Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention
Nov 20, 2025
INTRODUCTION: Road traffic injuries (RTIs) rank among the top causes of mortality and disability in children and adolescents, resulting in substantial socioeconomic impacts.
Existing intersection safety analysis studies have primarily focused on macro-level static infrastructure and highly aggregated traffic features. The emergence of Connected Vehicle (CV) has enabled researchers to extract micro-level driving behavior ...
This paper aims to examine the influence mechanism of education level as a key situational factor in the relationship between risk perception and behavioral response, encompassing both behavioral intention and preparatory behavior. Utilizing non-para...
To deepen the understanding of the impact of car-following driving style (CFDS) on traffic conflict risk and address the lack of clear CFDS evaluation metrics, this study proposes an improved CFDS metric based on the Asymmetric Behavior (AB) theory. ...
Despite extensive research on the impact of individual and environmental factors on negative risk-taking behaviors, the understanding of these factors' influence on positive risk-taking, and how it compares to negative risk taking, remains limited. T...
Aesthetics has always been an advanced requirement in road environment design, because it can provide a pleasant driving experience and guide better driving behavior for human drivers. However, it remains unknown whether aesthetics-based road environ...
The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to ...
BACKGROUND: Suicide is the second-leading cause of death among adolescents and is associated with clusters of suicides. Despite numerous studies on this preventable cause of death, the focus has primarily been on single nations and traditional statis...
The use of machine learning techniques to identify problem gamblers has been widely established. However, existing methods often rely on self-reported labeling, such as temporary self-exclusion or account closure. In this study, we propose a novel ap...
Normative and descriptive models have long vied to explain and predict human risky choices, such as those between goods or gambles. A recent study reported the discovery of a new, more accurate model of human decision-making by training neural networ...
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