Journal articles: 'Risky Driving'

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Leal, Nerida, Barry Watson, and Kerry Armstrong. "Risky Driving or Risky Drivers?" Transportation Research Record: Journal of the Transportation Research Board 2182, no. 1 (January 2010): 16–23. http://dx.doi.org/10.3141/2182-03.

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Chen, Shengdi, Qingwen Xue, Xiaochen Zhao, Yingying Xing, and Jian John Lu. "Risky Driving Behavior Recognition Based on Vehicle Trajectory." International Journal of Environmental Research and Public Health 18, no. 23 (November 24, 2021): 12373. http://dx.doi.org/10.3390/ijerph182312373.

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This paper proposes a measurement of risk (MOR) method to recognize risky driving behavior based on the trajectory data extracted from surveillance videos. Three types of risky driving behavior are studied in this paper, i.e., speed-unstable driving, serpentine driving, and risky car-following driving. The risky driving behavior recognition model contains an MOR-based risk evaluation model and an MOR threshold selection method. An MOR-based risk evaluation model is established for three types of risky driving behavior based on driving features to quantify collision risk. Then, we propose two methods, i.e., the distribution-based method and the boxplot-based method, to determine the threshold value of the MOR to recognize risky driving behavior. Finally, the trajectory data extracted from UAV videos are used to validate the proposed model. The impact of vehicle types is also taken into consideration in the model. The results show that there are significant differences between threshold values for cars and heavy trucks when performing speed-unstable driving and risky car-following driving. In addition, the difference between the proportion of recognized risky driving behavior in the testing dataset compared with that in the training dataset is limited to less than 3.5%. The recognition accuracy of risky driving behavior with the boxplot- and distribution-based methods are, respectively, 91% and 86%, indicating the validation of the proposed model. The proposed model can be widely applied to risky driving behavior recognition in video-based surveillance systems.

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Măirean, Cornelia, Grigore M. Havârneanu, Danijela Barić, and Corneliu Havârneanu. "Cognitive Biases, Risk Perception, and Risky Driving Behaviour." Sustainability 14, no. 1 (December 22, 2021): 77. http://dx.doi.org/10.3390/su14010077.

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This study evaluated the relationship between drivers’ cognitive biases (i.e., optimism bias, illusion of control) and risky driving behaviour. It also investigated the mediational role of risk perception in the relationship between cognitive biases and self-reported risky driving. The sample included 366 drivers (Mage = 39.13, SD = 13.63 years) who completed scales measuring optimism bias, illusion of control, risk perception, and risky driving behaviour, as well as demographic information. The results showed that risky driving behaviour was negatively predicted by optimism bias and positively predicted by the illusion of control. Further, risk perception negatively correlated with risky behaviour and also mediated the relation between both optimism bias and illusion of control with risky driving. The practical implications of these results for traffic safety and future research are discussed.

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Bao, Qiong, Hanrun Tang, and Yongjun Shen. "Driving Behavior Based Relative Risk Evaluation Using a Nonparametric Optimization Method." International Journal of Environmental Research and Public Health 18, no. 23 (November 26, 2021): 12452. http://dx.doi.org/10.3390/ijerph182312452.

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Evaluating risks when driving is a valuable method by which to make people better understand their driving behavior, and also provides the basis for improving driving performance. In many existing risk evaluation studies, however, most of the time only the occurrence frequency of risky driving events is considered in the time dimension and fixed weights allocation is adopted when constructing a risk evaluation model. In this study, we develop a driving behavior-based relative risk evaluation model using a nonparametric optimization method, in which both the frequency and the severity level of different risky driving behaviors are taken into account, and the concept of relative risk instead of absolute risk is proposed. In the case study, based on the data from a naturalistic driving experiment, various risky driving behaviors are identified, and the proposed model is applied to assess the overall risk related to the distance travelled by an individual driver during a specific driving segment, relative to other drivers on other segments, and it is further compared with an absolute risk evaluation. The results show that the proposed model is superior in avoiding the absolute risk quantification of all kinds of risky driving behaviors, and meanwhile, a prior knowledge on the contribution of different risky driving behaviors to the overall risk is not required. Such a model has a wide range of application scenarios, and is valuable for feedback research relating to safe driving, for a personalized insurance assessment based on drivers’ behavior, and for the safety evaluation of professional drivers such as ride-hailing drivers.

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Lazuras, Lambros, Richard Rowe, Antonia Ypsilanti, Isabelle Smythe, Damian Poulter, and John Reidy. "Driving self-regulation and risky driving outcomes." Transportation Research Part F: Traffic Psychology and Behaviour 91 (November 2022): 461–71. http://dx.doi.org/10.1016/j.trf.2022.10.027.

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Iversen, Hilde. "Risk-taking attitudes and risky driving behaviour." Transportation Research Part F: Traffic Psychology and Behaviour 7, no. 3 (May 2004): 135–50. http://dx.doi.org/10.1016/j.trf.2003.11.003.

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Li, Zhenming, Siu Shing Man, Alan Hoi Shou Chan, and Jianfang Zhu. "Integration of Theory of Planned Behavior, Sensation Seeking, and Risk Perception to Explain the Risky Driving Behavior of Truck Drivers." Sustainability 13, no. 9 (May 7, 2021): 5214. http://dx.doi.org/10.3390/su13095214.

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Truck-related accidents account for a substantial portion of traffic accidents. Risky driving behavior is a main cause of traffic accidents. Understanding the risky driving behavior of truck drivers is therefore important in reducing truck-related accidents. This study aimed to propose and validate a research model that integrated a theory of planned behavior, sensation seeking, and risk perception to explain the risky driving behavior of truck drivers. A total of 471 valid data were collected from Chinese truck drivers in this study. Structural equation modeling and mediation analysis were used to examine the influence of factors in the research model on the risky driving behavior of truck drivers. Results showed that sensation seeking and risk perception of truck drivers were influential in shaping their intention to drive riskily with the mediation of attitude toward risky driving. Risk perception and attitude toward risky driving also had a negative influence and positive influence on the intention, respectively. On the basis of the findings, practical recommendations for reducing the risky driving behavior of truck drivers were provided for concerned parties.

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Huh, Jason, and Julian Reif. "Teenage Driving, Mortality, and Risky Behaviors." American Economic Review: Insights 3, no. 4 (December 1, 2021): 523–39. http://dx.doi.org/10.1257/aeri.20200653.

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We investigate the effect of teenage driving on mortality and risky behaviors in the United States using a regression discontinuity design. We estimate that total mortality rises by 5.84 deaths per 100,000 (15 percent) at the minimum legal driving age cutoff, driven by an increase in motor vehicle fatalities of 4.92 deaths per 100,000 (44 percent). We also find that poisoning deaths, which are caused primarily by drug overdoses, rise by 0.31 deaths per 100,000 (29 percent) at the cutoff and that this effect is concentrated among females. Our findings show that teenage driving contributes to sex differences in risky drug use behaviors. (JEL I12, J13, J16, R41)

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Mas-Cuesta, Laura, Sabina Baltruschat, Antonio Cándido, and Andrés Catena. "Relationships between Personality Traits and Brain Gray Matter Are Different in Risky and Non-risky Drivers." Behavioural Neurology 2022 (April 5, 2022): 1–15. http://dx.doi.org/10.1155/2022/1775777.

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Personality traits such as impulsivity or sensitivity to rewards and punishments have been associated with risky driving behavior, but it is still unclear how brain anatomy is related to these traits as a function of risky driving. In the present study, we explore the neuroanatomical basis of risky driving behavior and how the level of risk-taking influences the relationship between the traits of impulsivity and sensitivity to rewards and punishments and brain gray matter volume. One hundred forty-four participants with different risk-taking tendencies assessed by real-life driving situations underwent MRI. Personality traits were assessed with self-report measures. We observed that the total gray matter volume varied as a function of risky driving tendencies, with higher risk individuals showing lower gray matter volumes. Similar results were found for volumes of brain areas involved in the reward and cognitive control networks, such as the frontotemporal, parietal, limbic, and cerebellar cortices. We have also shown that sensitivity to reward and punishment and impulsivity are differentially related to gray matter volumes as a function of risky driving tendencies. Highly risky individuals show lower absolute correlations with gray matter volumes than less risk-prone individuals. Taken together, our results show that risky drivers differ in the brain structure of the areas involved in reward processing, cognitive control, and behavioral modulation, which may lead to dysfunctional decision-making and riskier driving behavior.

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Ka, Eunhan, Do-Gyeong Kim, Jooneui Hong, and Chungwon Lee. "Implementing Surrogate Safety Measures in Driving Simulator and Evaluating the Safety Effects of Simulator-Based Training on Risky Driving Behaviors." Journal of Advanced Transportation 2020 (June 19, 2020): 1–12. http://dx.doi.org/10.1155/2020/7525721.

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Human errors cause approximately 90 percent of traffic accidents, and drivers with risky driving behaviors are involved in about 52 percent of severe traffic crashes. Driver education using driving simulators has been used extensively to obtain a quantitative evaluation of driving behaviors without causing drivers to be at risk for physical injuries. However, since many driver education programs that use simulators have limits on realistic interactions with surrounding vehicles, they are limited in reducing risky driving behaviors associated with surrounding vehicles. This study introduces surrogate safety measures (SSMs) into simulator-based training in order to evaluate the potential for crashes and to reduce risky driving behaviors in driving situations that include surrounding vehicles. A preliminary experiment was conducted with 31 drivers to analyze whether the SSMs could identify risky driving behaviors. The results showed that 15 SSMs were statistically significant measures to capture risky driving behaviors. This study used simulator-based training with 21 novice drivers, 16 elderly drivers, and 21 commercial drivers to determine whether a simulator-based training program using the SSMs is effective in reducing risky driving behaviors. The risky driving behaviors by novice drivers were reduced significantly with the exception of erratic lane-changing. In the case of elderly drivers, speeding was the only risky driving behavior that was reduced; the others were not reduced because of their difficulty with manipulating the pedals in the driving simulator and their defensive driving. Risky driving behaviors by commercial drivers were reduced overall. The results of this study indicated that the SSMs can be used to enhance drivers’ safety, to evaluate the safety of traffic management strategies as well as to reduce risky driving behaviors in simulator-based training.

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Bonnett, Laura. "Driving is a risky business." Significance 18, no. 4 (July 28, 2021): 12–15. http://dx.doi.org/10.1111/1740-9713.01546.

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Jonah, Brian A. "Age differences in risky driving." Health Education Research 5, no. 2 (1990): 139–49. http://dx.doi.org/10.1093/her/5.2.139.

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Steinberg, Laurence. "Adolescents' Risky Driving in Context." Journal of Adolescent Health 49, no. 6 (December 2011): 557–58. http://dx.doi.org/10.1016/j.jadohealth.2011.10.001.

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Simons-Morton, Bruce G., Kaigang Li, Johnathon Ehsani, Marie Claude Ouimet, Jessamyn Perlus, and Sheila G. Klauer. "Are Perceptions About Driving Risk and Driving Skill Prospectively Associated with Risky Driving Among Teenagers?" Transportation Research Record: Journal of the Transportation Research Board 2584, no. 1 (January 2016): 39–44. http://dx.doi.org/10.3141/2584-06.

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Liu, Jing, Cheng Wang, Zhipeng Liu, Zhongxiang Feng, and N. N. Sze. "Drivers’ Risk Perception and Risky Driving Behavior under Low Illumination Conditions: Modified Driver Behavior Questionnaire (DBQ) and Driver Skill Inventory (DSI)." Journal of Advanced Transportation 2021 (November 19, 2021): 1–13. http://dx.doi.org/10.1155/2021/5568240.

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Most road crashes are caused by human factors. Risky behaviors and lack of driving skills are two human factors that contribute to crashes. Considering the existing evidence, risky driving behaviors and driving skills have been regarded as potential decisive factors explaining and preventing crashes. Nighttime accidents are relatively frequent and serious compared with daytime accidents. Therefore, it is important to focus on driving behaviors and skills to reduce traffic accidents and enhance safe driving in low illumination conditions. In this paper, we examined the relation between drivers’ risk perception and propensity for risky driving behavior and conducted a comparative analysis of the associations between risk perception, propensity for risky driving behavior, and other factors in the presence and absence of streetlights. Participants in Hefei city, China, were asked to complete a demographic questionnaire, the Driver Behavior Questionnaire (DBQ), and the Driver Skill Inventory (DSI). Multiple linear regression analyses identified some predictors of driver behavior. The results indicated that both the DBQ and DSI are valuable instruments in traffic safety analysis in low illumination conditions and indicated that errors, lapses, and risk perception were significantly different between with and without streetlight conditions. Pearson’s correlation test found that elderly and experienced drivers had a lower likelihood of risky driving behaviors when driving in low illumination conditions, and crash involvement was positively related to risky driving behaviors. Regarding the relationship between study variables and driving skills, the research suggested that age, driving experience, and annual distance were positively associated with driving skills, while myopia, penalty points, and driving self-assessment were negatively related to driving skills. Furthermore, the differences across age groups in errors, lapses, violations, and risk perception in the presence of streetlights were remarkable, and the driving performance of drivers aged 45–55 years was superior to that of drivers in other age groups. Finally, multiple linear regression analyses showed that education background and crash involvement had a positive influence on error, whereas risk perception had a negative effect on errors; crash involvement had a positive influence, while risk perception had a negative effect on lapse; driving experience and crash involvement had a positive influence on violation; and age had a negative influence on it.

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Ventsislavova, Petya, David Crundall, Pedro Garcia-Fernandez, and Candida Castro. "Assessing Willingness to Engage in Risky Driving Behaviour Using Naturalistic Driving Footage: The Role of Age and Gender." International Journal of Environmental Research and Public Health 18, no. 19 (September 28, 2021): 10227. http://dx.doi.org/10.3390/ijerph181910227.

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Young novice drivers are more prone than older drivers to get involved in a risky driving situation. Some young drivers underestimate risk while overestimating their driving abilities, increasing the likelihood of engaging in risky behaviour. Age and inexperience both influence risk estimation, though it is not clear which of these variables is more important. Can drivers’ willingness to engage in risky behaviour be assessed in a similar way to hazard perception skill using video-based risky situations? The aim of the current study was to assess whether a video-based tool could measure the willingness to participate in risky driving situations and whether it can distinguish between different types of risky driving scenarios across gender and driver age groups. We also explored the moderating effect of age and gender on drivers’ experience in relation to the risky manoeuvres and participants’ willingness to engage in risky situations. Participants were presented with naturalistic videos from the perspective of the driver that contained active risky situations (result of driver’s own actions) and were asked to make a decision regarding a potential action (to overtake a bus/bicycle or pass through an amber light) and whether they would accelerate at this point. Participants reported that they were more willing to accelerate and overtake cyclists and buses and less willing to pass a light in amber. Young drivers were more willing to both engage in the risky behaviours and accelerate than older drivers, with young males reporting higher scores than the other groups. Gender differences were observed, with males being more prone to overtake and pass through a light in amber than females; however, this difference was not observed for the intention to accelerate. All the above effects remained when we tested the impact of experience on decision making while controlling for age and gender, although driving experience was no longer significant. These results demonstrate that drivers’ intention to assume risk can indeed be measured in a similar video-based methodology to that used by hazard perception tests. The findings raise the possibility of assessing and training drivers on a wider range of safety-related behaviours.

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Karlsson, G. "HOSPITALIZATION AND MORTALITY SUCCEEDING DRUNK DRIVING AND RISKY DRIVING." Alcohol and Alcoholism 38, no. 3 (May 1, 2003): 281–86. http://dx.doi.org/10.1093/alcalc/agg068.

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Kochetova, T. V., and G. Meinhard. "The Method for Evaluation of Driver’s Risky Traffic Behaviour: Validation in the Russian Sample." Social Psychology and Society 11, no. 3 (2020): 196–210. http://dx.doi.org/10.17759/sps.2020110313.

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Objectives. The purpose of the article is approbation the method for evaluation of drivers’ risky traffic behaviour, its structure and main patterns. Background. Psychological research of risky traffic behaviour of drivers is especially acute. It is a very important to measure and identify the features of the traffic behaviour of drivers as high-risk behaviour. In this context, the study of risky traffic behaviour is of particular interest. Study design. The structure and patterns of risky traffic behavior of three groups of Russian drivers with different driving experience were studied. It was using a special questionnaire for evaluation of patterns of risky traffic behaviour of drivers, which has been implemented in Traffic Offenders Prevention Program in Estonia. The data analysis used comparative, correlation and exploratory factor analysis. Participants. 398 drivers with various driving experience, including: 114 — novice drivers; 80 — taxi drivers; 204 — drivers of commercial transportation. Measurements. The structure of drivers’ risky traffic behaviour was studied using a special questionnaire “Traffic Risk Evaluation Model” (Meinhard G., 2018), that includes AUDIT (Babor T., Hig¬gins-Biddle J., Saunders J., Monteiro, 2010). Results. Data on the structure of drivers risky traffic behaviour was obtained. It was found that this structure includes three main patterns: “Attitudes towards alcohol and drunk driving”, “Proneness to violations of law” and “Evaluation of risks and threats in the course of driving”. Correlations were found between patterns of risky traffic behaviour and road accident rates. Conclusions. The research has shown that the consideration of drivers’ risky traffic behaviour structure can become a promising area of research required in the psychological practice of road crash prevention.

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Kim, Jae Sung, Jong Bin Bae, Kyuhee Han, Jong Woo Hong, Ji Hyun Han, Tae Hui Kim, Kyung Phil Kwak, et al. "Driving-Related Adverse Events in the Elderly Men: A Population-Based Prospective Cohort Study." Psychiatry Investigation 17, no. 8 (August 25, 2020): 744–50. http://dx.doi.org/10.30773/pi.2019.0219.

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Objective This study estimated the incidence of driving-related adverse events and examined the association of cognitive function with the risk of future driving-related adverse events in the elderly Korean male population.Methods We analyzed 1,172 male drivers aged 60 years or older in the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD). Using the data from Korean National Police Agency, we classified the participants into three groups: safe driving (drove for 2 years after baseline without a traffic accident or repeated violations), driving cessation (stopped driving), and risky driving (one or more traffic accidents or repeated violations). We estimated the incidences of driving cessation and risky driving, and examined the effect of cognitive function on their risks.Results The incidence of driving cessation and risky driving in the Korean male drivers aged 60 years or older was 19.3 and 69.9 per 1,000 person-years respectively and increased in the late 80s. Drivers with better baseline Word List Memory Test scores showed less risky driving (OR=0.94, p=0.039).Conclusion Driving-related adverse events increased in late 80s, and better memory function was protective against these events.

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오주택 and 이상용. "Analysis of Risky Driving Pattern and Warning Effect by Risk Driving Judgment Device." Journal of Transport Research 17, no. 2 (June 2010): 139–41. http://dx.doi.org/10.34143/jtr.2010.17.2.139.

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Morgenroth, Thekla, Cordelia Fine, Michelle K. Ryan, and Anna E. Genat. "Sex, Drugs, and Reckless Driving." Social Psychological and Personality Science 9, no. 6 (September 19, 2017): 744–53. http://dx.doi.org/10.1177/1948550617722833.

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We investigated whether risk-taking measures inadvertently focus on behaviors that are more normative for men, resulting in the overestimation of gender differences. Using a popular measure of risk-taking (Domain-Specific Risk-Taking) in Study 1 ( N = 99), we found that conventionally used behaviors were more normative for men, while, overall, newly developed behaviors were not. In Studies 2 ( N = 114) and 3 ( N = 124), we demonstrate that differences in normativity are reflected in gender differences in self-reported risk-taking, which are dependent on the specific items used. Study 3 further demonstrates that conventional, masculine risk behaviors are perceived as more risky than newly generated, more feminine items, even when risks are matched. We conclude that there is confirmation bias in risk-taking measurement.

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Marks, Charles, Arash Jahangiri, and Sahar Ghanipoor Machiani. "Identifying and Labeling Potentially Risky Driving: A Multistage Process Using Real-World Driving Data." Journal of Advanced Transportation 2021 (February 13, 2021): 1–11. http://dx.doi.org/10.1155/2021/8819094.

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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.

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Puspa Nirmala, Happy Virgina, and Bhina Patria. "Peran Regulasi Diri dan Konformitas terhadap Perilaku Berkendara Berisiko pada Remaja." Gadjah Mada Journal of Psychology (GamaJoP) 2, no. 2 (February 6, 2018): 113. http://dx.doi.org/10.22146/gamajop.33095.

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There are a number factors that contribute to the traffic accident at adolescents. One of them is the implementation of risky driving behaviour. The psychological factros that can be the predictors of risky driving behaviour are self-regulation and conformity. The aim of this research is to identify the factors’ role of risky driving behaviour at young novice drivers. The study involved adolescents aged 16 to 18 who were eligible subjects of the study. This study uses an adaptation of the Driving Behaviour Quetionnaire (DBQ) and The Czech Self-Regulation Quetionnaire (SRQ-Cz). Hypotheses of this research is that self-regulation and conformity have contribution to the risky driving behaviour. Regression analysis shows that self-regulation and conformity 13.5% contribute to the risky driving behaviour simultaneously, 6.5% is from self-regulation and 7% from conformity (F= 6,29; p<0,05). Analysis also shows that self-regulation has negative correlation to the risky driving behaviour where as conformity has positive correlation to the risky driving behaviour.

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Shams, Mohsen, and Vafa Rahimi-Movaghar. "Risky Driving Behaviors in Tehran, Iran." Traffic Injury Prevention 10, no. 1 (February 27, 2009): 91–94. http://dx.doi.org/10.1080/15389580802492280.

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Yuan, Yiran, Feng Du, Weina Qu, Wenguo Zhao, and Kan Zhang. "Identifying risky drivers with simulated driving." Traffic Injury Prevention 17, no. 1 (April 2, 2015): 44–50. http://dx.doi.org/10.1080/15389588.2015.1033056.

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Bina, Manuela, Federica Graziano, and Silvia Bonino. "Risky driving and lifestyles in adolescence." Accident Analysis & Prevention 38, no. 3 (May 2006): 472–81. http://dx.doi.org/10.1016/j.aap.2005.11.003.

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Yilmaz, Veysel, and H. Eray Çelik. "A MODEL FOR RISKY DRIVING ATTITUDES IN TURKEY." Social Behavior and Personality: an international journal 32, no. 8 (January 1, 2004): 791–96. http://dx.doi.org/10.2224/sbp.2004.32.8.791.

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Different research traditions have attempted to explain individual differences in risky driving behavior and traffic accident involvement. This study was designed to improve a model for risky driving attitudes (RDA) and to understand the mechanisms underlying drivers' risk-taking behavior in traffic. The questionnaire used in this study was composed of “the driver's behavior” questionnaire, improved by Reason, Manstead, Stradling, Baxter, and Campbell, (1990), and “the scales measuring attitudes and behavior” questionnaire, improved by Ulleberg and Rundmo (2003). The questionnaire survey was carried out among 600 drivers. Analysis revealed that risky drivers' attitudes are related to factors such as obedience to speed rules, risk-taking tendency in traffic and positive attitudes towards traffic.

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Corte, Colleen M., and Marilyn S. Sommers. "Alcohol and Risky Behaviors." Annual Review of Nursing Research 23, no. 1 (January 2005): 327–60. http://dx.doi.org/10.1891/0739-6686.23.1.327.

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The purpose of this chapter is to review and critique the literature on risky drinking, driving, and sexual behaviors. To complete this review, electronic searches using databases from the disciplines of nursing, medicine, and psychology were used with keywords alcohol and risky behavior, risky drinking, risky driving, risky sex, and sexual aggression, as well as other relevant terms.The basic tenets of contemporary theoretical models of risky behaviors are used as a framework for reviewing the literature. Most relevant to the discussion are the relationships among the behaviors, risk and protective factors, and major unresolved theoretical and methodological issues. In the literature, sensation seeking was differentially associated with risky drinking, driving, and sex, but causal assertions are premature.Important conceptual and physiological issues are clarified. First, unconventionality contributes to risky drinking, risky driving, and, among adolescents, risky sex. Second, the pharmacologic effects of alcohol on cognitive processing contribute to risky sex, but only among persons who feel conflicted about risky sex (e.g., condom use). This perception may be particularly true for men who have a belief that alcohol will enhance sex. Third, sexual aggression appears to stem from a variety of factors, including the pharmacologic effects of alcohol on aggression and stereotypes about drinking women.Exploration of risk and protective factors adds breadth and depth to the discussion of risk taking. Risk factors include (1) high tolerance for deviance, (2) unconventional attitudes and behaviors such as early alcohol use and precocious sex, (3) peer norms for deviance, (4) high sensation seeking, and, to a lesser extent, (5) disturbed risk perception and positive beliefs about alcohol. Protective factors appear to mitigate risk and include (1) conventional attitudes and behaviors and (2) having peers that model conventional attitudes and behaviors. Although empirical evidence suggests that risky behaviors tend to covary, most intervention trials to date have focused on single behaviors, and often are based on clinical information rather than existing theoretical and empirical knowledge.

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Craig, Curtis M., and Samuel J. Levulis. "The relationship between global and information processing factors and self-perceived risky driving among older adults." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (September 2017): 1447–51. http://dx.doi.org/10.1177/1541931213601847.

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Drivers typically calibrate their driving behavior with their perceived risk of the current driving situation. However, the degree of risky behavior that drivers find acceptable may be affected by individual difference factors, such as gender, cognitive ability, and personality traits. Using a publicly available dataset examining cognitive and personality variables in a sample of older American adults (CogUSA; McArdle, Rodgers, & Willis, 2015), the present study assessed the relationships between global and information processing factors and self-perceived risky driving behavior (after controlling for general self-perceived risk-taking). Global factors included gender, age, and the big five personality traits. Information processing factors were measured by scores on Visual Matching, Incomplete Words, Auditory Working Memory, and Spatial Relations tests. Results indicated that gender, conscientiousness, agreeableness, and visuo-spatial processing predicted increased self-perceived risky driving behavior. The results have implications for the assessment of driving risk factors across ages, as well as the burgeoning field of hazard perception training.

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Gershon, Pnina, Johnathon P. Ehsani, Chunming Zhu, Kellienne R. Sita, Sheila Klauer, Tom Dingus, and Bruce Simons-Morton. "Crash Risk and Risky Driving Behavior Among Adolescents During Learner and Independent Driving Periods." Journal of Adolescent Health 63, no. 5 (November 2018): 568–74. http://dx.doi.org/10.1016/j.jadohealth.2018.04.012.

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Gershon, Pnina, and Bruce Simons-Morton. "Crash Risk and Risky Driving Behavior Among Adolescents During Learner and Independent Driving Periods." Journal of Adolescent Health 64, no. 5 (May 2019): 671–72. http://dx.doi.org/10.1016/j.jadohealth.2019.02.005.

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Rachmatunnisa, Firdha, and Sunu Bagaskara. "Peran Locus of Control dan Sikap Pro Risiko terhadap Perilaku Mengemudi Berisiko." Jurnal Penelitian Transportasi Darat 23, no. 2 (December 27, 2021): 135–41. http://dx.doi.org/10.25104/jptd.v23i2.1799.

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ABSTRACTThe Locus of Control’s Role and Pro Risk Attitude in Risky Driving Behavior: This study aims to determine whether the role of locus of control and pro-risk attitudes towards risky driving behavior. Using accidental sampling, this study involved 88 participants who were car drivers and motorcycle riders, and were male aged 18-25 years. The results of multiple linear regression analysis showed that the Traffic-Locus of Control scores of self dimensions and pro-risk attitudes together explain 35.2% variance of risky driving behavior. This finding indicates that road users who have a high LoC self and a positive attitude towards risky driving behavior will show a higher tendency to engage in risky driving behavior. The results of this study can be an education for road users to consider the safety of themselves and others in order to minimize the occurrence of accidents.Keywords: Locus of control; pro-risk attitudes; risky driving behavior.ABSTRAKPenelitian ini bertujuan untuk mengetahui apakah terdapat peran locus of control dan sikap pro risiko terhadap perilaku mengemudi berisiko. Menggunakan accidental sampling, penelitian ini melibatkan 88 partisipan pengemudi mobil dan pengendara sepeda motor, dan berjenis kelamin laki-laki berusia 18-25 tahun.. Hasil analisis regresi linier majemuk menunjukkan bahwa skor Traffic-Locus of Control dimensi self dan sikap pro risiko secara bersama-sama mampu menjelaskan 35,2% varians dari perilaku mengemudi berisiko. Temuan ini menandakan bahwa pengguna jalan yang memiliki LoC self tinggi dan sikap positif terhadap perilaku mengemudi berisiko akan menunjukkan kecenderungan yang lebih tinggi dalam menampilkan perilaku mengemudi berisiko. Hasil penelitian ini bisa menjadi salah satu edukasi bagi para pengguna jalan untuk lebih mempertimbangkan keselamatan diri sendiri dan orang lain agar dapat meminimalisir terjadinya kecelakaan.Kata Kunci: Locus of control; perilaku mengemudi berisiko; sikap pro risiko

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Adira, Nesya, Machmuroch Machmuroch, and Pratista Arya Satwika. "Difficulties in Emotion Regulation and Optimistic Bias in Young Drivers’ Risky Driving Behaviors." Gadjah Mada Journal of Psychology (GamaJoP) 8, no. 1 (May 23, 2022): 95. http://dx.doi.org/10.22146/gamajop.72115.

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Risky driving behavior is the most dominant human error among young novice drivers. This research's objective was to find the correlation between difficulties in emotion regulation and optimistic bias towards risky driving behavior of teenagers. Sample was Senior High School students from grade 10 to 11 S who drove private vehicles on a daily basis (N=160). Instruments used were modified Behavior of Young Novice Drivers' Scale (BYNDS), modified Difficulties in Emotion Regulation Scale (DERS) and optimistic bias scale. Hypotheses were tested using multiple regression analysis. Results showed that there was a positive and significant correlation between difficulties in emotion regulation and optimistic bias towards risky driving behavior (F (2, 157) = 47.846; p < .01). Bigger contribution was found on difficulties in emotion regulation, indicating that teenagers while driving, relied more on their emotion regulation abilities than their awareness of driving risks.

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Zakrajsek, Jennifer, Lisa Molnar, David Eby, Lidia Kostyniuk, Nicole Zanier, David J. LeBlanc, and Tina B. Sayer. "GUIDELINES FOR DEVELOPING EVIDENCE-BASED RISKY DRIVING COUNTERMEASURES THAT INCLUDE OLDER DRIVERS." Innovation in Aging 6, Supplement_1 (November 1, 2022): 164. http://dx.doi.org/10.1093/geroni/igac059.655.

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Abstract Driver behavior will continue to play a critical role in driving safety for the foreseeable future. Utilizing behavior change theory appropriately presents opportunities to improve the effectiveness of risky driving countermeasures that have been under-utilized to date. Older drivers should not be excluded from consideration of risky behaviors. Forty-six drivers (33% age 65+) completed surveys, then drove for three weeks with data collection during all trips. The Theory of Planned Behavior guided a two-phased regression analysis approach: 1) behavioral intentions were predicted using attitudes about behaviors and demographics; 2) observed risky behavior was predicted using behavioral intentions, theory constructs, personality/psychosocial characteristics, demographics, and driving exposure. Results were synthesized and the emergent themes were used to formulate guidelines for developing theory-based education and communication risky driving countermeasures. Guidelines focused on four risky driving behaviors observed in a large proportion of participants (72% - 96%): holding/using a cellphone; eating/drinking; speeding; and tailgating. Twenty-six guidelines were developed across four categories: 1) relationships among risky behaviors; 2) characteristics or underlying dimensions of risky driving (e.g., time, location, emotion); 3) behavior change theory constructs; 4) audience and message factors. While older drivers self-reported low frequencies of risky behaviors, low intentions for future risky behaviors, and less favorable attitudes toward risky behaviors than younger drivers they were regularly observed engaging in risky behaviors: distracted behaviors in 79% of trips and 2.1 speeding events per trip. Risky driving countermeasures are appropriate for older drivers and the emergent guidelines will be presented with recommended variations for older drivers.

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VLAICU, Claudia, and Felicia HAIDU. "THE PROFILE OF THE ROMANIAN AGGRESSIVE DRIVER." Pro Edu. International Journal of Educational Sciences 3, no. 5 (June 27, 2021): 81–95. http://dx.doi.org/10.26520/peijes.2021.5.3.81-95.

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Various studies have documented that aggressive driving is indeed a real problem. In each country there are various aspects of dangerous driving of empirical and practical concern and there are also individual differences to be explored. The present study aims at profiling the Romanian aggressive driver and questioning whether there are differences according to demographic variables such as: gender, age, area of living, marital status, religion, socio-economic status and level of instruction. An educational purpose may be nevertheless included. If psychologist may be provided with the profile of psychological driver and the predisposition of some to risky drivind according to age, marital status, religion, area of living and other demographic variables, they may shorten the time spent for evaluation and recommend counseling sessions for anger management for those identified with risky driving behavior. Nevetheless, other sound measures of dangerous driving are needed to understand differences and commonalties between aggression, negative cognitive/emotional driving, and risky driving. The study presents the DDDI results that might help psychologist in evaluating some variables that are part of the profile of the aggressive driver in Romania; we used it as a psychometric screening tool to select individuals who are prone to dangerous driving styles and who could benefit from sketching a cognitive-behaviour therapy (CBT)-type therapeutic intervention, at least in Romania. The educational implication of this study are that such types of interventions as cognitive-behavioral interventions (e.g., relaxation, cognitive restructuring, and behavioral skill building) may be suggested after testing the drivers in order to reduce and maintain reductions of driving anger, aggressive anger expression, aggression, risky behavior, and general anger

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Kidd, Pamela, and Sandra Huddleston. "Psychometric properties of the driving practices questionnaire: Assessment of risky driving." Research in Nursing & Health 17, no. 1 (February 1994): 51–58. http://dx.doi.org/10.1002/nur.4770170108.

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Venkatesh, Nandini, and Sumit Kumar. "Risky driving behaviour among the motorized two-wheeler novice riders in Davanagere city, Karnataka- Cross-sectional study." Indian Journal of Community Health 32, no. 1 (March 31, 2020): 133–36. http://dx.doi.org/10.47203/ijch.2020.v32i01.027.

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Introduction-Road traffic accidents (RTA) account for more significant mortality and morbidity rates worldwide, resulting in considerable global burden. In Indi, motor vehicle accidents are one of the common reasons for mortality among young riders. The present study provides insight into different aspects of risky driving behavior from individual attitudes, and psychological factors like anger, mood, and emotions. Methodology- A cross-sectional study was conducted among college students; findings from this study say young males are more involved in risky driving behavior. Results- Number of males are involved in risky driving behaviour and mobile phone usage while riding is more significant the results focus on the role that risky driving behaviour plays in two-wheeler motor vehicle accidents and suggests the need for further research in this area of risky driving behaviour to improve road safety education and law enforcement policies that focus upon ensuring good driving behaviours.

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Yang, Longhai, Xiqiao Zhang, Xiaoyan Zhu, Yule Luo, and Yi Luo. "Research on Risky Driving Behavior of Novice Drivers." Sustainability 11, no. 20 (October 9, 2019): 5556. http://dx.doi.org/10.3390/su11205556.

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Novice drivers have become the main group responsible for traffic accidents because of their lack of experience and relatively weak driving skills. Therefore, it is of great value and significance to study the related problems of the risky driving behavior of novice drivers. In this paper, we analyzed and quantified key factors leading to risky driving behavior of novice drivers on the basis of the planned behavior theory and the protection motivation theory. We integrated the theory of planned behavior (TPB) and the theory of planned behavior (PMT) to extensively discuss the formation mechanism of the dangerous driving behavior of novice drivers. The theoretical analysis showed that novice drivers engage in three main risky behaviors: easily changing their attitudes, overestimating their driving skills, and underestimating illegal driving. On the basis of the aforementioned results, we then proposed some specific suggestions such as traffic safety education and training, social supervision, and law construction for novice drivers to reduce their risky behavior.

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Murugan, N., C. Sagong, A. S. Cuamatzi Castelan, K. Moss, T. Roth, C. L. Drake, and P. Cheng. "0203 To and From the Night Shift: Risky On-the-Road Driving in Night Shift Workers." Sleep 43, Supplement_1 (April 2020): A79—A80. http://dx.doi.org/10.1093/sleep/zsaa056.201.

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Abstract Introduction Drowsy driving is a common occupational hazard for night shift workers (NSWs). While sleep loss is commonly identified as the primary culprit of drowsy driving, another critical factor to consider is circadian phase. However, the role of circadian phase in driving safety has not been well characterized in NSWs. This study examined if dim light melatonin offset (DLMOff, i.e. the cessation of melatonin secretion) is also a relevant phase marker of susceptibility to four different subtypes of risky on-the-road driving behaviors. Methods On-the-road driving was monitored over 8 weeks via a mobile application that tracked risky driving behaviors using accelerometer and GPS data from cell phones (N=15; 3052 total driving events recorded). Risky driving behaviors included: 1) frequency of hard-braking events, 2) frequency of aggressive-acceleration events, 3) duration of excessive-speeding, and 4) duration of phone-usage. At week 2, participants spent 24 hours in-lab where hourly saliva samples were collected and assayed for melatonin, and DLMOff was calculated. Phase angle of driving events relative to DLMOff was used as the predictor in nested mixed-effects regressions, with risky driving behaviors as the outcome variables. Results The most common occurrences of risky driving were phone-usage and hard-braking. On average, NSWs had 46.7% and 42.0% of driving events with at least one occurrence of phone-usage and hard-braking, respectively. Rates of aggressive-acceleration and speeding were 24.4% and 20.4%. Positive phase angles (i.e. driving after DLMOff) were associated with reduced rates of hard-braking and aggressive-acceleration, but not of phone-usage and excessive-speeding. Specifically, rates of hard-braking and aggressive-acceleration decreased by 4.5% (p<.01) and 3.4% (p=.05) every two hours following DLMOff, respectively. Conclusion The study suggests DLMOff appears to be an important variable for predicting accident risk in NSWs. If replicated, circadian phase should be considered in recommendations to increase occupational health and safety of NSWs. Support Support for this study was provided to PC by NHLBI (K23HL138166).

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Farooq, Danish. "Statistical Evaluation of Risky Driver Behavior Factors that Influence Road Safety based on Drivers Age and Driving Experience in Budapest and Islamabad." European Transport/Trasporti Europei 80, ET.2020 (December 2020): 1–18. http://dx.doi.org/10.48295/et.2020.80.2.

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Driver behavior is considered as one of the most influential factors on road safety. Most of the drivers on road involve in risky driving attitudes which cause fatal and seriously injured road accidents. This study aims to evaluate and compare the risky driver behavior factors that influence road safety based on drivers age and driving experience for Budapest and Islamabad. To achieve this, the study utilized the well-proved driver behavior questionnaire (DBQ) designed on a three-point scale to analyse statistically the driver behavior responses on perceived road safety issues. The study overall results found that drivers with age group ‘18-21 year’ and drivers with driving experience less than one year are more likely to involve in risky driver behavior factors as compared to other studied groups. Furthermore, the Budapest drivers with age group ‘18-21 year’ and driving experience less than one year are more concerned in risky driver behavior factors such as ‘disregard speed limit’, ‘failing to use personal intelligent assistant’ and ‘frequently changing lanes’. While Islamabad drivers with the same demographic characteristics are more concerned in several risky driver behavior factors as compared to other age and driving experience groups. Moreover, ANOVA analysis was run to measure the statistical significance of risky driver behavior factors between designated groups of drivers. Finally, relative risk (RR) was measured to compare that how much times one driver group is more likely to involve in risky driver behavior factors as compared to the other driver group in the sample. The study highlighted the most frequent risky driver behavior factors for each observed group to help the local policymakers to solve related road safety issues.

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Zhang, Fangda, Meng Wang, Jah’inaya Parker, and Shannon C. Roberts. "The Effect of Driving Style on Responses to Unexpected Vehicle Cyberattacks." Safety 9, no. 1 (January 31, 2023): 5. http://dx.doi.org/10.3390/safety9010005.

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Vehicle cybersecurity is a serious concern, as modern vehicles are vulnerable to cyberattacks. How drivers respond to situations induced by vehicle cyberattacks is safety critical. This paper sought to understand the effect of human drivers’ risky driving style on response behavior to unexpected vehicle cyberattacks. A driving simulator study was conducted wherein 32 participants experienced a series of simulated drives in which unexpected events caused by vehicle cyberattacks were presented. Participants’ response behavior was assessed by their change in velocity after the cybersecurity events occurred, their post-event acceleration, as well as time to first reaction. Risky driving style was portrayed by scores on the Driver Behavior Questionnaire (DBQ) and the Brief Sensation Seeking Scale (BSSS). Half of the participants also received training regarding vehicle cybersecurity before the experiment. Results suggest that when encountering certain cyberattack-induced unexpected events, whether one received training, driving scenario, participants’ gender, DBQ-Violation scores, together with their sensation seeking measured by disinhibition, had a significant impact on their response behavior. Although both the DBQ and sensation seeking have been constantly reported to be linked with risky and aberrant driving behavior, we found that drivers with higher sensation seeking tended to respond to unexpected driving situations induced by vehicle cyberattacks in a less risky and potentially safer manner. This study incorporates not only human factors into the safety research of vehicle cybersecurity, but also builds direct connections between drivers’ risky driving style, which may come from their inherent risk-taking tendency, to response behavior to vehicle cyberattacks.

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Kalyoncuoglu, S. F., and M. Tigdemir. "Modelling of personality, attitudes and risky driving." Proceedings of the Institution of Civil Engineers - Transport 161, no. 1 (February 2008): 37–43. http://dx.doi.org/10.1680/tran.2008.161.1.37.

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Orlowske, Lori L., and Paul D. Luyben. "Risky Behavior: Cell Phone Use While Driving." Journal of Prevention & Intervention in the Community 37, no. 3 (June 30, 2009): 221–29. http://dx.doi.org/10.1080/10852350902976106.

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Morisset, Nolwenn, Florence Terrade, and Alain Somat. "Perceived Self-Efficacy and Risky Driving Behaviors." Swiss Journal of Psychology 69, no. 4 (January 2010): 233–38. http://dx.doi.org/10.1024/1421-0185/a000027.

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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.

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Vinceti, M., M. Bergomi, R. Vivoli, S. Rovesti, P. Bussetti, and G. Vivoli. "Personality Traits as Predictors of Risky Driving." Epidemiology 18, Suppl (September 2007): S207—S208. http://dx.doi.org/10.1097/01.ede.0000289058.58519.a1.

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Harré, Niki, Theo Brandt, and Martin Dawe. "The Development of Risky Driving in Adolescence." Journal of Safety Research 31, no. 4 (December 2000): 185–94. http://dx.doi.org/10.1016/s0022-4375(00)00035-9.

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Jonah, Brian A., Rachel Thiessen, and Elaine Au-Yeung. "Sensation seeking, risky driving and behavioral adaptation." Accident Analysis & Prevention 33, no. 5 (September 2001): 679–84. http://dx.doi.org/10.1016/s0001-4575(00)00085-3.

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Otto, Jay, Nicholas Ward, Steven Swinford, and Jeffrey Linkenbach. "Engaging worksite bystanders to reduce risky driving." Transportation Research Part F: Traffic Psychology and Behaviour 26 (September 2014): 370–78. http://dx.doi.org/10.1016/j.trf.2014.02.006.

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BATES, BETSY. "Women Closing Gap in Risky Drinking, Driving." Clinical Psychiatry News 33, no. 10 (October 2005): 50. http://dx.doi.org/10.1016/s0270-6644(05)70891-2.

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Danno, Mikio, and Akio Wakabayashi. "Neuropsychological Approach to Identifying Risky Driving Behaviors." IEEE Intelligent Systems 25, no. 5 (September 2010): 91–96. http://dx.doi.org/10.1109/mis.2010.127.

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Journal articles on the topic 'Risky Driving'

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