Search Results (92)

Road safety is a global priority, and driver behavioral factors are among its most critical determinants. Although the literature has advanced in characterizing driving styles using psychometric instruments such as the Multidimensional Driving Style Inventory (MDSI), an empirical gap persists in the simultaneous integration of latent behavioral profiles, driving exposure, and road safety outcomes, particularly in Latin American contexts and across generational extremes. This study examined the relationship between latent driving style profiles and road safety outcomes among young (18–25 years) and older (≥65 years) licensed drivers in Ecuador, while evaluating the moderating role of driving exposure. A structured survey based on the MDSI was administered to 833 active drivers, and data were analyzed using Latent Profile Analysis (LPA) and binary logistic regression. The six-profile solution was selected according to the Bayesian Information Criterion (BIC = 11,655.07), with acceptable classification quality (entropy = 0.860; minimum posterior probability = 0.808); for inferential parsimony, these profiles were subsequently consolidated into three analytically interpretable categories: Predominantly Careful, Predominantly Risky, and Distress-Reduction. The Predominantly Risky profile was significantly associated with higher odds of traffic accident involvement (OR = 2.76, 95% CI [1.55, 4.93]), whereas the Distress-Reduction profile showed substantially higher odds of receiving traffic infraction fines (OR = 4.74, 95% CI [1.69, 13.34]). The composite driving exposure index was a robust predictor across both models (accident model: OR = 2.82, 95% CI [1.60, 5.14]; fine model: OR = 1.87, 95% CI [1.29, 2.74]). In addition, a significant interaction was observed between the Predominantly Risky profile and driving exposure in the model predicting traffic infraction fines, suggesting that exposure amplified sanction risk within this behavioral category. Older drivers showed a substantially higher representation of the Distress-Reduction profile than young drivers. These findings underscore the utility of person-centered approaches for identifying heterogeneous driver configurations and for designing profile-differentiated road safety interventions; from a practical perspective, these results support the development of targeted road safety programs that integrate behavioral profile screening with exposure-based risk management for young and older drivers. Full article

Despite extensive research on traffic safety, the complex, non-linear spatial discrepancy between risky driving and actual accidents remains a significant challenge to quantify within diverse urban contexts. This study investigates the non-linear relationship between grid-level risky driving patterns and traffic accident occurrence in Daejeon, Korea, examining how these associations vary across different urban contexts. Using data collected from July 2023 to June 2024, the analysis incorporates GPS-based risky driving indicators, including rapid acceleration, deceleration, and sudden maneuvers from general passenger vehicles, thereby overcoming the limitations of previous studies reliant on commercial vehicle data. By adopting an H3-based spatial grid system, the study classifies areas into four quadrants based on median values of risky behaviors and accident counts, further categorizing them into “Matched” and “Mismatched” types to identify spatial discrepancies. Furthermore, the Explainable Artificial Intelligence (XAI) technique is employed to integrate regional variables—including population density, land use, and transport infrastructure—to uncover the key drivers of accident risks. Providing a significant methodological improvement over traditional linear models, the findings demonstrate that identical driving behaviors can yield different safety outcomes depending on local environmental interactions. Specifically, while driver behavioral factors directly explain accident frequency in matched regions, accident risks in mismatched regions are more significantly shaped by spatial environmental factors, such as green spaces and commercial land use, which override direct behavioral impacts. This study provides a robust framework for developing data-driven, region-specific traffic intervention strategies, including context-aware advanced driver assistance systems (ADAS) and spatially tailored traffic calming, to enhance urban safety. Full article

The effective recognition of risky driving behaviors holds technical potential for supporting accident prevention and sustainable transportation. However, existing intelligent algorithms for optimizing deep learning models in this field often suffer from slow convergence and high errors. This study proposes a novel hybrid model (ICPO-XGBoost) for risky driving behavior classification. The improved crested porcupine optimizer (ICPO) was developed using logistic-tent composite mapping for population initialization, a hybrid mechanism combining refraction opposition-based learning and Cauchy mutation to avoid local optima, and an adaptive variable spiral search with inertia weight to balance global and local search. The ICPO was then employed to optimize the hyperparameters of the XGBoost classifier. The ICPO demonstrated superior optimization accuracy and convergence speed compared to benchmark algorithms. The ICPO-XGBoost model achieved accuracy, precision, recall, and F1 scores of 96.2%, 95.4%, 95.8%, and 95.6%, respectively, for classifying and identifying risky driving behaviors. Compared to various benchmark models, these results represent increases of 12.7–24.8%, 14.8–31.8%, 14.9–31.0%, and 15.0–32.4%, respectively. For specific driving behavior categories (normal driving, slow driving, short-distance tailgating, sudden acceleration/deceleration, frequent lane changing, and forced lane changing), the precision, recall, and F1 scores of the ICPO-XGBoost model fell within the ranges of 84.8–99.2%, 87.5–100.0%, and 86.2–99.2%, respectively. Compared to benchmark models, these metrics show increases of 1.5–75.8%, 5.8–68.1%, and 3.3–72.6%, respectively. Notably, the model significantly improved accuracy in identifying sudden acceleration/deceleration behaviors. The results of this model facilitate the classification and early warning of risky driving behaviors, thereby reducing the frequency of such behaviors, lowering the risk of traffic accidents, and enhancing road traffic safety. Full article

Conventional Safety Performance Functions (SPFs) primarily rely on static exposure measures such as Annual Average Daily Traffic (AADT), often failing to capture real-time, individual-level risky driving behaviors. To address this gap, this study proposes a Risky Driving Indicator (RDI) that integrates large-scale smartphone-based hard braking event data with traffic detector occupancy measures. The RDI was evaluated against traditional models across three specific aggregation levels: AADT, Annual Average Weekday Daily Traffic (AAWDT), and AAWDT excluding the overnight period. A case study was conducted using data from 2021 to 2022, a period coinciding with the COVID-19 pandemic, on South Korea’s busiest freeway to evaluate RDI-based SPFs. The results showed that models using the COM-Poisson framework outperformed traditional volume-based versions, showing superior performance across Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Akaike Information Criterion (AIC) values. These findings confirm that integrating crowdsourced behavioral data enhances predictive accuracy, offering transportation agencies a cost-effective, scalable solution for proactive hotspot identification and dynamic safety monitoring. By improving safety management through scalable and cost-effective mobile sensing, this study contributes to the development of more sustainable highway transportation systems. Full article

This paper examines the association between company reputation and internal control quality. The prior literature suggests that reputation concerns reduce the range of risky choices by management. Building on this idea, we propose that reputation concerns drive high-reputation firms to uphold strong internal control quality, leading to lower internal control risk as reflected by fewer material weaknesses in their internal controls. By analyzing Fortune 1000 companies, our study finds that high-reputation companies are motivated to safeguard their reputation, driven by their need to signal strong performance and by the monitoring pressure from high-quality auditors. As a result, these high-reputation companies are less likely to have internal control material weaknesses, reflecting lower internal control risk and higher internal control quality. Our study enhances the understanding of the role company reputation plays in corporate behavior and decision-making processes. Full article

Road safety ranks among the most apparent concerns in present-day urban existence, with risky driving the most prevalent cause of road crashes. In this paper, we present an external camera video-based automatic hazardous driving behavior detection model for use in smart cities. We addressed the problem with a holistic approach covering data collection to hazardous driving behavior classification including zig-zag driving, risky overtaking, and speeding over a pedestrian crossing. Our strategy employs a specially generated dataset with diverse driving situations under diverse traffic conditions and luminosities. We advocate for a Multi-Speed Transformer model with dual vehicle trajectory data timescale operation to capture near-future actions in the context of extended driving trends. A new contribution lies in our symbiotic system which, apart from the detection of unsafe driving, also assumes the responsibility of triggering countermeasures through a real-time continuous loop with vehicle systems. Empirical results demonstrate the Multi-Speed Transformer’s performance with 97.5% in accuracy and 93% in F1-score over our balanced corpus, surpassing comparison baselines including Temporal Convolutional Networks and Random Forest classifiers by significant amounts. The performance is boosted to 98.7% in accuracy as well as 95.5% in F1-score with the symbiotic framework. They confirm the promise of leading-edge neural architectures paired with symbiotic systems in enhancing road safety in smart cities. The ability of the system to provide real-time risky driving behavior detection with mitigation offers a real-world solution for the prevention of accidents while not restricting driver autonomy, a balance between automatic intervention, and passive monitoring. Empirical evidence on the TRAF-derived corpus, which includes 18 videos and 414 labelled trajectory segments, indicates that the Multi-Speed Transformer reaches an accuracy of 97.5% and an F1-score of 93% under the balanced-training protocol, and in this configuration it consistently surpasses the considered baselines when we use the same data splits and the same evaluation metrics. Full article

This study takes the limited household participation in risky financial markets as its point of departure. Drawing on microdata from the 2019 China Household Finance Survey (CHFS), we construct a multidimensional analytical framework using machine learning methods. The results indicate that this limitation arises from the interplay of multiple dimensions, with significant nonlinear relationships observed between these factors and household investment behavior. Insufficient development of key driving factors constitutes the main barrier to participation in risky financial markets. Feature interaction analysis reveals a “reversal effect” in how urban–rural disparities, economic attention, income level, and social engagement shape participation behavior. Educational attainment and financial literacy act as “threshold conditions” that enable economic attention to translate into actual investment decisions. The heterogeneity analysis further shows that households at different life-cycle stages as well as across urban–rural settings exhibit distinct participation patterns. These findings provide data-driven insights that can inform policies to promote financial inclusion, enhance investor education, and strengthen household risk management practices. Full article

Background: Adolescence is marked by heightened reward sensitivity and incomplete maturation of cognitive control, creating conditions that favor engagement in risky behaviors. Traditional self-report methods often overlook the fast, automatic processes—such as attentional biases, approach–avoidance tendencies, and associative schemas—that shape adolescent decision-making in real time. Aims: This Perspective aims to synthesize recent (2018–2025) advances in the study of implicit measures relevant to adolescent risk behaviors, evaluate their predictive value beyond explicit measures, and identify translational pathways for prevention and early intervention. Methods: A narrative synthesis was conducted, integrating evidence from eye-tracking, drift-diffusion modeling, approach–avoidance tasks, single-category implicit association tests, ecological momentary assessment (EMA), and passive digital phenotyping. Emphasis was placed on multi-method phenotyping pipelines and on studies validating these tools in adolescent populations. Results: Implicit indices demonstrated incremental predictive validity for risky behaviors such as substance use, hazardous driving, and problematic digital engagement, outperforming self-reports in detecting context-dependent and state-specific risk patterns. Integrative protocols combining laboratory-based measures with EMA and passive sensing captured the influence of peer presence, affective state, and opportunity structures on decision-making. Mobile-based interventions, including approach bias modification and attention bias training, proved feasible, scalable, and sensitive to change in implicit outcomes. Acoustic biomarkers further enhanced low-burden state monitoring. Conclusions: Implicit measures provide a mechanistic, intervention-sensitive complement to explicit screening, enabling targeted, context-aware prevention strategies in adolescents. Future priorities include multi-site validations, school-based implementation trials, and the use of implicit parameter change as a primary endpoint in prevention research. Full article

Traffic safety continues to be a pressing worldwide issue, with young drivers especially exposed to accidents because of limited experience, reckless behaviors, and risky practices such as driving under the influence of alcohol or other substances. In this scenario, reliable methods to evaluate psychomotor and sensory abilities essential for safe driving are highly needed. This study presents the development of a Virtual Reality (VR) prototype aimed at enhancing psychometric testing. The platform incorporates immersive environments to assess peripheral vision, reaction time, and motor accuracy, implemented with Oculus Quest 2, Blender, and Unity. The VR-based system was validated through black-box testing and user satisfaction surveys with a sample of 80 licensed drivers in single-session evaluations. The findings demonstrate that VR increases both precision and realism in psychomotor evaluations: 81.25% of participants perceived the scenarios as realistic, and 85% agreed that the system effectively measured critical driving skills. While a few users experienced minor discomfort, 97.5% recommended its application in practical assessments. This study highlights VR as a robust alternative to conventional psychometric/psychotechnical tests, capable of improving measurement reliability and user engagement and paving the way for more efficient and inclusive driver training initiatives. Full article

Background: Truck drivers are a vital workforce sustaining Thailand’s freight transport, particularly in Northeastern Thailand (Isan), a major logistics hub connecting with Laos, Vietnam, and Cambodia via Highway No. 2 and the AEC network. However, these drivers face disproportionately high risks of severe road accidents due to occupational factors such as fatigue, time pressure, and long-distance driving. Methods: This study developed and validated a second-order confirmatory factor analysis (CFA) model to examine the multidimensional structure of risky driving behavior among Thai truck drivers. Grounded in the Driver Behavior Questionnaire (DBQ), the framework was extended to include seven dimensions: traffic violations, errors, lapses, aggressive behavior, substance use, technology-related distractions, and pedestrian-related risks. Results: Data were collected from 400 truck drivers in Isan using a structured questionnaire. CFA results confirmed the model’s structural validity, with satisfactory fit indices (X²/df = 2.122, CFI = 0.913, TLI = 0.897, RMSEA = 0.053, SRMR = 0.079). Conclusions: The findings reveal that risky driving behavior in this group extends beyond traditional DBQ categories, incorporating emerging risks specific to the commercial transport environment. This framework can be effectively utilized for risk assessment, behavioral screening, and the development of targeted safety interventions for this high-risk occupational group. Full article

Truck drivers play a pivotal role in global freight transport systems, yet their occupational and behavioral risk exposures make them a priority population in road safety research. This systematic review examines the factors influencing risky driving behaviors among truck drivers and their impacts on road safety outcomes. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, the review aimed to identify hazardous driving behaviors, the internal and external factors contributing to these behaviors, and their consequences for traffic safety. Inclusion criteria targeted original research published in English between 2009 and 2024 specifically focused on truck driver behavior and road safety outcomes. Systematic searches across PubMed, Scopus, Web of Science, and IEEE Xplore yielded 104 studies meeting these criteria. The synthesis revealed prevalent risky behaviors—such as speeding, fatigue-related impairments, distracted driving, and substance use—driven by internal factors (e.g., health conditions, psychological stress) and external pressures (e.g., occupational demands, regulatory constraints). These behaviors were consistently associated with increased crash risk. Nonetheless, limitations including the exclusion of non-English studies, reliance on self-reported data, and lack of standardized metrics constrained cross-study comparability and generalizability. Effective interventions identified include fatigue management programs, driver monitoring technologies, and positive safety climates. Findings underscore the urgent need for evidence-based, multifaceted strategies to enhance truck driver safety and inform policy, industry practices, and future research. Full article

This study developed and validated an integrated theoretical framework combining the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to investigate how driving assistance technologies (DATs) influence risky driving behaviors among young urban Chinese drivers. Based on this framework, we proposed and tested several hypotheses regarding the effects of psychological and technological factors on risky driving intentions and behaviors. A survey was conducted with 495 young drivers in Shaoguan, Guangdong Province, examining psychological factors, technology acceptance, and their influence on risky driving behaviors. Structural equation modeling revealed that the integrated TPB-TAM explained 58.3% of the variance in behavioral intentions and 42.6% of the variance in actual risky driving behaviors, significantly outperforming single-theory models. Attitudes toward risky driving (β = 0.287) emerged as the strongest TPB predictor of behavioral intentions, while perceived usefulness (β = −0.172) and perceived ease of use (β = −0.113) of driving assistance technologies negatively influenced risky driving intentions. Multi-group analysis identified significant gender and driving experience differences. Logistic regression analyses demonstrated that model constructs significantly predicted actual traffic violations and accidents. These findings provide theoretical insights into risky driving determinants and practical guidance for developing targeted interventions and effective traffic safety policies for young drivers in urban China. Full article

This study investigates the association between aggressive driving behavior and elderly pedestrian traffic accidents using the Explainable Artificial Intelligence (XAI) method. This study focuses on Seoul, South Korea, where an aging population and urban challenges create a pressing need for pedestrian safety research. The analysis reveals that aggressive driving behaviors, particularly rapid acceleration, rapid deceleration, and speeding, are the most influential factors on the frequency of and deaths from elderly pedestrian traffic accidents. In addition, several built environments and demographic factors such as the number of crosswalks and elderly population play varying roles depending on the spatial match or mismatch between risky driving areas and accident spots. The findings of this study underscore the importance of tailored interventions including well-lit crosswalks, traffic calming measures, and driver education, to reduce the vulnerabilities of elderly pedestrians. The integration of XAI methods provides transparency and interpretability, enabling policymakers to make data-driven decisions. Expanding this approach to other urban contexts with diverse characteristics could validate and refine the findings, contributing to a comprehensive strategy for improving pedestrian safety globally. Full article

The paper applies the prototype willingness model (PWM) and incorporates components of the theory of planned behavior (TPB), along with deterrence factors, to understand the behavioral intentions, willingness, and recidivism behaviors of individuals penalized for drunk driving. It explores psychological and social factors influencing repeat offenses, focusing on attitudes, subjective norms, prototypes, and deterrence. The PWM outlines two pathways—reasoned (based on intentions) and social reactive (based on willingness). The model helps predict risky behaviors like drunk driving. Thirteen hypotheses are proposed in this study to examine how various factors, such as attitudes, subjective norms, and deterrence, influence willingness, intentions, and behavior. Surveys were conducted among individuals attending road safety classes after being penalized for drunk driving. A total of 1156 individuals participated in the survey, with 855 valid responses collected. The results indicate that behavioral willingness had a stronger impact on recidivism than intention. On the other hand, subjective norms did not significantly affect the intent to reoffend, but attitudes, deterrence, and PBC did. The findings suggest that focusing on behavioral willingness, deterrence, and educational interventions could help reduce repeat drunk driving offenses. The paper offers insights for policymakers to improve prevention strategies, by focusing on the psychological motivators of repeat offenders. Full article

Background: Driving adolescents to more correct food habits and physical activity is crucial to promoting health and avoiding the increase in morbidity and mortality in adulthood. Literature has focused on these behaviors in the adult population, while studies on adolescents are more limited. This study aims to explore the level of knowledge, attitudes, and behaviors regarding nutrition and physical activity to acquire insight into adolescents and identify the associated predictors. Methods: A cross-sectional study was conducted among adolescents aged 10 to 19 years from public middle and high schools randomly selected in the Campania Region, Southern Italy. A self-administered questionnaire, including closed and open-ended questions, assessed socio-demographic and health-related characteristics, dietary habits, physical activity, and sources of health information. Results: Regarding socio-demographic and health-related characteristics, among 1433 adolescents who completed the survey, the mean age was 15.2 years, 50.5% were boys, 16.8% reported having a non-communicable disease, and 18% were overweight or obese. Multivariate analysis showed that older age, male gender, daily breakfast with at least one parent, higher self-rated knowledge on nutrition, awareness of fruit and vegetables consumption recommendations, correct dietary attitudes (daily breakfast, consumption of fruit and vegetables at least once a day, of legumes at least twice a week, and of carbonated sugary drinks less than once a day), the need for additional dietary information, meeting WHO physical activity recommendations, and less than two hours of daily screen time are determinants of a high quality diet score. Conversely, living with a single family member and current smoking were negatively associated with high quality diet. Older age, male gender, risk of alcohol abuse, higher quality diet, and lower mobile phone use are associated with meeting WHO physical activity recommendations. Since we investigated risky behaviors, potential limitations of this study could include social desirability and recall bias. Conclusions: Many adolescents lead unhealthy lifestyles, but younger adolescents and girls appear to be at higher risk of unhealthy behaviors. Targeted initiatives promoting regular physical activity and balanced diets in schools, involving parents and teachers in a collaborative plan, are essential to improving adolescents’ health and well-being. Full article