# A Vehicle Conflict Risk Identification Method Based on an Improved Intelligent Driver Model
## 3 Result  
In order to make the established VISSIM simulation environment closer to the real scene of the road test experiment, it is necessary to check the speed distribution obtained by the simulation environment and the actual speed distribution. When the two distributions are close, it can be considered that the established VISSIM can simulate the real environment. To ensure the established VISSIM simulation environment aligns closely with real-world road test conditions, the simulated speed distribution was rigorously validated against empirical speed distribution data. As illustrated in Figure 6, a close match between the two distributions confirms that the VISSIM model accurately replicates real-world traffic dynamics. As illustrated in Figure 7, parameter optimization involved comparing key statistical metrics--the 15 th percentile, median, and 85 th percentile of vehicle speeds--as well as the morphological alignment of the distribution histograms. Upon achieving convergence between simulated and empirical distributions, the calibration process was deemed complete. This validation confirms that the VISSIM simulation realistically captures traffic network behavior, including vehicle acceleration and deceleration patterns. The calibrated VISSIM data were subsequently integrated into the enhanced intelligent driver model (IDM). To ensure the IDM accurately replicates real-world car-following behavior, critical parameters--including front-vehicle speed and headway--were iteratively calibrated against VISSIM outputs. Given the dynamic variability of preceding vehicle speeds, model validity was confirmed only after achieving close alignment between simulated and empirical results. The calibration outcomes are presented in Figures 8 and 9. The inclusion of traffic signal control in the simulation introduced distinct behavioral patterns, such as periodic speed deceleration to 0 with corresponding waiting intervals and minimized inter-vehicle spacing. These phenomena, evident in the calibrated model, demonstrate its capacity to reflect real-world traffic scenarios under signalized conditions. The finalized simulation parameters are summarized in Table 3.  
Figure 6: VISSIM calibration results. Figure 7: Speed comparison of on-road experiment and VISSIM simulation. (**a**) Speed distribution of on-road experiment. (**b**) Speed distribution of VISSIM simulation. Figure 8: Speed calibration results between IDM and VISSIM simulation. Figure 9: Headway calibration results between IDM and VISSIM simulation. When two vehicles in the same lane are in close proximity, a collision will occur if the following vehicle exceeds the speed of the preceding vehicle and no active avoidance measures are taken. The TTC is defined as the time required for a collision to occur if both vehicles maintain their current states (e.g., speed and distance). It is calculated as the ratio of the relative distance to the relative speed between the two vehicles and is widely used as a metric for assessing traffic conflicts. However, when the speed difference between the two vehicles is minimal during car-following scenarios, the TTC model becomes ineffective. To address this limitation, the ITTC is introduced as a more robust indicator. ITTC values typically range between 0 and 1, with units of 1/s. The frequency distribution of ITTC was fitted to a negative exponential distribution, as illustrated in Figure 11.  
The median and 85 th percentile of the ITTC distribution were selected as thresholds for potential conflict risk (indicating safe following conditions) and general conflict risk, respectively. The corresponding ITTC thresholds are 0.25 (1/s) and 0.48 (1/s), with equivalent TTC values of 4 s and 2.1 s, respectively. The classification of vehicle conflict risk levels and their corresponding thresholds are summarized in Table 4.