The application and extension of the theory of planned behavior to an analysis of delivery riders' red-light running behavior in China.

• The theory of planned behavior was used to investigate the behavior of delivery riders running red lights. • The structural equation model was used to predict delivery riders running red lights. • Attitude, conformity tendency and the traffic environment were significant predictors in the modified TPB model. • The results of multiple group analysis show that differences between the means of psychological variables of different population groups. • Some intervention measures are obtained by analyzing the influencing factors of running red lights. Delivery riders, an occupation that has emerged from China's booming E-commerce industry, have attracted widespread attention due to their red-light running (RLR) and high accident rates. This study aimed to utilize the theory of planned behavior (TPB) to investigate the psychological characteristics of delivery riders' RLR intentions. A survey questionnaire was designed to collect data, including information regarding the extended variables, the basic components of the TPB and demographic characteristics. The survey was conducted in Xi'an, and 228 complete questionnaires were collected. Structural equation modeling was used to examine the data, and a multiple group analysis of the demographic variables was conducted. The results showed that the expanded TPB model had a better model fit and higher variance explanation than the original TPB model. Extended constructs, i.e., conformity tendency (CT) and the traffic environment (TE), were significant predictors, and attitude was the strongest predictor of all the examined variables related to RLR intentions. Finally, the path parameters of the expended TPB model were adapted for different demographic groups, and some differential effects were also found. These results could provide a basis for the design of intervention measures and safety education schemes by delivery platforms and traffic management departments to reduce RLR behavior among delivery riders. [ABSTRACT FROM AUTHOR]