In recent years, the increasing rate of road crashes involving cyclists with a disproportionate overrepresentation in injury statistics has become a major concern in road safety and public health. However, much remains unknown about factors contributing to cyclists’ high crash rates, especially those related to personal characteristics. This study aims to explore the influence of cyclist personality traits and cycling behaviors on their road safety outcomes using a mediated model combining these constructs. A total of 628 cyclists completed an online questionnaire consisting of questions related to cycling anger, impulsiveness, normlessness, sensation seeking, risky cycling behaviors, and involvement in crash-related conditions in the past year. After the psychometric properties of the employed scales were examined, the relationships among the tested constructs were investigated using structural equation modeling. The results showed that cyclists’ crash risks were directly predicted by risky cycling behaviors and cycling anger, and the effects of cycling anger, impulsiveness, as well as normlessness on crash risks, were mediated by cycling behaviors. The current findings provide insight into the importance of personality traits in impacting cycling safety and could facilitate the development of evidence-based prevention and promotion strategies targeting cyclists in China.

Periodic measurements of sight distance on as-built roads and subsequent removal of sight obstructions are important for guaranteeing highway safety. In this paper, an accurate and efficient framework is proposed for automated detection of obstacles restricting sight distance on highways using mobile laser scanning (MLS) data. The developed framework was implemented in MATLAB (version: 2020a) and operates along the mapping trajectory recorded in the MLS data. A linear index-based segmentation technique was used to efficiently segment MLS point clouds; based on this, methods for identifying target points, removing on-road noise, and detecting sight obstacles were then developed. The target points for sight obstacle detection were derived from the pavement surface points, which were identified via a similarity-and-connectivity-based technique. Considering that on-road vehicle noise may adversely affect the detection of sight obstructions, a data-refinement procedure was developed to remove them and to fill the missing point regions they caused. For each sight point in the mapping trajectory, a segmentation-based algorithm was applied to achieve fast sight obstacle detection. Tests on MLS data from two real-world highways in the case study showed that the proposed framework detected sight obstructions on the combined highway alignments in the presence of noise. The procedure detected sight obstacles at each sight point within 0.2 s, with limited computational power. Therefore, it can be applied in real-world projects and will be of interest to researchers and practitioners in this field.

Objective: In the last few decades, the growing popularity of e-bikes in China has raised public concerns regarding an increasing number of fatalities and injuries involving e-bikes. Although previous studies have explored the impacts of personality on driving behaviors of automobile drivers and motorcyclists, little attention has been paid to safety-related issues involving e-bikers from the aspect of their personality traits and sociocognitive variables. The aim of this study was to examine the effects of personality on e-bikers’ risk-taking behaviors and test the effectiveness of the model proposed by Ulleberg and Rundmo ( 2003 Ulleberg P , Rundmo T . 2003. Personality, attitudes and risk perception as predictors of risky driving behavior among young drivers. Saf Sci. 41(5):427–443. [Crossref], [Web of Science ®] , [Google Scholar] ) among e-bikers. Methods: Four hundred and five Chinese e-bikers aged 16 to 61 completed a self-administrated questionnaire, which included questions investigating their demographics, personality traits (anger, altruism, sensation-seeking, normlessness), risk perceptions, safety attitudes, and risky riding behaviors. The reliability and validity of all scales were first examined through reliability analysis and principal component analysis, respectively, and a structural equation model was developed and fitted to test the relationships among e-bikers’ personality traits, risk perceptions, safety attitudes, and risky riding. Results: A satisfactory level of reliability and validity was reached for all variables. Anger, altruism, sensation-seeking, and normlessness were all significantly related to e-bikers’ risk perceptions and unsafe riding, and only altruism correlated significantly to safety attitude. For 2 sociocognitive variables, safety attitudes was directly and negatively related to respondents’ risky riding, and risk perception only exerted impacts on riding behaviors by affecting safety attitudes. Conclusions: Personality traits of e-bikers impacted their riding behaviors both directly and indirectly, and sociocognitive variables played an intermediate role in the personality–behavior relationship. The results revealed the importance of personality traits in influencing e-bikers’ risky riding and also verified the applicability of the personality–behavior model proposed by Ulleberg and Rundmo ( 2003 Ulleberg P , Rundmo T . 2003. Personality, attitudes and risk perception as predictors of risky driving behavior among young drivers. Saf Sci. 41(5):427–443. [Crossref], [Web of Science ®] , [Google Scholar] ) among e-bikers. The findings of this study may provide an empirical basis for evidence-based safety interventions for e-bikers in China.

Roadside monitoring Lidars (RMLs) will be a crucial part of the future intelligent transportation system. Current approaches for optimizing RMLs’ placement at intersections work in hypothetical environments which do not well reflect real-world situations. This article proposes a new virtual method (VM) for optimizing the deployment of RMLs at as-built intersections. The proposed VM operates in a virtual environment where both static background and dynamic agents are modeled by dense point clouds. The agents are driven by real-world motion data. Using RMLs’ parameters as inputs, a coarse-to-fine subsampling approach is developed to generate laser scans in the virtual world. An objective function is then defined by comparing the agents’ points in the generated laser scan sequences against their original models. Bayesian optimization is applied to maximize the objective function by setting the RMLs’ positions and poses as decision variables. Besides, batch processing strategy and parallel computing are used to accelerate the optimization process. The effectiveness of the proposed VM is demonstrated in a case study. The VM shall help road administrators make decisions on RMLs’ deployment at as-built intersections.

Truck automation is emerging as an innovative technology with benefits in traffic safety and the economy to revolutionize freight traffic. Despite these benefits, the potential negative or positive effects of different driving automation levels (from no automation to full automation) on highway geometry remained to be determined. In this study, differences related to sight distance characteristics among varied automation levels were firstly discussed and calibrated. Then, seven analysis scenarios of typical levels were proposed. Based on each level with tailored characteristics, the current models of geometric design elements including the required stopping sight distance, horizontal sight line offset, and lengths of vertical curves were revised. Finally, impacts of each level on computed values of those elements were evaluated. Results show that high or full driving automation could substantially lower the requirements of geometric design. Active safety systems have a similar role but with less significant effects. Differently, the driver assistance and partial or conditional automation systems put a higher demand on the road geometric design in terms of driving safety. Outcomes of this study can be used to design real-world geometry of dedicated lanes and provide a methodological basis for the operation of different driving automation features.