## 2 Literature Review  
Near-misses, also known as traffic conflicts, near-crashes, and safety-critical events [10], are growing in importance for traffic safety studies. This trend is likely driven by their shorter observation periods [12] and increased obtainability [10] due to advancements in technology.  
While various definitions of near-misses exist--such as a traffic event that requires a rapid evasive maneuver [13], or situations where two or more road users come so close to each other that there is a risk of collision [14]--most studies employ the Time to Collision (TTC) metric with a specific threshold to identify these incidents. In previous studies, for example, TTC thresholds of 0.5 s [15], 1.5 s [16], 2.5 s [17], and 6 s [18] have been employed for near-miss identification. Using other time-based measures such as PET [19], Time to Collision with Disturbance (TTCD) [20], and two-dimension TTC (2D-TTC) [21] has also been proposed. These time-based measures, employed for identifying near-misses, have demonstrated correlations with crashes [10]. However, as of now, there is no standardized method for near-miss identification.  
A systematic review [10] suggests that real-world near-miss data collection methods can be categorized into road user-level and facility-level methods. The road user-level method utilizes onboard devices (OBDs) to observe near-misses [22]. The primary function of these OBDs is to measure the distance and relative speed between the ego vehicle and the forward vehicle, enabling the calculation of time-based safety measures. Sensors frequently used include cameras employing computer vision technology [23], radar [24], lidar [25], and their combinations [26]. Since these devices are vehicle-mounted, they can capture near-misses involving the ego vehicle. The sampling of near-miss events is based on the Market Penetration Rate (MPR) of OBD-equipped vehicles and their movements.  
In contrast, the facility-level method involves sensors installed near specific fixed facilities, like roadside cameras [27], microwave radars [28], roadside lidar [29], and weight-in-motion detectors [30]. This method can capture all near-misses within the detection range of the sensors. However, due to often limited sensor detection range, the facility-level method is typically unsuitable and uneconomical for collecting near-misses across wide spaces, such as city-wide road networks.  
Most recent safety estimation studies leveraging near-miss data primarily focus on smaller-scale road facilities like intersections. Roadside cameras or drones are typically utilized to capture road user trajectories at intersections, from which near-misses are extracted to build extreme value models for crash risk estimation [9; 10; 31; 32]. However, the application of this method on a larger scale, such as city-wide road networks, presents challenges. Despite near-miss data providing valuable insights for safety estimation [11], their integration into safety evaluations is seldom seen. For example, the safety performance functions provided by the Highway Safety Manual (HSM) [33] do not incorporate near-miss data. Nevertheless, the integration of real-world near-miss data into the safety evaluation of large-scale road networks presents an interesting avenue for exploration.