# Simulation-Oriented Analysis and Modeling of
###### Abstract  
Distracted driving significantly affects the efficiency and safety of traffic flow. Modeling distracted driving behavior in microscopic traffic flow simulation is essential for understanding its critical impacts on traffic flow. However, due to the influence of various external factors and the considerable uncertainties in behavior characteristics, modeling distracted driving behavior remains a challenge. This study proposed a model which incorporates distraction features into the microscopic traffic flow model to simulate distracted driving behavior. Specifically, the study first examines the characteristics of distracted driving, including the intervals and durations of distraction events, as well as the patterns and environments of distraction. It then introduces distraction parameters into the Intelligent Driver Model (IDM), including reaction time delays and perception deviations in both speed difference and following distance. These parameters are quantified by probabilistic distributions to reflect the uncertainty and individual differences in driving behavior. The model is calibrated and validated using 772 distracted following events from the Shanghai Naturalistic Driving Study (SH-NDS) data. Three patterns of distraction (excessive, moderate, mild) are distinguished and modeled separately. The results show that the model's accuracy surpasses that of the IDM under various road types and traffic volumes, with an average improvement in model accuracy of about 11.30% on expressways with high traffic volume, 4.54% on expressways with low traffic volume, and 4.46% on surface roads. Meanwhile, the model can effectively simulate the variations in reaction times and perceptual deviations in both speed and following distance for different distraction modes at the individual level, maintaining consistency with reality. Finally, the study simulates distracted driving behavior under different road environments and traffic volumes to explore the impact of distracted driving on traffic flow. The simulation results indicate that an increase in the proportion of distraction reduces the efficiency and safety of traffic flow, which is consistent with real-world observations. Since the model considers human distraction factors, it can generate more dangerous driving scenarios in simulations, which holds significant importance for safety-related research. The findings from this study are expected to be helpful for understanding distracted driving behavior and mitigate its negative influence on the efficiency and safety of traffic flow. 2024 24 June 2024 2  
need for models that can more accurately reflect the complexities of driver behaviors to improve the analysis and understanding of traffic flow dynamics. Human factors are considered crucial in contributing to driving behavior, which affects traffic efficiency and safety. Studies have shown that drivers' risk attitudes, driving preferences, and other personal traits play a central role in various traffic flow phenomena that significantly affect traffic efficiency [1; 2], and human factors are the main causes of over 90% of traffic crashes [3; 4; 5]. These findings highlight the necessity of incorporating human factors into microscopic traffic flow models to more accurately simulate microscopic driving behavior, and thereby enhance the understanding of efficiency and safety of traffic flow [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. Among human factors, distraction is a significant factor that poses a considerable threat to traffic flow. Distracted driving can cause driving performance degradation, traffic flow delays, oscillations, and other instability phenomena [17; 18; 19]. Distraction has been proven to affect both lateral and longitudinal driving performance. Several studies have investigated the impact of distraction on lateral performance, such as lane-keeping ability. [PERSON] et al. [20] demonstrated through simulations that the standard deviation of lane position increases when drivers are distracted. [PERSON] et al.