## 4 Simulation and Calibration  
### Simulation Scenarios and Data Collection  
The microscopic traffic flow simulator VISSIM 8.0 (PTV Group, Karlsruhe, Germany) was used for building the proposed model. The model primarily consisted of Hongqi District Center (HDC) and Guilin District Center (GDC) in Changchun, China. They are all in the old town of inner cities, with a high densitiy of buildings, population and other urban infrastructure, and with a grid traffic network, as shown in Figure 2.  
The distance between arterial streets in these district centers is about 500-700 m. The lane number is four-lanes-bidirectional in these district centers. The speed limit in these district centers is 60 km h. The average vehicle speed is below 20 km/h during peak hours. The average vehicle speed is about 35 km/h during off-peak hours. The traffic composition in these district centers is: cars account for 75%-85%, the public transit buses account for 8%-12%, other vehicles including vans and trucks account for 5%-15%. The design capacity of the road segments in these district centers is about 1200 pcu/h/lane. Because of the discontinuous traffic flow caused by intersection, the real capacity of road segment is below 800 pcu/h/lane. To simplify to model building in the VISSIM simulator, a Sudoku traffic network based on the scenes of HDC and GDC was coded in VISSIM as links and connectors, as shown in Figure 3.  
In the Sudoku traffic network, the traffic network is setting as: (1) the links between the intersections are setting as 600 m. (2) Each arterial road in the traffic network is setting as 2400 m and including three intersections. (3) Each road segment in the traffic network has two lanes, the inside lane is a straightforward lane and the outside lane is a straight and right-turn lane based on the traffic channelization in HDC and GDC. The traffic composition is setting as: cars account for 80%, buses account for 10% and trucks account for 10%. The expected vehicle speed is setting as 45 km/h. The traffic model simulated peak-hour traffic flow state and a sudden fire disaster occurring in these state during the peak hour in urban high-density area. Traffic volume data were categorized in two groups. Traffic volumes at the edge of the traffic network were used to input traffic to the network. The input traffic is setting as 800 pcu/lane/h. The traffic data at the center of the network were used to calibrate the network. Figure 2: Backgrounds urban high-density areas of model building in simulator. (**a**) Hongqi district center in Changchun; (**b**) Guilin district center in Changchun. To validate the model building in the simulation, peak-hours traffic volume and travel time data were collected on the traffic networks of HDC and GDC. The field data of travel volume and travel time obtained from the local traffic agent were collected by loop detectors and video-based intelligent monitoring and recording system. The recording, uploading and statistical interval of field data is 5 min. With several iterations and model running were completed in the VISSIM simulator without sudden fire disasters, the average traffic volumes and travel times of specially assigned locations and links in the Sudoku traffic network were output. The output data interval also was set as 5 min. Then the simulated data of traffic volume and travel time and actual data of traffic volume and travel time are compared in Tables 1 and 2, respectively. The difference in traffic volume between simulated network of Sudoku and the real network of HDC is less than 10.92%. The difference in traffic volume between simulated network of Sudoku and the real network of GDC is less than 9.39%. The difference in average travel time between simulated network of Sudoku and the real network of HDC is less than 15.69%. The difference in average