Analysis of risky driving behaviors among bus drivers in China: The role of enterprise management, external environment and attitudes towards traffic safety

A quantitative traffic performance comparison study of bicycles and E-bikes at the non-signalized intersections: Evidence from survey data

As common transportation modes at non-signalized intersections, bicycles and e-bikes have been involved in most traffic crashes. Although a large number of studies have been dedicated to studying the safety problems caused by bicycles and e-bikes, there is still limited attention paid to the differences between them, especially at non-signalized intersections. This paper compares the differences between bicycles and e-bikes based on a self-administered questionnaire. This questionnaire was distributed to bicycle users (N = 453) and e-bike users (N = 439). The personal characteristics, decision-making capacity, the feeling of infrastructure, perceived level of service, and perceived level of risk were adopted as the performance indicators to depict the difference in the study area. Using statistical methodologies and the Structural Equation Model (SEM), key findings indicate that perceived level of service was found to be significantly different between bicycles and e-bikes at most non-signalized intersections. 43.4 % of e-bike riders often or always choose to avoid riding under extreme weather, while 58.7 % of bicycle riders avoid riding under extreme weather. Moreover, compared with bicycles, e-bikes' decision-making capacity is affected more by infrastructure quality. The difference between bicycles and e-bikes highlights the need for differentiated development of cycling safety education and law enforcement.

Will Improved Safety Attitudes Necessarily Curb Unsafe Behavior? Hybrid Method Based on NCA and SEM

As an inevitable product of the development of the construction industry, safety production has attracted more and more attention. In particular, it affects sustainable development. It is important to study the unsafe behavior of individuals. This study integrated the hybrid method of necessary condition analysis (NCA) and structural equation model (SEM). Based on the institutional environment perspective of social cognition theory, an empirical analysis was conducted through field observation and 186 questionnaire data to explore the influence of the institutional environment, safety attitude, and unsafe behavior. The results showed that improved safety attitudes of workers are a key requisite to curb unsafe behavior, and it was confirmed that safety attitude plays a complete mediating role between institutional environment and unsafe behavior. Through the analysis of the necessary conditions and mediating effects of safety attitude, the study deepened the theoretical understanding of the interaction between institutional environment, safety attitude, and unsafe behavior. Also, it provided relevant management suggestions for the construction industry in safety management.

Bayesian spatial correlation, heterogeneity and spillover effect modeling for speed mean and variance on urban road networks

Analyzing speed mean and variance is vital to safety management in urban roadway networks. However, modeling speed mean and variance on structured roads could be influenced by the spatial effects, which are rarely addressed in the existing studies. The inadequacy may lead to biased conclusions when considering vehicle speed as a surrogate safety measure. The current study focuses on developing a Bayesian modeling approach with three types of spatial effects, i.e., spatial correlation, spatial heterogeneity, and spillover effect. To capture the spatial correlation, the study employs the intrinsic conditional autoregressive (ICAR) models, spatial lag models (SLM), and spatial error models (SEM). Spatial heterogeneity and spillover effect are considered by the random parameters approach and spatially lagged covariates (SLCs). Speed data are collected from the float cars running on 134 urban arterials in Chengdu, China. The results indicate that the random parameters ICAR model with SLCs (RPICAR-SLC) outperforms others in terms of goodness-of-fit, accuracy, and efficiency for modeling speed mean, while the random parameters ICAR model (RPICAR) is the best for modeling speed variance. Moreover, RPICAR-SLC and RPICAR models are beneficial to address spatial correlation of residuals, explaining the unobserved influence among the observations, and are less likely to cause biased or overestimated parameters. The study also discusses how traffic conditions, road characteristics, traffic management strategies, and facilities on roadway networks influence speed mean and variance. The findings highlight the importance of multi-type spatial effects on modeling speed mean and variance along the structured roadways.

Association between psychological symptoms and illegal driving behaviors in a sample of Chinese private car drivers

BACKGROUND

Findings on the associations between psychological symptoms and driving behaviors in private car drivers are inadequate.

METHOD

The study consisted of 3,115 private car drivers in Yulin, China. The measurements included socio-demographic data, traffic violations, accidents, and Symptom Checklist-90 (SCL-90). In addition, an ordered logistic regression model was employed to examine the association between each psychological symptom and risky driving behaviors.

RESULTS

The overall prevalence rate of any self-reported psychological symptom was 10.24%, with 9.22% for males and 11.49% for females. Among them, obsessive-compulsive, interpersonal sensitivity, additional items, hostility, and depression were the five most common psychological symptoms, with prevalence rates of 7.90, 6.29, 6.00, 5.91, and 5.62%, respectively. Any psychological symptom factor was associated with a higher risk of traffic violations and accidents. However, the intensity of the correlations varied, with obsessive-compulsive symptoms the strongest in general traffic violations and anxiety symptoms in traffic accidents. All psychological symptoms except phobic anxiety and paranoid ideation contributed to a higher risk of failing the driver's license test.

CONCLUSIONS

The prevalence rate of psychological symptoms was high in private car drivers. This study calls for an urgent need to establish a pilot tertiary prevention strategy to reduce risky driving behaviors through psychological symptom screening and interventions among private car drivers.