Using Real-Time and Post Hoc Feedback to Improve Driving Safety for Novice Drivers

2016 ◽  
Vol 60 (1) ◽  
pp. 1936-1940 ◽  
Author(s):  
Sheila G. Klauer ◽  
Tina B. Sayer ◽  
Peter Baynes ◽  
Gayatri Ankem
Keyword(s):  
Real Time ◽  
Motor Vehicle ◽  
Driving Behavior ◽  
Driving Safety ◽  
Risky Driving ◽  
Novice Drivers ◽  
Safe Driving ◽  
Driving Behaviors ◽  
Post Hoc ◽  
Risky Driving Behaviors

Introduction. Motor vehicle crashes remain the leading cause of fatalities among teens in the U.S. (National Center for Injury Prevention and Control, 2013). Prior research suggests that real-time and post hoc feedback can improve teen driver behavior. The Driver Coach Study (DCS) aimed to improve teens’ safe driving habits by providing them real-time feedback and post hoc feedback to a broader range of risky driving behaviors that have never been used in previous studies. Exposure data were also collected so that rates of risky driving behaviors over time could be assessed. Post hoc feedback, which included an electronic report card of risky driving behavior as well as video clips, was provided to both teens and parents via email and secure website link. Method. Ninety-two teen/parent dyads were recruited in southwest Virginia to have a data acquisition system (DAS) installed in their vehicles within two weeks of receiving their learner’s permit. Data were collected through the nine-month (minimum) learner’s permit phase plus seven months of provisional licensure. Feedback was only provided for the first six months of post licensure, then turned off to assess whether teenagers returned to unsafe driving behavior. Trained data coders reviewed 15 seconds of video surrounding each risky driving maneuver, and recorded driver errors such as poor vehicle control, poor speed selection, drowsiness, etc., for each event. Results. In this paper, the relationship between driver coaching and driver errors will be examined across the six-month feedback phase and also compared to the seventh month when feedback was turned off. Conclusions. This study has implications for the design of future monitoring and feedback systems, as it is currently unknown whether these devices can improve novice drivers’ crash rates.

scholarly journals Examining Risky Driving Behavior Among Older Drivers Using Self-Reported and Objective Data

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 465-465
Author(s):  
Jennifer Zakrajsek ◽  
Lisa Molnar ◽  
David Eby ◽  
David LeBlanc ◽  
Lidia Kostyniuk ◽  
...  
Keyword(s):  
Motor Vehicle ◽  
Seat Belt ◽  
Driving Behavior ◽  
Video Data ◽  
Older Drivers ◽  
Driving Frequency ◽  
Middle Aged ◽  
Risky Driving ◽  
Driving Behaviors ◽  
Study Results

Abstract Motor vehicle crashes represent a significant public health problem. Efforts to improve driving safety are multifaceted, focusing on vehicles, roadways, and drivers with risky driving behaviors playing integral roles in each area. As part of a study to create guidelines for developing risky driving countermeasures, 480 drivers (118 young/18-25, 183 middle-aged/35-55, 179 older/65 and older) completed online surveys measuring driving history, risky driving (frequency of engaging in distracted [using cell phone, texting, eating/drinking, grooming, reaching/interacting] and reckless/aggressive [speeding, tailgating, failing to yield right-of-way, maneuvering unsafely, rolling stops] driving behaviors), and psychosocial characteristics. A cluster analysis using frequency of the risky behaviors and seat belt use identified five risky behavior-clusters: 1) rarely/never distracted-rarely/never reckless/aggressive (n=392); 2) sometimes distracted-rarely/never reckless/aggressive (n=33); 3) sometimes distracted-sometimes reckless/aggressive (n=40); 4) often/always distracted-often/always reckless/aggressive (n=11); 5) no pattern (n=4). Older drivers were more likely in the first/lowest cluster (93.8% of older versus 84.2% of middle-aged and 59.3% of young drivers; p<.0001). Fifteen older drivers participated in a follow-up study in which their vehicles were equipped with a data acquisition system that collected objective driving and video data of all trips for three weeks. Analysis of video data from 145 older driver trips indicated that older drivers engaged in at least one distracted behavior in 115 (79.3%) trips. While preliminary, this suggests considerably more frequent engagement in distracted driving than self-reported and that older drivers should not be excluded from consideration when developing risky driving behavior countermeasures. Full study results and implications will be presented.

scholarly journals Driving Behaviors in Iran: Comparison of Impulsivity, Attentional Bias, and Decision-Making Styles in Safe and High-Risk Drivers

2020 ◽  
Author(s):  
Fatemeh Barati ◽  
Abas Pourshahbaz ◽  
Masode Nosratabadi ◽  
Yasaman Shiasy
Keyword(s):  
Decision Making ◽  
Attentional Bias ◽  
Traffic Accidents ◽  
Function Analysis ◽  
Economic Losses ◽  
Risky Driving ◽  
Safe Driving ◽  
Driving Behaviors ◽  
Decision Making Styles ◽  
Risky Driving Behaviors

Objective: Road traffic injuries are leading cause of death and economic losses, particularly in developing countries such as Iran. Thus, increased understanding of the causes of traffic accidents can help solve this problem. The primary goal of this study was to examine attentional bias, decision-making styles, and impulsiveness in drivers with safe or risky driving behaviors. The secondary purpose was to determine the variance of each variable among 2 groups of drivers. Method: This was a cross sectional design study, in which 120 male drivers aged 20-30 years (60 males with risky driving behaviors and 60 with safe driving behaviors) were recruited from Tehran using sampling technique. Barratt Impulsiveness Scale (BIS), Decision-Making Style Scale (DMSQ), Manchester Driver Behavior Questionnaire (MDBQ), Self-Assessment Manikin Scale (SAM), and Dot Probe Task were used. The analyses were performed using IBM SPSS version 22. Results: The mean age of participants was 26 years. Significant differences were found between impulsiveness (attentional, motor, and non planning impulsiveness) and decision-making styles (spontaneous and avoidant) between the 2 groups. Also, based on the results of discriminant function analysis (DFS), the subscales of impulsiveness and 2 decision-making styles explained 25% of the variance in the 2 groups of risky and safe drivers. Conclusion: Findings of this study indicated that impulsiveness and 2 decision-making styles were predominant factors. Therefore, not only is there a need for research to reduce traffic accidents, but studies can also be helpful in issuing driving licenses to individuals.

scholarly journals Understanding Traffic Accidents among Young Drivers in Qatar

2022 ◽  
Vol 19 (1) ◽  
pp. 514
Author(s):  
Faris Tarlochan ◽  
Mohamed Izham Mohamed Ibrahim ◽  
Batool Gaben
Keyword(s):  
Traffic Accidents ◽  
Corrective Feedback ◽  
Risky Behaviors ◽  
Driving Behavior ◽  
Self Report ◽  
Measurement Scales ◽  
Risky Driving ◽  
Driving Behaviors ◽  
Young Drivers ◽  
Risky Driving Behaviors

Young drivers are generally associated with risky driving behaviors that can lead to crash involvement. Many self-report measurement scales are used to assess such risky behaviors. This study is aimed to understand the risky driving behaviors of young adults in Qatar and how such behaviors are associated with crash involvement. This was achieved through the usage of validated self-report measurement scales adopted for the Arabic context. A nationwide cross-sectional and exploratory study was conducted in Qatar from January to April 2021. Due to the Covid-19 pandemic, the survey was conducted online. Therefore, respondents were selected conveniently. Hence, the study adopted a non-probability sampling method in which convenience and snowball sampling were used. A total of 253 completed questionnaires were received, of which 57.3% were female, and 42.7% were male. Approximately 55.8% of these young drivers were involved in traffic accidents after obtaining their driving license. On average, most young drivers do have some risky driving behavior accompanied by a low tendency to violate traffic laws, and their driving style is not significantly controlled by their personality on the road. The older young drivers are more involved in traffic accidents than the younger drivers, i.e., around 1.5 times more likely. Moreover, a young male driver is 3.2 times less likely to be involved in traffic accidents than a female driver. In addition, males are only 0.309 times as likely as females to be involved in an accident and have approximately a 70% lower likelihood of having an accident versus females. The analysis is complemented with the association between young drivers’ demographic background and psychosocial-behavioral parameters (linking risky driving behavior, personality, and obligation effects on crash involvement). Some interventions are required to improve driving behavior, such as driving apps that are able to monitor and provide corrective feedback.

scholarly journals Implementing Surrogate Safety Measures in Driving Simulator and Evaluating the Safety Effects of Simulator-Based Training on Risky Driving Behaviors

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Eunhan Ka ◽  
Do-Gyeong Kim ◽  
Jooneui Hong ◽  
Chungwon Lee
Keyword(s):  
Driving Simulator ◽  
Driver Education ◽  
Risky Driving ◽  
Safety Measures ◽  
Novice Drivers ◽  
Elderly Drivers ◽  
Driving Behaviors ◽  
Commercial Drivers ◽  
Surrogate Safety Measures ◽  
Risky Driving Behaviors

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.

scholarly journals 0203 To and From the Night Shift: Risky On-the-Road Driving in Night Shift Workers

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A79-A80
Author(s):  
N Murugan ◽  
C Sagong ◽  
A S Cuamatzi Castelan ◽  
K Moss ◽  
T Roth ◽  
...  
Keyword(s):  
Night Shift ◽  
Driving Safety ◽  
Drowsy Driving ◽  
Risky Driving ◽  
Shift Workers ◽  
The Road ◽  
Circadian Phase ◽  
Driving Behaviors ◽  
On The Road ◽  
Risky Driving Behaviors

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).

scholarly journals The Role of Impulsivity, Attentional bias and Decision-Making Styles in Risky Driving Behaviors

2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Fatemeh Barati ◽  
Abbas Pourshahbaz ◽  
Masoud Nosratabadi ◽  
Zahra Mohammadi
Keyword(s):  
Decision Making ◽  
Attentional Bias ◽  
Pearson Correlation ◽  
Driving Behavior ◽  
Pearson Correlation Coefficient ◽  
Road Accidents ◽  
Risky Driving ◽  
Driving Behaviors ◽  
Decision Making Styles ◽  
Risky Driving Behaviors

Background: Road accidents are a major cause of deaths, injuries, and financial losses globally, especially in developing countries. Iran is one of the countries with a high rate of road accidents causing considerable damage in different domains. Therefore, in order to tackle this problem, we need to examine its causes. Objectives: The aim of the present study was to examine the association of risky driving behavior with impulsiveness, attentional bias, and decision-making styles. Patients and Methods: This was a descriptive-correlational study. The sample included 117 male drivers, aged 20 - 34 years, attending car insurance agencies in Tehran. The participants were selected using the convenience sampling method. The data were gathered using the Manchester Driver Behavior Questionnaire (DBQ), the Barratt Impulsiveness Scale (BIS), the Decision-Making Style Scale (DMS), and the Dot Probe Task to assess attentional bias. All data analysis was conducted using Pearson correlation coefficient and multiple regression analysis, by using SPSS, version 22. Results: According to the results of the Pearson correlation coefficient, risky driving behavior was significantly correlated with impulsiveness subscales (P < 0.01) and attentional bias (P < 0.05). In addition, significant relationships were observed between risky driving behaviors and three decision-making styles, including rational (P < 0.05), spontaneous (P < 0.01), and avoidant (P < 0.01). Conclusions: Based on the study results, impulsivity, decision-making styles, and attentional bias as factors influencing drivers’ cognitive skills related to driving, could explain the increase in the frequency of risky driving behavior.

scholarly journals Drivers’ Risk Perception and Risky Driving Behavior under Low Illumination Conditions: Modified Driver Behavior Questionnaire (DBQ) and Driver Skill Inventory (DSI)

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jing Liu ◽  
Cheng Wang ◽  
Zhipeng Liu ◽  
Zhongxiang Feng ◽  
N. N. Sze
Keyword(s):  
Risk Perception ◽  
Driver Behavior ◽  
Positive Influence ◽  
Driving Behavior ◽  
Risky Driving ◽  
Driving Experience ◽  
Driving Behaviors ◽  
Driving Skills ◽  
Low Illumination ◽  
Risky Driving Behaviors

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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