# A Vehicle Conflict Risk Identification Method Based on an Improved Intelligent Driver Model
## 1 Introduction  
With the rapid advancement of global urbanization, the proportion of urban population worldwide exceeded 57% in 2023 [1]. This growth has led to an explosive increase in transportation demand and the overloading of road networks. Consequently, traffic flow complexity has risen exponentially, characterized by frequent acceleration and deceleration behaviors due to higher vehicle density [2], the spatiotemporal interweaving of multi-modal transportation [3] (including motor vehicles, non-motorized vehicles, and pedestrians), and the cascading effects of sudden traffic events such as congestion and accidents [4]. This complex dynamic system has exacerbated road safety challenges. According to the World Health Organization (WHO), road traffic accidents caused 1.19 million deaths globally in 2021, with an average of 15 fatalities per 100,000 people. Additionally, these accidents resulted in economic losses equivalent to 1-3% of the gross national product [5]. In 2023, motor vehicle traffic accidents in the United States resulted in a total of 40,990 fatalities, with a fatality rate of 1.26 deaths per 100 million vehicle miles traveled (VMT) [6]. Concurrently, in China, there were 254,738 road traffic accidents reported in the same year, leading to 60,028 deaths and direct economic losses amounting to 1.18 billion yuan [7]. These statistics underscore the urgency of research on vehicle conflict risk identification and prevention. The essence of vehicle conflict risk lies in the overlap of spatiotemporal trajectories and the loss of control in interactions among traffic participants. Traditional traffic safety research primarily relies on post hoc analysis of historical accident data, which suffers from significant time lags and limited coverage of potential risk scenarios [8; 9]. In highly dynamic urban road networks, conflict risks often arise from the coupling effects of multiple factors, including traffic flow dynamics, road conditions, weather, and driver behavior [10; 11; 12; 13]. This complexity necessitates the integration of real-world experiments and microscopic simulations for accurate traffic conflict risk assessment and prediction. Recent studies have combined real-world experiments with microscopic simulations to predict vehicle conflict probabilities. [PERSON] et al. [14] utilized the VISSIM simulation platform to identify vehicle-pedestrian conflicts at urban intersections based on time to collision (TTC) and post-encroachment time (PET), achieving an accuracy rate exceeding 90% in conflict frequency identification. [PERSON] et al. [15] analyzed the influence mechanism of autonomous vehicles on traffic conflicts by optimizing VISSIM parameters using genetic algorithms. [PERSON] et al. [16] conducted a collaborative simulation of Prescan and VISSIM to identify traffic risks at highway entrances and exits. [PERSON] [17] employed VISSIM to study entrance roads with varying linear and traffic flow parameters, establishing a Poisson-based conflict prediction model. However, the inherent randomness of simulation tools often leads to deviations from real-world traffic characteristics. While microscopic traffic simulations can provide intuitive traffic flow representations, they struggle with dynamic adaptability in complex scenarios, failing to accurately model instantaneous changes and sudden events [18; 19]. For example, car-following models in microscopic simulations, typically based on theoretical driver behavior [20; 21], face limitations in parameter adjustments, hindering accurate conflict risk predictions under varying traffic conditions. In parallel, advancements in artificial intelligence (AI) have introduced innovative approaches for traffic conflict detection and risk prediction, leveraging techniques such as computer vision, deep learning, and predictive analytics to address the growing complexity of modern transportation systems [22]. For instance, [PERSON] et al. [23] proposed a bi-level framework for real-time crash risk forecasting at signalized intersections.