# A Vehicle Conflict Risk Identification Method Based on an Improved Intelligent Driver Model
## 2 Methodology
### VISSIM Scenario Development  
VISSIM is a micro-traffic simulation system simulation tool developed by PTV, and it enables the modeling and analysis of traffic flow dynamics on urban roads and highways under diverse traffic conditions [35]. While the native VISSIM platform extracts basic vehicle travel speeds, its secondary development interfaces allow calculation of time headways and relative speeds between vehicles within the simulated network. These parameters were integrated into the intelligent driver model to derive conflict risk indicators. The methodological workflow of VISSIM secondary development is illustrated in Figure 2.  
From the research objectives of this paper, the VISSIM simulation framework incorporates the Wiedemann car-following model, a physiological-psychological driver behavior model originally proposed in 1974. Among them, Wiedemann74 is generally applicable to road traffic within cities, and Wiedemann99 is applicable to intercity highways and expressways. Within this model, speed distribution parameters critically influence road network capacity and operational efficiency. Vehicles initiate overtaking maneuvers when their desired speed exceeds their current speed, provided safe conditions are maintained. Figure 3 illustrates the calibration of desired speed distributions based on empirical driving data, ensuring alignment with real-world traffic dynamics. Figure 1: Experiment route. The horizontal axis denotes the desired vehicle speed, while the vertical axis corresponds to the normalized cumulative frequency (ranging from 0 to 1). To better approximate the stochasticity of real-world traffic, speeds during acceleration and deceleration phases were excluded from the cumulative distribution function (CDF) analysis. A speed threshold of 35 km/h was established, with a sigmoidal distribution centered on the median speed. The study area--Harbin's urban road network--was modeled by sequentially calibrating road geometry, vehicle characteristics, and signal timing parameters to replicate real-world conditions. Driver distraction probability was parameterized at 15%. The  
Figure 3: Expected vehicle speed distribution in VISSIM simulation. Figure 2: Simulation flow chart. Wiedemann 74 car-following model, implemented in the simulation's behavioral module, calculates inter-vehicle distances (d), as defined in Equations (1) and (2). \[d=ax+bx \tag{1}\]  
\[bx=(bx\_add+bx\_mult\times z)\times\sqrt{v} \tag{2}\]  
where \(ax\) is the average parking spacing, with a range of \(\pm 1\) m. In this paper, the value is 2.0 m; \(v\) is the vehicle speed in \(m/s\); \(z\) is a value between \([0,1]\), with 0.5 as the average value of a standard normal distribution and a standard deviation of 0.15; \(bx\_add\) is the additional part of the safety distance, and the value in this paper is 2; and \(bx\_mult\) is the multiple part of the safety distance, and the value in this paper is 3. The key parameters governing urban traffic behavior encompass time headway, following-state entry thresholds, and following sensitivity variables, with their typological classifications and operational ranges detailed in Table 2. Calibration settings for the car-following model parameters are illustrated in Figure 4a. Among them, average parking distance refers to the minimum separation distance between stationary lead and following vehicles. The time headway, measured in seconds (s), quantifies the temporal interval between consecutive vehicles passing a fixed point. This metric serves as a critical indicator of traffic flow density and driver aggressiveness, where smaller values typically correspond to denser traffic or riskier driving behaviors. Follow-up variables represent a microscopic traffic simulation parameter quantifying a driver's responsiveness to changes in the preceding vehicle's speed, acceleration, and spacing. This variable directly influences inter-vehicle safety margins and traffic flow stability.