scholarly journals Patterns of risky driving behaviors among Tuscan adolescent drivers: a cluster analysis

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
V Lastrucci ◽  
F Innocenti ◽  
C Lorini ◽  
A Berti ◽  
C Silvestri ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
High Risk ◽  
Sexual Behaviors ◽  
Road Traffic ◽  
Well Being ◽  
Risky Driving ◽  
Cluster Membership ◽  
Driving Behaviors ◽  
Adolescent Drivers ◽  
Risky Driving Behaviors

Abstract Background Adolescents have a high risk of road traffic accident (RTA) because of their high engagement in risky driving behaviors (RDBs); to date, very few studies have investigated the patterns of RDBs. The aim of the study is to identify distinctive RDBs patterns and to examine their associations with RTAs in a sample of adolescent drivers Methods The EDIT project is a cross-sectional survey carried out in a representative sample (6.824) of Tuscany Region students aged 14-19 years. The study analyses a subsample of students who reported to drive/ride at least once a week (2764). Self-reported frequency in the last year of the following RDBs was determined: talking on phone; texting; using GPS; talking to passengers; smoking; eating; listening to loud music; fatigued driving; speeding; and driving under the influence (DUI) of alcohol or drugs. A cluster analysis was conducted to identify RDBs patterns. A multivariate model was used to evaluate the difference in the risk of RTA across clusters; ANOVA and post-hoc pairwise comparisons were used to further characterize cluster membership Results Four distinct RDBs clusters were identified: “safe”(45.6%), “average”(21.8%), “careless but not DUI”(21.5%) and “reckless and DUI”(11.2%) drivers. When compared with “safe” drivers, “careless but not DUI” and “reckless and DUI” drivers showed a significantly higher risk of RTA (respectively, OR 1.68, 95%CI 1.29-2.18, p < 0.001; OR 2.88; 95%CI 2.10-3.95, p < 0.001). Clusters were characterized by several significant differences in sociodemographic variables, cell-phone use, quality of the relationships with parents, school performances, mental health and well-being, health behaviors, gaming, bullying and risky sexual behaviors Conclusions RDBs evidently occur in typical patterns that are linked with different RTA risks. Several domains of adolescent life seem to be involved in cluster membership. An awareness of this clustering enables to better targeting adolescents at higher risk of RTA Key messages RDBs occur in patterns in adolescents, and indicators of risky behaviors and of mental and social well-being may help to identify RDBs clusters at high risk of road traffic accidents. Multimodal prevention approaches in risky driving behaviors are likely to be more successful than targeting a single behavior in adolescents.

scholarly journals Profiles of Risky Driving Behaviors in Adolescent Drivers: A Cluster Analysis of a Representative Sample from Tuscany Region (Italy)

2021 ◽  
Vol 18 (12) ◽  
pp. 6362
Author(s):  
Vieri Lastrucci ◽  
Francesco Innocenti ◽  
Chiara Lorini ◽  
Alice Berti ◽  
Caterina Silvestri ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
High Risk ◽  
Representative Sample ◽  
Road Traffic ◽  
Risky Behaviors ◽  
Cross Sectional ◽  
Risky Driving ◽  
Cluster Membership ◽  
Driving Behaviors ◽  
Risky Driving Behaviors

(1) Background: Research on patterns of risky driving behaviors (RDBs) in adolescents is scarce. This study aims to identify distinctive patterns of RDBs and to explore their characteristics in a representative sample of adolescents. (2) Methods: this is a cross-sectional study of a representative sample of Tuscany Region students aged 14–19 years (n = 2162). The prevalence of 11 RDBs was assessed and a cluster analysis was conducted to identify patterns of RDBs. ANOVA, post hoc pairwise comparisons and multivariate logistic regression models were used to characterize cluster membership. (3) Results: four distinct clusters of drivers were identified based on patterns of RDBs; in particular, two clusters—the Reckless Drivers (11.2%) and the Careless Drivers (21.5%)—showed high-risk patterns of engagement in RDBs. These high-risk clusters exhibited the weakest social bonds, the highest psychological distress, the most frequent participation in health compromising and risky behaviors, and the highest risk of a road traffic accident. (4) Conclusion: findings suggest that it is possible to identify typical profiles of RDBs in adolescents and that risky driving profiles are positively interrelated with other risky behaviors. This clustering suggests the need to develop multicomponent prevention strategies rather than addressing specific RDBs in isolation.

scholarly journals Type A Behavior Pattern, Risky Driving Behaviors, and Serious Road Traffic Accidents: A Prospective Study of the GAZEL Cohort

2005 ◽  
Vol 161 (9) ◽  
pp. 864-870 ◽  
Author(s):  
Hermann Nabi ◽  
Silla M. Consoli ◽  
Jean-François Chastang ◽  
Mireille Chiron ◽  
Sylviane Lafont ◽  
...  
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Behavior Pattern ◽  
Type A Behavior ◽  
Road Traffic Accidents ◽  
Risky Driving ◽  
Type A Behavior Pattern ◽  
Driving Behaviors ◽  
A Prospective Study ◽  
Risky Driving Behaviors

Association of Environmental Risk Factors and Personality Traits With Risky Driving Behaviors in a Sample of Young Adults From Saudi Arabia

2021 ◽  
pp. 0272684X2110049
Author(s):  
Kevin M. Beaver ◽  
Mohammed Said Al-Ghamdi ◽  
Ahmed Nezar Kobeisy
Keyword(s):  
Risk Factors ◽  
Saudi Arabia ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Self Control ◽  
Risky Driving ◽  
Male And Female ◽  
Driving Behaviors ◽  
Saudi Arabians ◽  
Risky Driving Behaviors

Road traffic accidents represent a serious problem in the Kingdom of Saudi Arabia (KSA), with rates of such accidents far exceeding the rates in developed nations. Even so, there remains relatively little knowledge regarding the driving behaviors among Saudi Arabians. The current study sought to address this gap in the literature by examining the environmental and trait-based contributors to risky driving behaviors among male and female drivers in the KSA. To do so, a sample of college students from a large university in the KSA was analyzed. The results revealed that delinquent peers, low levels of self-control, and higher levels of driving anger were associated with involvement in risky driving behaviors for both male and female drivers. Understanding the interconnections among peers, self-control, anger, and risky driving behaviors may provide some insight into how to reduce risky driving behaviors. Focusing on ways to reduce exposure to risk factors for risky driving behaviors may be one strategy for reducing these types of driving behaviors.

Perceived Self-Efficacy and Risky Driving Behaviors

2010 ◽  
Vol 69 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Nolwenn Morisset ◽  
Florence Terrade ◽  
Alain Somat
Keyword(s):  
Self Efficacy ◽  
Risky Driving ◽  
Driving Behaviors ◽  
Conduite Automobile ◽  
Risky Driving Behaviors

Les recherches dans le domaine de la santé, et notamment en matière de conduite automobile, attestent que le jugement subjectif du risque (comparatif et absolu) et l’auto-efficacité perçue sont impliqués dans les comportements à risque. Cette étude avait pour objectif d’étudier l’influence de l’auto-efficacité perçue sur le jugement subjectif du risque, évalué au moyen d’une mesure indirecte, et de tester le rôle médiateur de ce facteur entre l’auto-efficacité perçue et les comportements auto-déclarés. Les participants, 90 hommes, lisaient deux scénarii décrivant les deux comportements les plus impliqués dans l’accidentologie: la vitesse et l’alcool au volant. Les résultats ne montrent pas de lien significatif entre l’auto-efficacité perçue et le score de jugement comparatif mais une relation significative avec les deux évaluations absolues du risque (autrui et soi). De plus, le jugement absolu du risque pour soi médiatise partiellement la relation entre auto-efficacité perçue et comportements auto-déclarés relatifs aux deux risques routiers étudiés.

Real-Time Prediction of Vehicle Trajectories for Proactively Identifying Risky Driving Behaviors at High-Speed Intersections

2018 ◽  
Vol 2672 (38) ◽  
pp. 233-244 ◽  
Author(s):  
Chaopeng Tan ◽  
Nan Zhou ◽  
Fen Wang ◽  
Keshuang Tang ◽  
Yangbeibei Ji
Keyword(s):  
Phase Transition ◽  
High Speed ◽  
Red Light ◽  
Risky Behaviors ◽  
Risky Driving ◽  
Red Light Running ◽  
Driving Behaviors ◽  
Vehicle Trajectories ◽  
Single Vehicle ◽  
Risky Driving Behaviors

At high-speed intersections in many Chinese cities, a traffic-light warning sequence at the end of the green phase—three seconds of flashing green followed by three seconds of yellow—is commonly implemented. Such a long phase transition time leads to heterogeneous decision-making by approaching drivers as to whether to pass the signal or stop. Therefore, risky driving behaviors such as red-light running, abrupt stop, and aggressive pass are more likely to occur at these intersections. Proactive identification of risky behaviors can facilitate mitigation of the dilemma zone and development of on-board safety altering strategies. In this study, a real-time vehicle trajectory prediction method is proposed to help identify risky behaviors during the signal phase transition. Two cases are considered and treated differently in the proposed method: a single vehicle case and a following vehicle case. The adaptive Kalman filter (KF) model and the K-nearest neighbor model are integrated to predict vehicle trajectories. The adaptive KF model and intelligent driver model are fused to predict the following vehicles’ trajectories. The proposed models are calibrated and validated using 1,281 vehicle trajectories collected at three high-speed intersections in Shanghai. Results indicate that the root mean square error between the predicted trajectories and the actual trajectories is 5.02 m for single vehicles and 2.33 m for following vehicles. The proposed method is further applied to predict risky behaviors, including red-light running, abrupt stop, aggressive pass, speeding pass, and aggressive following. The overall prediction accuracy is 95.1% for the single vehicle case and 96.2% for the following vehicle case.

scholarly journals Identifying and Labeling Potentially Risky Driving: A Multistage Process Using Real-World Driving Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Charles Marks ◽  
Arash Jahangiri ◽  
Sahar Ghanipoor Machiani
Keyword(s):  
Real World ◽  
Traffic Accidents ◽  
Network Models ◽  
Multistage Process ◽  
Neural Network Models ◽  
Risky Driving ◽  
Vehicle Accidents ◽  
Driving Behaviors ◽  
Novel Approach ◽  
Risky Driving Behaviors

Every year, over 50 million people are injured and 1.35 million die in traffic accidents. Risky driving behaviors are responsible for over half of all fatal vehicle accidents. Identifying risky driving behaviors within real-world driving (RWD) datasets is a promising avenue to reduce the mortality burden associated with these unsafe behaviors, but numerous technical hurdles must be overcome to do so. Herein, we describe the implementation of a multistage process for classifying unlabeled RWD data as potentially risky or not. In the first stage, data are reformatted and reduced in preparation for classification. In the second stage, subsets of the reformatted data are labeled as potentially risky (or not) using the Iterative-DBSCAN method. In the third stage, the labeled subsets are then used to fit random forest (RF) classification models—RF models were chosen after they were found to be performing better than logistic regression and artificial neural network models. In the final stage, the RF models are used predictively to label the remaining RWD data as potentially risky (or not). The implementation of each stage is described and analyzed for the classification of RWD data from vehicles on public roads in Ann Arbor, Michigan. Overall, we identified 22.7 million observations of potentially risky driving out of 268.2 million observations. This study provides a novel approach for identifying potentially risky driving behaviors within RWD datasets. As such, this study represents an important step in the implementation of protocols designed to address and prevent the harms associated with risky driving.

E-Bike Riders Risky Driving Behaviors Questionnaire

2018 ◽  
Author(s):  
Chen Wang ◽  
Chengcheng Xu ◽  
Jingxin Xia ◽  
Zhendong Qian
Keyword(s):  
Risky Driving ◽  
Driving Behaviors ◽  
Risky Driving Behaviors
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