## 1 Introduction  
Simulation is an effective means of replicating the real world or envisioning future scenarios, particularly in studies involving complex systems, multiple variables, uncertain and dynamic environments, or high-cost experiments [1, 2, 3]. For traffic flow simulation, many software tools have been developed and widely applied, such as VISSIM, SUMO, and others [4, 5, 6]. These tools use various micro driving behavior models to describe traffic performance with different levels of accuracy, ranging from microscopic analysis of driver behavior to macroscopic modeling of traffic flow [7]. In scenarios involving signalized intersections, the signal light is a key factor influencing both efficiency and safety, because the decision-making behavior of drivers during the yellow light phase is characterized by randomness and uncertainty [8, 9, 10]. However, these simulation tools rarely provide a detailed description of driving behavior during the yellow phase and struggle to explore the impact of yellow phase settings on driving behavior and traffic flow. Instead, they define driver decision-making through simple response parameters. This does not align with real-world traffic conditions, especially conditions that are typical in China. Furthermore, the micro driving behavior models used in the simulation tools are typically developed separately based on the characteristics of road spatial structure, such as car-following models, lane-changing models, and yielding models at intersections. When these different knowledge-driven or data-driven models are integrated into a simulation environment, they need to be switched at the junctions of different road spatial structures to ensure that the driving behavior model a vehicle follows aligns with the road spatial structure it is currently in. This transition can negatively affect the smoothness of the simulation between different road spatial structures. Therefore, there is a clear need for a driving behavior model developed with unified logic. Our previous research focused on modeling driving behavior based on the subjective risk perception of drivers under \"risk prediction-motion planning\" logic, which includes car-following and lane-changing, as well as behavior at both signalized and unsignalized intersections [8; 11; 12; 13; 14] and will be described in detail in Section 3. It is particularly important to highlight that we have developed a refined model for stop/go decision-making behavior during the yellow phase, capturing the heterogeneity in drivers' risk acceptance [8]. However, each driver's risk acceptance level has been assumed to be fixed, with his/her inherent uncertainty overlooked. Although broad insights are available about the effects of signal timing characteristics on traffic flow performance [15; 16; 17], little is known about the exact effects of the perception processes involved which may have indirect effects on traffic flow by influencing driver-related characteristics. In this paper, we improve our previous model by incorporating a fuzzy decision-making approach and integrating it into a simulation framework with a particular focus on the setting of yellow light duration. By simulating continuous vehicle movement in an upstream road segment and within an intersection, we investigate the impact of yellow light duration on driver performance at isolated signalized intersections and explain the underlying mechanisms based on risk perception theory. This study extends our previous research, aiming to establish the connection between driving behavior mechanisms and observed traffic phenomena, thereby providing a theoretical foundation and simulation tool for explaining the underlying logic of driver behavior and improving safety and efficiency at signalized intersections.