Investigating Contributing Factors on Aggressive Driving Based on a Structural Equation Model

Abstract

Tailored countermeasures that may significantly improve road traffic safety can be proposed and implemented if the relationship between various associated factors and aggressive driving is well understood. However, this relationship remains unknown, as driving behavior is complex, and the interrelationships among variables are not easy to identify. Considering this situation, this paper constructed a model based on a structural equation model (SEM) and factor analysis (FA), which is a multivariate statistical analysis technique used to analyze structural relationships. The model is applied in a case study using data from the Shanghai Naturalistic Driving Study. In the case study, 16 variables were grouped into five latent factors in the SEM, and the model fits the data well. Compared with other variables, the results show that age had the most significant positive impact on aggressive driving behavior (older drivers exhibited high aggressive driving frequency). Adverse weather negatively impacted driver behavior (lower speed and high longitude acceleration), which in turn negatively affected aggressive driving behavior. In addition, the results show that driver factors (such as age and sex) were the main factors influencing vehicle use (such as hard acceleration), and the environment was the main factor determining risky scenarios, where safety-critical situations increase. This paper provides a reference for defining and determining aggressive driving and a model for exploring the relationship between driving safety factors and aggressive driving, which can be used in real-world applications for improving driving safety with applications in advanced driver-assistance (ADAS) and traffic enforcement safety control systems.

Recognition and interpretation of aggressive driving behavior for heavy‐duty vehicles based on artificial neural network and SHAP

“…Concerning the precipitation, no precipitation did not increase the likelihood of aggressive driving, but an insignificant amount of precipitation did. Xu et al's (2022) research also confirmed that adverse weather conditions, such as rain and fog, can amplify drivers' anxiety and contribute to aggressive driving behavior. One possible explanation is that the noise and low visibility on rainy days are disturbing and irritating.…”

“…The establishment of the model is also relatively simple. In addition to simple statistical analysis, traditional statistical models such as logistic regression (Oh & Jang, 2017), discrete choice models (Feng et al, 2012), and structural equation models (Xu et al, 2022) are also used.…”