow should not exceed 439 pcu/lane/h, 529 pcu/lane/h, 377 pcu/lane/h within the disaster-affected area in the 5 s, 10 s, 15 s traffic date collection interval, respectively. [PERSON] and [PERSON] [50] analyzed the traffic flow characteristics under sudden fire disasters in road segment used a Capacity Assessment Approach (CAA) based on VISSIM simulator data. The study results showed that the remaining capacity of the road after the fire is 61.611%. It can be concluded that if the traffic flow is 800 pcu/lane/h in traffic network in normal traffic condition, the capacity of traffic flow will remain about 493 pcu/lane/h under sudden fire disasters. If the volume of traffic flow exceed this value, the traffic network will be caught in congestion and disorder. This value can be considered as the catastrophe border of traffic network under sudden disasters.