# Simulation-Oriented Analysis and Modeling of
###### Abstract
[21] found that distraction leads to an increase in lane exceedance (vehicles deviating from the lane) and lane deviation (tracking errors). [PERSON] et al. [22] also showed that high-information-density distraction tasks result in an increased number of lane deviations. [PERSON] et al. [23] discovered that drivers performing distraction tasks (such as calibration and navigation tasks) exhibit a greater average deviation in lane-change paths compared to when they are not distracted. Additionally, numerous studies have demonstrated that distraction significantly impacts longitudinal car-following performance. [PERSON] et al. [22] found that during distraction tasks, the stability of drivers' speed and following distance deteriorates. [PERSON] et al. [24] showed that distraction results in significant compensatory speed reduction and adversely affects vehicle performance, such as significantly reduced headway and increased braking pressure. [PERSON] et al. [25] found that drivers increase following distance to compensate for the increased task demands associated with distraction. Distraction tasks also significantly degrade vehicle control, target detection, and car-following performance. Approximately 14% of traffic crashes can be attributed to distraction [26]. In the United States, about 20,000 people died in distracted-driving crashes between 2014 and 2019 [27]. Despite this, research is still insufficient regarding microscopic traffic flow models under distracted-driving conditions. [PERSON] and [PERSON] [28] proposed a multi-level microscopic traffic simulation framework in which distraction leads to erroneous driving behaviors. [PERSON] et al. [29] introduced a novel car-following modeling framework that incorporates driving task difficulty to model the impact of distraction on driver decision-making. [PERSON] et al. [30] utilized the Dynamic Time Warping algorithm to identify characteristics of distracted driving behavior, and they expanded the Newell car-following model to include probabilities of driver distraction. [PERSON] and [PERSON] [31] simulated distracted driving behavior to study its impact on car-following model parameters. [PERSON] et al. [32] developed a driver-scanning model under a state of distraction and predicted the risk of crash by combining the driver-scanning model with the basic car-following model. These models typically use fixed parameters to simulate distracted driving, without considering the diversity and uncertainty of driver behavior. Moreover, they do not differentiate distracted driving behavior across different road environments and patterns. Finally, these models have not been applied to explore the impact of distraction on efficiency and safety of traffic flow. It is challenging to simulate distracted driving behavior. Distracted driving is influenced by multiple factors, leading to significant diversities and uncertainties in its behavioral characteristics. First, the surrounding environment plays an important role in distraction. In these cases, drivers' distraction performance may vary. For example, [PERSON] et al. [33] explored the impact of different traffic flow states on distraction. Theyobserved degradation of driving performance due to in-vehicle distractions in both simple and complex highway environments. However, the average driving speed is lower in complex highway environments. Secondly, different patterns of distraction have different impacts on driving performance. [PERSON] et al. [34] analyzed the driver's pedal positions under secondary tasks and found that distraction has three different levels of impact on drivers. These impacts are categorized as primary, moderate, and minor, based on which the distraction tasks are classified. Therefore, this paper attempts to consider the above influencing factors, including road type, traffic volume, and different patterns of distraction, when modeling distracted car-following behavior. In addition, the impact of distraction on driving ability (i.e. perception ability and reaction time) involves significant uncertainties.