Impact of texting on young drivers' behavior and safety on urban and rural roads through a simulation experiment

Individual differences, ADHD diagnosis, and driving performance: effects of traffic density and distraction type

The present study examined the impact of individual differences, attention, and memory deficits on distracted driving. Drivers with ADHD are more susceptible to distraction which results in more frequent collisions, violations, and licence suspensions. Consequently, the present investigation had 36 participants complete preliminary questionnaires, memory tasks, workload indices, and four, 4-min simulated driving scenarios to evaluate such impact. It was hypothesised ADHD diagnosis, type of cellular distraction, and traffic density would each differentially and substantively impact driving performance. Results indicated traffic density and distraction type significantly affected the objective driving facets measured, as well as subjective and secondary task performance. ADHD diagnosis directly impacted secondary task performance. Results further showed significant interactions between distraction type and traffic density on both brake pressure and steering wheel angle negatively impacting lateral and horizontal vehicle control. Altogether, these findings provide substantial empirical evidence for the deleterious effect of cellphone use on driving performance.Practitioner summary: This study examined how ADHD diagnosis, traffic density, and distraction type affect driver behaviour. Participants completed driving behaviour questionnaires, memory tasks, workload indices, and driving scenarios. Results showed that ADHD diagnosis impacted secondary task performance, while traffic and distractions significantly impacted driving performance as well secondary task performance and workload.

Driver distraction and in-vehicle interventions: A driving simulator study on visual attention and driving performance

Driving simulator studies are popular means to investigate driving behaviour in a controlled environment and test safety-critical events that would otherwise not be possible in real-world driving conditions. While several factors affect driving performance, driving distraction has been emphasised as a safety-critical issue across the globe. In this context, this study explores the impact of distraction imposed by mobile phone usage, i.e., writing and reading text messages, on driver behaviour. As part of the greater i-DREAMS project, this study uses a car driving simulator experimental design in Germany to investigate driver behaviour under various conditions: (I) monitoring scenario representing normal driving conditions, (II) intervention scenario in which drivers receive fixed timing in-vehicle intervention in case of unsafe driving manoeuvres, and (III) distraction scenario in which drivers receive in-vehicle interventions based on task completion capability, where mobile phone distraction is imposed. Besides, eye-tracking glasses are used to further explore drivers' attention allocation and eye movement behaviour. This research focuses on driver response to risky traffic events (i.e., potential pedestrian collisions, and tailgating) and the impact of distraction on driving performance, by analysing a set of eye movement and driving performance measures of 58 participants. The results reveal a significant change in drivers' gaze patterns during the distraction drives with significantly higher gaze points towards the i-DREAMS intervention display (the utilised advanced driver assistance systems in this study). The overall statistical analysis of driving performance measures suggests nearly similar impacts on driver behaviour during distraction drives; a higher deviation of lateral positioning was noted irrespective of the event risk levels and lower longitudinal acceleration rates were observed for pedestrian collisions and non-critical events during distracted driving.

Analysis of mobile phone use engagement during naturalistic driving through explainable imbalanced machine learning

While driver distraction remains an issue in modernized societies, technological advancements in data collection, storage and analysis provide the means for deeper insights of this complex phenomenon. In this research, factors influencing when driver distraction through mobile phone use occurs during naturalistic driving are investigated. Naturalistic data from a 6-stage, 230-driver experiment are exploited, in which drivers installed a non-intrusive driving recording application in their devices and conducted their trips normally across a 21-month timespan, coupled with corresponding questionnaire data. The various experiment stages involved providing progressively more behavioral feedback to drivers while continuing to record them. Subsequently, supervised Machine Learning XGBoost algorithms were employed to model the contributions of naturalistic driving and questionnaire features to the decision to engage mobile phone use. Mobile phone use percentages were heavily skewed towards zero, therefore imbalanced ML with a minority-oversampling approach in a binary format was employed. To increase the explainability offered by the algorithm, SHAP values were calculated for the informative features. Results indicate that the decision of drivers to use a mobile while driving is governed by a number of complex, non-linear relationships. Total trip distance is the most significant predictor variable by a wide margin, with mean SHAP values of 0.79 towards affecting the model decisions for the probability of mobile phone use of each driver. However, other variables influence the final predictions as well, such as the number of tickets in the last three years (m.SHAP = 0.30), declared mobile phone use (m.SHAP = 0.26), the amount and variety of provided feedback (m.SHAP = 0.17) (i.e. experiment phase number) and family member numbers (m.SHAP = 0.09) decrease the probability of using a mobile phone while driving. Conversely, increases in driver experience (m.SHAP = 0.22), driver age (m.SHAP = 0.11), engine capacity (m.SHAP = 0.11) and total kilometers driven annually (m.SHAP = 0.08) increase the probability of using a mobile phone in naturalistic driving conditions. SHAP dependency plots reveal non-linear effects present in almost all variables. Fuel consumption had a particularly strong non-linear effect, as higher values of this variable lead to both higher and lower probability of drivers using a mobile phone, deviating from the safer average. Legislation, campaigns and enforcement measures can be restructured to take advantage of gains margins in terms of understanding and predicting driver distraction behavior, as explored in the present study.

Texting While Driving: A Literature Review on Driving Simulator Studies

Road safety is increasingly threatened by distracted driving. Studies have shown that there is a significantly increased risk for a driver of being involved in a car crash due to visual distractions (not watching the road), manual distractions (hands are off the wheel for other non-driving activities), and cognitive and acoustic distractions (the driver is not focused on the driving task). Driving simulators (DSs) are powerful tools for identifying drivers' responses to different distracting factors in a safe manner. This paper aims to systematically review simulator-based studies to investigate what types of distractions are introduced when using the phone for texting while driving (TWD), what hardware and measures are used to analyze distraction, and what the impact of using mobile devices to read and write messages while driving is on driving performance. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. A total of 7151 studies were identified in the database search, of which 67 were included in the review, and they were analyzed in order to respond to four research questions. The main findings revealed that TWD distraction has negative effects on driving performance, affecting drivers' divided attention and concentration, which can lead to potentially life-threatening traffic events. We also provide several recommendations for driving simulators that can ensure high reliability and validity for experiments. This review can serve as a basis for regulators and interested parties to propose restrictions related to using mobile phones in a vehicle and improve road safety.

Investigating the impact of environmental and temporal features on mobile phone distracted driving behavior using phone use data

Mobile phone distracted driving (MPDD) is one of the most significant and common factors in distraction-affected crashes. In previous studies, MPDD has been described as a self-selected behavior that affects driving performance, rather than a multidimensionally impacted behavior. In this study, the researchers hypothesized that external environmental features significantly impacted MPDD and tested this hypothesis by structural equation modeling (SEM). Three external latent variables (road, operation, and control factors) were measured at different times during weekdays in urban areas of Texas by integrating a large number of mobile phone sensor data and roadway inventory data. A structural model was developed to test the relationship between the latent variables and the rate of drivers involved in MPDD (MPDDR) on the roadway during different time periods. Finally, the data summary and model results revealed significant temporal effects. Standardized estimates from the SEM results revealed the positive impact of roads factors in the morning peak that broader shoulders, wider medians, and smaller curve radians were correlated with higher MPDDR in the morning peak hours; the negative impact of operation factors that higher average annual daily truck traffic (truck AADT) were associated with lower MPDDR significantly. And the impact of control factors on MPDDR is positive. In other words, the road segments with a large number of traffic signals in urban areas had a higher MPDDR than those without traffic signals. These findings could assist transportation and legislation agencies in the development of appropriate countermeasures or enforcement tactics and implement them effectively to reduce the occurrence of MPDD. In addition, this study provides a novel perspective close to the actual consideration of drivers about using mobile phones while driving, in the context of MPDD research, rather than comparing driver groups and vehicle performance.

Evaluation of the optimal quantity of in-vehicle information icons using a fuzzy synthetic evaluation model in a driving simulator

In-Vehicle Information (IVI) features such as navigation assistance play an important role in the travel of drivers around the world. Frequent use of IVI, however, can easily increase the cognitive load of drivers. The interface design, especially the quantity of icons presented to the driver such as those for navigation, music, and phone calls, has not been fully researched. To determine the optimal number of icons, a systematic evaluation of the IVI Human Machine Interface (HMI) was examined using single-factor and multivariate analytical methods in a driving simulator. When one-way ANOVA was performed, the results showed that the 3-icon design scored best in subjective driver assessment, and the 4-icon design was best in the steering wheel angle. However, when a new method of analyzing the data that enabled a simultaneous accounting of changes observed in the dependent measures, 3 icons had the highest score (that is, revealed the overall best performance). This method is referred to as the fuzzy synthetic evaluation model (FSE). It represents the first use of it in an assessment of the HMI design of IVI. The findings also suggest that FSE will be applicable to various other HMI design problems.

The Analysis of Spatial Patterns and Significant Factors Associated with Young-Driver-Involved Crashes in Florida

Over the last three decades, traffic crashes have been one of the leading causes of fatalities and economic losses in the U.S.; compared with other age groups, this is especially concerning for the youth population (those aged between 16 and 24), mostly due to their inexperience, greater inattentiveness, and riskier behavior while driving. This research intends to investigate this issue around selected Florida university campuses. We employed three methods: (1) a comparative assessment for three selected counties using both planar Euclidean Distance and Roadway Network Distance-based Kernel Density Estimation methods to determine high-risk crash locations, (2) a crash density ratio difference approach to compare the maxima-normalized crash densities for the youth population and those victims that are 25 and up, and (3) a logistic regression approach to identify the statistically significant factors contributing to young-driver-involved crashes. The developed GIS maps illustrate the difference in spatial patterns of young-driver crash densities compared to those for other age groups. The statistical findings also reveal that intersections around university areas appear to be significantly problematic for youth populations, regardless of the differences in the general perspective of the characteristics of the selected counties. Moreover, the speed limit countermeasures around universities could not effectively prevent young-driver crash occurrences. Hence, the results of this study can provide valuable insights to transportation agencies in terms of pinpointing the high-risk locations around universities, assessing the effectiveness of existing safety countermeasures, and developing more reliable plans with a focus on the youth population.

Alcohol and Alcohol Combined with Texting: Evaluation of Driving Impairment Effects in a Closed-Course Section

OBJECTIVE

Examine the driving impairment effects of alcohol alone and of alcohol combined with texting.

METHODS

Fifteen drivers (nine male, six female; mean age: 31.1 ± 6.9 years, range: 23 to 43 years) with similar drinking habit (i.e., social drinkers) completed a lap in a closed-course section in six different situations: (I) sober; (II) sober and while texting; (III) 30 minutes after ingesting a moderate dose of ethanol (0.50 g/kg); (IV) 30 minutes after drinking and while texting; (V) 60 minutes after drinking, (VI) 60 minutes after drinking and while texting. Driving performance was analyzed by means of maximum and mean speed, braking time and braking distance; and ability to control the car (i.e., evaluating if the drivers hit a traffic cone or exceeded the boundaries of the course). P values of < 0.05 were considered significant.

RESULTS

Pre and post-alcohol consumption results show a significant increase concerning the drivers' mean and maximum speed after drinking (p < 3.2x10^-8). However, neither alcohol nor texting had significant effects on braking parameters (p > 0.05). Traffic cones were knocked down only in texting experiments. In addition, when using the cell phone drivers tended to reduce the speed, and to accelerate abruptly right after they finish texting.

CONCLUSION

Our findings strengthen the hypothesis that even moderate alcohol doses may significantly impair the driving performance. Additionally, alcohol and texting have complementary effects on driving impairment, and their combination represents a significant risk factor for crashes.

Risk-taking behaviors of e-scooter users: A survey in Paris

Risk-taking behavior is often held responsible for increased crash involvement. We designed and undertook a face-to-face road survey (N = 459) in order to explore incident involvement history, driving attitudes and perceived risk among e-scooter users is Paris, France. Three risk factors were specifically explored: (i) riding after having consumed alcohol, (ii) riding after having consumed drugs, and (iii) using the smartphone while riding. The relationship between these factors and user attributes (such as age and gender) and travel behavior (such as frequency of e-scooter usage and trip duration) was examined using logit and mixed logit specifications and a structural equation model. Empirical evidence suggests that it is more likely for young and male riders to develop risky behaviors. Longer trip durations seem to be associated with risk-taking behaviors.

Determining the risk of driver-at-fault events associated with common distraction types using naturalistic driving data

INTRODUCTION

Studies thus far have focused on automobile accidents that involve driver distraction. However, it is hard to discern whether distraction played a role if fault designation is missing because an accident could be caused by an unexpected external event over which the driver has no control. This study seeks to determine the effect of distraction in driver-at-fault events.

METHOD

Two generalized linear mixed models, one with at-fault safety critical events (SCE) and the other with all-cause SCEs as the outcomes, were developed to compare the odds associated with common distraction types using data from the SHRP2 naturalistic driving study.

RESULTS

Adjusting for environment and driver variation, 6 of 10 common distraction types significantly increased the risk of at-fault SCEs by 20-1330%. The three most hazardous sources of distraction were handling in-cabin objects (OR = 14.3), mobile device use (OR = 2.4), and external distraction (OR = 1.8). Mobile device use and external distraction were also among the most commonly occurring distraction types (10.1% and 11.0%, respectively).

CONCLUSIONS

Focusing on at-fault events improves our understanding of the role of distraction in potentially avoidable automobile accidents. The in-cabin distraction that requires eye-hand coordination presents the most danger to drivers' ability in maintaining fault-free, safe driving. Practical Applications: The high risk of at-fault SCEs associated with in-cabin distraction should motivate the smart design of the interior and in-vehicle information system that requires less visual attention and manual effort.

Examining driver distraction in the context of driving speed: An observational study using disruptive technology and naturalistic data

Considering the number of people who have been involved in crashes associated with driver distractions, it is important to understand the characteristics of distracted driving on public roadways. While experiments have indicated that driver distractions are associated with slower driving speeds, the methodologies tend to have limited external validity. Observational studies are often conducted under limited circumstances - be it time or location. Therefore, in order to better understand the nature of driver distractions, the authors investigated the relationships between driving speed, posted speed limits, and phone handling frequency through naturalistic driving data obtained (via disruptive technology) from 8,240 mobile application users on state-maintained highways throughout Texas. As a measure of manual distractions, a phone handling rate (PHR; times/hours driven) was calculated based on phone rotations. Within-subject comparisons were drawn for driving speed and posted speed limits under normal driving conditions and distracted conditions. The analysis revealed a strong negative correlation between PHR and driving speed (r_s = -0.87). Paired t-tests revealed significantly lower driving speeds (p = 0.000 < 0.01, d = -0.48, η = 0.69) and posted speed limits (p = 0.000 < 0.01, d = -0.20, η = 0.42) during phone handling events when compared to driving without phone handling. On average, users drove 3.26 mph slower in distracted conditions than in undistracted conditions. Driving speed had a larger effect size than posted speed limits. The findings were in line with existing theories and experiments as well as other observational studies conducted at fixed locations. Although this research did not reveal causal relations, it is noteworthy that speed reduction with manual distractions was observed under real road conditions. Spatial analyses are recommended to conduct in order to paint a more thorough picture of speed reduction, its relationship to space, and crash risks related to distracted driving.

Characterizing phone usage while driving: Safety impact from road and operational perspectives using factor analysis

Phone use while driving (PUWD) is one of the most crucial factors of distraction related traffic crashes. This study utilized an unsupervised learning method, known as factor analysis, on a unique distracted driving dataset to understand PUWD behavior from the roadway geometry and operational perspectives. The results indicate that the presence of a shoulder, median, and access control on the relatively higher functional class roadways could encourage more PUWD events. The roadways with relatively lower speed limits could have high PUWD event occurrences if the variation in operating speed is high. The results also confirm the correlations between the frequency of PUWD events and the frequency of distracted crashes. This relationship is strong on urban roadways. For rural roadways, this correlation is only strong on the roadways with a large amount of PUWD events. The findings could help transportation agencies to identify suitable countermeasures in reducing distraction related crashes. Moreover, this study provides researchers a new perspective to study PUWD behavior rather than only focus on drivers' personalities.

The Effects of Texting, Sitting Surface Stability, and Balance Training on Simulated Driving Performance and Perceived Workload in Young and Older Drivers

It has been shown that texting degrades driving performance, but the extent to which this is mediated by the driver’s age and postural stability has not been addressed. Hence, the present study examined the effects of texting, sitting surface stability, and balance training in young and older adults’ driving performance. Fifteen young (mean age = 24.3 years) and 13 older (mean age = 62.8 years) participants were tested in a driving simulator with and without texting on a smartphone and while sitting on a stable or unstable surface (i.e., a plastic wobble board), before and after a 30-min sitting balance training. Analyses of variance showed that texting deteriorated driving performance but irrespective of sitting surface stability. Balance training decreased the negative effects of texting on driving, especially in older adults. Perceived workload increased when drivers were texting, and balance training reduced perceived workload. Perceived workload was higher while sitting on the unstable surface, but less so after balance training. Path analyses showed that the effects on driving performance and perceived workload were (indirectly) associated with changes in postural stability (i.e., postural sway). The study confirms that texting threatens safe driving performance by challenging postural stability, especially in older adults. The study also suggests that it is important to further investigate the role balance training can play in reducing these negative effects of texting.

Analysis of motorcycle accidents using association rule mining-based framework with parameter optimization and GIS technology

INTRODUCTION

Analyzing key factors of motorcycle accidents is an effective method to reduce fatalities and improve road safety. Association Rule Mining (ARM) is an efficient data mining method to identify critical factors associated with injury severity. However, the existing studies have some limitations in applying ARM: (a) Most studies determined parameter thresholds of ARM subjectively, which lacks objectiveness and efficiency; (b) Most studies only listed rules with high parameter thresholds, while lacking in-depth analysis of multiple-item rules. Besides, the existing studies seldom conducted a spatial analysis of motorcycle accidents, which can provide intuitive suggestions for policymakers.

METHOD

To address these limitations, this study proposes an ARM-based framework to identify critical factors related to motorcycle injury severity. A method for parameter optimization is proposed to objectively determine parameter thresholds in ARM. A method of factor extraction is proposed to identify individual key factors from 2-item rules and boosting factors from multiple-item rules. Geographic information system (GIS) is adopted to explore the spatial relationship between key factors and motorcycle injury severity.

RESULTS AND CONCLUSIONS

The framework is applied to a case study of motorcycle accidents in Victoria, Australia. Fifteen attributes are selected after data preprocessing. 0.03 and 0.7 are determined as the best thresholds of support and confidence in ARM. Five individual key factors and four boosting factors are identified to be related to fatal injury. Spatial analysis is conducted by GIS to present hot spots of motorcycle accidents. The proposed framework has been validated to have better performance on parameter optimization and rule analysis in ARM. Practical applications: The hot spots of motorcycle accidents related to fatal factors are presented in GIS maps. Policymakers can refer to those maps straightforwardly when decision making. This framework can be applied to various kinds of traffic accidents to improve the performance of severity analysis.

A Comparative Study of Accident Risk Related to Speech-Based and Handheld Texting during a Sudden Braking Event in Urban Road Environments

The use of mobile phones while driving is a very common phenomenon that has become one of the main causes of traffic accidents. Many studies on the effects of mobile phone use on accident risk have focused on conversation and texting; however, few studies have directly compared the impacts of speech-based texting and handheld texting on accident risk, especially during sudden braking events. This study aims to statistically model and quantify the effects of potential factors on accident risk associated with a sudden braking event in terms of the driving behavior characteristics of young drivers, the behavior of the lead vehicle (LV), and mobile phone distraction tasks (i.e., both speech-based and handheld texting). For this purpose, a total of fifty-five licensed young drivers completed a driving simulator experiment in a Chinese urban road environment under five driving conditions: baseline (no phone use), simple speech-based texting, complex speech-based texting, simple handheld texting, and complex handheld texting. Generalized linear mixed models were developed for the brake reaction time and rear-end accident probability during the sudden braking events. The results showed that handheld texting tasks led to a delayed response to the sudden braking events as compared to the baseline. However, speech-based texting tasks did not slow down the response. Moreover, drivers responded faster when the initial time headway was shorter, when the initial speed was higher, or when the LV deceleration rate was greater. The rear-end accident probability respectively increased by 2.41 and 2.77 times in the presence of simple and complex handheld texting while driving. Surprisingly, the effects of speech-based texting tasks were not significant, but the accident risk increased if drivers drove the vehicle with a shorter initial time headway or a higher LV deceleration rate. In summary, these findings suggest that the effects of mobile phone distraction tasks, driving behavior characteristics, and the behavior of the LV should be taken into consideration when developing algorithms for forward collision warning systems.

Investigating in-vehicle distracting activities and crash risks for young drivers using structural equation modeling

Distracted driving has been considered one of the main reasons for traffic crashes in recent times, especially among young drivers. The objectives of this study were to identify the distracting activities in which young drivers engage, assess the most distracting ones based on their experiences, and investigate the factors that might increase crash risk. The data were collected through a self-report questionnaire. Most participants reported frequent cell phone use while driving. Other reported activities include adjusting audio devices, chatting with passengers, smoking, eating, and drinking. A structural equation model was constructed to identify the latent variables that have a significant influence on crash risk. The analysis showed that in-vehicle distractions had a high effect on the crash likelihood. The results also indicated that dangerous driving behavior had a direct effect on the crash risk probability, as well as on the rash driving latent variables. The results provide insight into distracted driving behavior among young drivers and can be useful in developing enforcement and educational strategies to reduce this type of behavior.

Analysis of Run-Off-Road Accidents by Association Rule Mining and Geographic Information System Techniques on Imbalanced Datasets

Run-off-road (ROR) accidents cause a large proportion of fatalities on roads. Exploring key factors is an effective method to reduce fatalities and improve safety sustainability. However, some limitations exist in current studies: (1) Datasets of ROR accidents have imbalance problems, in which the samples of fatal accidents (FA) are much less than non-fatal accidents (NFA). Data mining methods on such imbalanced datasets make the results biased. (2) Few studies conducted spatial analysis of ROR accidents in visualization. Therefore, this study proposes an association rule mining (ARM)-based framework to analyze ROR accidents on imbalanced datasets. A novel method is proposed to address the imbalance problem and ARM is applied to analyze accident severity. Geographic information system (GIS) is adopted for spatial analysis of ROR accidents. The proposed framework is applied to ROR accidents in Victoria, Australia. Six FA factors and seven NFA factors are identified from two-item rules. The results of three-item rules indicate factors acting interactively increase the likelihood of FA or NFA. Hot spots of ROR accidents are presented by GIS maps. Effective measures are accordingly proposed to improve road safety. Compared with traditional data-balancing methods, the proposed framework has been validated to provide more robust and reliable results on imbalanced datasets.

Using European naturalistic driving data to assess secondary task engagement when stopped at a red light

PROBLEM

Some evidence exists that drivers choose to engage in secondary tasks when the driving demand is low (e.g., when the car is stopped). While such a behavior might generally be considered as rather safe, it could be argued that the associated diversion of attention away from the road still leads to a reduction of situational awareness, which might increase collision risk once the car regains motion. This is especially relevant for texting, which is associated with considerable eyes-off-the-road-time. Nonetheless, it seems that previous research has barely addressed the actual engagement in secondary tasks while waiting at a red light (as compared to just addressing the tasks' mere prevalence).

OBJECTIVE

The present study investigated secondary task engagement while stopped at a red light using European naturalistic driving data collected through the UDRIVE project. Attention was given to the whole engagement process, including simple prevalence and the tasks' relation (in terms of start/end) to the red light period. Moreover, given that texting is one of the most problematic forms of distraction, it was characterized in more detail regarding glance behavior.

METHOD

Videos of 804 red light episodes from 159 drivers were annotated. Glance behavior was also coded for a sub-set of 75 texting events and their matched baselines. Results, conclusions and practical applications: Drivers engaged in at least one secondary task across almost half of the annotated red light episodes. Drivers who texted while stopped spent most of the time looking at their cell phone. Consequently, drivers might not have been prepared for potentially unexpected events once the light turned green. Further, drivers concluded texting a considerable number of times well after the red light period, which has potential implications for traffic safety.

An investigation on the risk factors associated with driving errors under the influence of alcohol using structural equation modeling

Objective: Driving errors contribute to traffic crashes and thus the causal factors associated with driving errors are of great interest to the road safety researchers. The present study attempts to identify and quantify the effects of various risk factors that are likely to influence driving error with the application of structural equation modeling (SEM). These risk factors include blood alcohol concentration (BAC) levels (0%, 0.03%, 0.05%, and 0.08%), different driving environments (rural, urban), and driver attributes (such as age, gender, education).Method: Eighty-two licensed drivers participated in a driving simulator experiment. They completed driving under the influence of 4 BAC levels in the driving environments within the framework of a full-factorial within-subjects design. Driving error was modeled as an unobserved latent variable based on several driving simulator indicators. An SEM approach was utilized to examine the influence of BAC level, driving environment, and driver attributes on the latent variable pertaining to driving error.Results: The findings suggest the suitability of an SEM approach in the investigation of driving error. The results revealed that all 3 positive BACs (0.03%, 0.05%, and 0.08%) had a significant positive influence on driving error compared to 0% BAC, and the tendency toward driving error increased with increasing BAC (factor loadings for 0.03%, 0.05%, and 0.08% BAC were 0.22, 0.31, and 0.37, respectively). Moreover, driving in an urban environment led to more driving errors compared to a rural environment, including sober drivers. Among the driver attributes, gender and awareness about drink and drive laws were the only factors influencing driving error.Conclusion: This study highlights a novel approach to investigate driving error by modeling it as a latent variable instead of modeling individual performance measures. The successful execution of SEM in alcohol impairment research may serve as a significant step in the human factors field moving from piecemeal analysis to a combined analysis where interrelationships among numerous risk factors and driving error can be established. The study outcomes may serve as a reference while developing strategies to enhance road traffic safety where special emphasis can be given to the critical risk factors influencing driving error identified in the study.

Mobile Phone Use in a Car-Following Situation: Impact on Time Headway and Effectiveness of Driver's Rear-End Risk Compensation Behavior via a Driving Simulator Study

Mobile phone use while driving has become one of the leading causes of traffic accidents and poses a significant threat to public health. This study investigated the impact of speech-based texting and handheld texting (two difficulty levels in each task) on car-following performance in terms of time headway and collision avoidance capability; and further examined the relationship between time headway increase strategy and the corresponding accident frequency. Fifty-three participants completed the car-following experiment in a driving simulator. A Generalized Estimating Equation method was applied to develop the linear regression model for time headway and the binary logistic regression model for accident probability. The results of the model for time headway indicated that drivers adopted compensation behavior to offset the increased workload by increasing their time headway by 0.41 and 0.59 s while conducting speech-based texting and handheld texting, respectively. The model results for the rear-end accident probability showed that the accident probability increased by 2.34 and 3.56 times, respectively, during the use of speech-based texting and handheld texting tasks. Additionally, the greater the deceleration of the lead vehicle, the higher the probability of a rear-end accident. Further, the relationship between time headway increase patterns and the corresponding accident frequencies showed that all drivers’ compensation behaviors were different, and only a few drivers increased their time headway by 60% or more, which could completely offset the increased accident risk associated with mobile phone distraction. The findings provide a theoretical reference for the formulation of traffic regulations related to mobile phone use, driver safety education programs, and road safety public awareness campaigns. Moreover, the developed accident risk models may contribute to the development of a driving safety warning system.

Prevalence of distracted walking with mobile technology: an observational study of Calgary and Edmonton high school students

OBJECTIVES

Distracted walking poses a safety concern while crossing roads. A 2014 Canadian survey showed that 51% of teenagers were hit or almost hit while crossing the street, many of whom were distracted by mobile technology. An American study demonstrated that 1 in 5 high school students engaged in distracted walking; however, prevalence estimates in Canada have not been described. This study aimed to obtain a prevalence estimate of distracted walking in high school students in Calgary and Edmonton, Alberta, Canada.

METHODS

Students were observed crossing the street at 20 high schools following afternoon school dismissal times during September to November 2017. Behaviours included talking on mobile phone, using earbuds, looking down at phone, and engaging in more than one of these behaviours. Also recorded were median household income of surrounding neighbourhood, outside temperature, gender, and crosswalk type.

RESULTS

A total of 3553 students were observed, with 666 engaged in distracted walking (18.7%). Females engaged in distracted walking more than males (19.9% vs. 17.6%, respectively). The most prevalent behaviour was using earbuds (66.5%). Males had a lower prevalence of distracted walking compared with females. Distracted walking was more prevalent when the temperature was above 10 °C compared with less than 0 °C and less prevalent at signaled compared with non-signaled intersections.

CONCLUSION

This study demonstrates that approximately 1 in 5 high school students in Calgary and Edmonton, Alberta, Canada, engage in distracted walking behaviours while crossing the street. These results may help raise awareness and inform policy to decrease risk of pedestrian injury.

Effects of texting on accident risk during a sudden hazardous event: Analysis of predetection and postdetection phases

OBJECTIVE

The present study aims to quantify the effects of texting and driver behavior on the accident risk associated with a sudden event. Further, the study attempts to compare the effects of driving behavior of inexperienced young and professional drivers on risk during predetection and postdetection phases of the event.

METHODS

Forty-nine drivers from 2 categories-inexperienced young drivers and experienced professional drivers-took part in simulated experiments. The participants drove in a free-flow road environment under 3 driving conditions: no distraction (baseline) and writing short and long texts while driving. The participants were exposed to a sudden hazardous event during each drive. Accident probability during the sudden event was modeled with a generalized linear mixed model (with a logit link function).

RESULTS

As expected, both texting tasks increased accident risk, and the risk was much higher for inexperienced young drivers than for professional drivers. Time lapsed in reducing speed increased the odds for accident risk significantly. A comparative analysis of the driver categories showed that impairment in driving behavior due to the texting tasks was similar for both groups during the predetection phase. However, the risk associated with the texting tasks was higher for young drivers during the postdetection phase. A possible reason could be that young drivers had 65% and 75% higher approach speeds (than the professional drivers) during the short and long text tasks, respectively.

CONCLUSIONS

The results provide statistical evidence that increased speed is expressed as increased risk-taking behavior among young drivers, which subsequently is the main reason for their higher accident risk during texting tasks. Moreover, the results confirm that professional drivers are not able to mitigate the increased accident risk associated with texting tasks due to late detection of the event during the tasks.

Driver distraction by smartphone use (WhatsApp) in different age groups

This paper investigates the effect that texting with WhatsApp, one of the most common applications for instant messaging, exerts on driving performance. Because distracted driving also affects older drivers, who can have seriously compromised vision, we also analysed the associations between visual-function parameters and driving performance. A total of 75 drivers, experienced in sending WhatsApp messages (≥10WhatsApp messages/week), participated in this study and were divided into four age categories. Visual-function tests included contrast sensitivity with and without glare, retinal straylight and objective assessment of optical quality. Simulated driving performance was assessed under a baseline driving condition (without distraction) as well as a texting condition (WhatsApp messages) while driving. The participants used their own mobile phone. Lastly, objective results of driving performance were compared with subjective self-report data from the Driver Behaviour Questionnaire (DBQ). The analysis indicated that functional changes occurring with age, such as a lower contrast sensitivity and greater retinal straylight, were correlated with a higher number of collisions, longer distances driven outside the lane, and greater standard deviation of lateral position (SDLP). The results showed a significant main effect of age for the driving-performance parameters. Also, compared to the baseline, texting WhatsApp messages while driving worsens driving performance for all age groups, most notably among older participants. Thus, the older drivers' SDLP was ∼14% higher than that for the baseline average of all the other drivers and rose to 29% under distraction, reflecting the impact of secondary tasks. The negative effect of the use of the smartphone during driving was also reflected in the number of collisions, with a greater risk of accidents in all the groups of drivers (by 8.3% for young adults, 25.0% for adults, 80.5% for middle-aged adults, and 134.5% for older drivers). Lastly, participants' subjective responses indicated that younger drivers (18-24 years) had a higher risk of deliberately violating safe driving practices (p < 0.05). The present study demonstrates that texting WhatsApp messages while driving significantly impairs the ability to drive safely, with older drivers being the group most adversely affected. It would be recommendable to include other nonstandard vision tests, which have shown associations with driving performance, in the examination for driver licensing. This would help raise the awareness of older drivers concerning their visual limitations, permitting them to adopt compensatory measures to improve their driving safety. Nevertheless, it is also necessary to raise awareness among the younger drivers of the risks involved in behaviour behind the wheel.

Using SHRP 2 naturalistic driving data to assess drivers' speed choice while being engaged in different secondary tasks

INTRODUCTION

The engagement in secondary tasks while driving has been found to result in considerable impairments of driving performance. Texting has especially been suspected to be associated with an increased crash risk. At the same time, there is evidence that drivers use various self-regulating strategies to compensate for the increased demands caused by secondary task engagement. One of the findings reported from multiple studies is a reduction in driving speed. However, most of these studies are of experimental nature and do not let the drivers decide for themselves to (not) engage in the secondary task, and therefore, eliminate other strategies of self-regulation (e.g., postponing the task). The goal of the present analysis was to investigate if secondary task engagement results in speed adjustment also under naturalistic conditions.

METHOD

Our analysis relied on data of the SHRP 2 naturalistic driving study. To minimize the influence of potentially confounding factors on drivers' speed choice, we focused on episodes of free flow driving on interstates/highways. Driving speed was analyzed before, during, and after texting, smoking, eating, and adjusting/monitoring radio or climate control; in a total of 403 episodes.

RESULTS

Data show some indication for speed adjustment for texting, especially when driving with high speed. However, the effect sizes were small and behavioral patterns varied considerably between drivers. The engagement in the other tasks did not influence drivers' speed behavior significantly.

CONCLUSIONS AND PRACTICAL APPLICATIONS

While drivers might indeed reduce speed slightly to accommodate for secondary task engagement, other forms of adaptation (e.g., strategic decisions) might play a more important role in a natural driving environment. The use of naturalistic driving data to study drivers' self-regulatory behavior at an operational level has proven to be promising. Still, in order to obtain a comprehensive understanding about drivers' self-regulatory behavior, a mixed-method approach is required.

Mobile phone use during driving: Effects on speed and effectiveness of driver compensatory behaviour

This study analysed and modelled the effects of conversation and texting (each with two difficulty levels) on driving performance of Indian drivers in terms of their mean speed and accident avoiding abilities; and further explored the relationship between speed reduction strategy of the drivers and their corresponding accident frequency. 100 drivers of three different age groups (young, mid-age and old-age) participated in the simulator study. Two sudden events of Indian context: unexpected crossing of pedestrians and joining of parked vehicles from road side, were simulated for estimating the accident probabilities. Generalized linear mixed models approach was used for developing linear regression models for mean speed and binary logistic regression models for accident probability. The results of the models showed that the drivers significantly compensated the increased workload by reducing their mean speed by 2.62m/s and 5.29m/s in the presence of conversation and texting tasks respectively. The logistic models for accident probabilities showed that the accident probabilities increased by 3 and 4 times respectively when the drivers were conversing or texting on a phone during driving. Further, the relationship between the speed reduction patterns and their corresponding accident frequencies showed that all the drivers compensated differently; but, among all the drivers, only few drivers, who compensated by reducing the speed by 30% or more, were able to fully offset the increased accident risk associated with the phone use.

Investigation of the disposal of dead pigs by pig farmers in mainland China by simulation experiment

Dead pigs are a major waste by-product of pig farming. Thus, safe disposal of dead pigs is important to the protection of consumer health and the ecological environment by preventing marketing of slaughtered and processed dead pigs and improper dumping of dead pigs. In this study, a probability model was constructed for the disposal of dead pigs by pig farmers by selecting factors affecting disposal. To that end, we drew on the definition and meaning of behavior probability based on survey data collected from 654 pig farmers in Funing County, Jiangsu Province, China. Moreover, the role of influencing factors in pig farmers' behavioral choices regarding the disposal of dead pigs was simulated by simulation experiment. The results indicated that years of farming had a positive impact on pig farmers' choice of negative disposal of dead pigs. Moreover, there was not a simple linear relationship between scale of farming and pig farmers' behavioral choices related to the disposal of dead pigs. The probability for farmers to choose the safe disposal of dead pigs increased with the improvement of their knowledge of government policies and relevant laws and regulations. Pig farmers' behavioral choice about the disposal of dead pigs was also affected by government subsidy policies, regulation, and punishment. Government regulation and punishment were more effective than subsidy. The findings of our simulation experiment provide important decision-making support for the governance in preventing the marketing of dead pigs at the source.

Texting while driving: the development and validation of the distracted driving survey and risk score among young adults

BACKGROUND

Texting while driving and other cell-phone reading and writing activities are high-risk activities associated with motor vehicle collisions and mortality. This paper describes the development and preliminary evaluation of the Distracted Driving Survey (DDS) and score.

METHODS

Survey questions were developed by a research team using semi-structured interviews, pilot-tested, and evaluated in young drivers for validity and reliability. Questions focused on texting while driving and use of email, social media, and maps on cellular phones with specific questions about the driving speeds at which these activities are performed.

RESULTS

In 228 drivers 18-24 years old, the DDS showed excellent internal consistency (Cronbach's alpha = 0.93) and correlations with reported 12-month crash rates. The score is reported on a 0-44 scale with 44 being highest risk behaviors. For every 1 unit increase of the DDS score, the odds of reporting a car crash increases 7 %. The survey can be completed in two minutes, or less than five minutes if demographic and background information is included. Text messaging was common; 59.2 and 71.5 % of respondents said they wrote and read text messages, respectively, while driving in the last 30 days.

CONCLUSION

The DDS is an 11-item scale that measures cell phone-related distracted driving risk and includes reading/viewing and writing subscores. The scale demonstrated strong validity and reliability in drivers age 24 and younger. The DDS may be useful for measuring rates of cell-phone related distracted driving and for evaluating public health interventions focused on reducing such behaviors.

Does family background impact driving attitudes and risky behaviours? An investigation on Chinese young drivers

The rapid pace of motorisation in China has been well documented, as has the large road trauma burden the Chinese citizens are facing as a result. China's unique political system represents an important consideration in helping reduce road trauma, yet political factors have not been previously investigated in this context. Recently, emerging issues on the road involving the adult children of politically powerful families have become a serious social problem in China, and have drawn widespread media and public attention. This study took a novel approach to examining factors associated with risky attitudes and risky road use in China by investigating the economic and political background status of a sample of young Chinese drivers. An online survey was conducted in May 2015 with a sample size of 476 Chinese young drivers from across the country, aged between 18 and 28, including 305 males and 171 females. The results suggest that for participants who reported having a familial political background, more risky driving behaviours were reported among those participants who reported more impact on their life from that political background; while for participants without political background, higher personal income was associated with more risky driving behaviours. The findings are discussed in light of China's political management system and potential education opportunities for young drivers.

Texting while driving: A study of 1211 U.S. adults with the Distracted Driving Survey

Texting and other cell-phone related distracted driving is estimated to account for thousands of motor vehicle collisions each year but studies examining the specific cell phone reading and writing activities of drivers are limited. The objective of this study was to describe the frequency of cell-phone related distracted driving behaviors. A national, representative, anonymous panel of 1211 United States drivers was recruited in 2015 to complete the Distracted Driving Survey (DDS), an 11-item validated questionnaire examining cell phone reading and writing activities and at what speeds they occur. Higher DDS scores reflect more distraction. DDS scores were analyzed by demographic data and self-reported crash rate. Nearly 60% of respondents reported a cell phone reading or writing activity within the prior 30 days, with reading texts (48%), writing texts (33%) and viewing maps (43%) most frequently reported. Only 4.9% of respondents had enrolled in a program aimed at reducing cell phone related distracted driving. DDS scores were significantly correlated to crash rate (p < 0.0001), with every one point increase associated with an additional 7% risk of a crash (p < 0.0001). DDS scores were inversely correlated to age (p < 0.0001). The DDS demonstrated high internal consistency (Cronbach's alpha = 0.94). High rates of cell phone-related distraction are reported here in a national sample. Distraction is associated with crash rates and occurs across all age groups, but is highest in younger drivers. The DDS can be used to evaluate the impact of public health programs aimed at reducing cell-phone related distracted driving.

Interruptions and distractions in the gynaecological operating theatre: irritating or dangerous?

Distractions and interference can include visual (e.g. staff obscuring monitors), audio (e.g. noise, irrelevant communication) and equipment problems. Level of distraction is usually defined as I: relatively inconsequential; II: > one member of the surgical team affected; III: the entire surgical team affected. The aim of this study was to observe the frequency and impact of distracting events and interruptions on elective gynaecology cases. Data from 35 cases were collected from 10 consecutive operating sessions. Mean number of interruptions was 26 episodes/case, while mean number of level II/III distractions was 17 episodes/case. Ninety per cent of interruptions occur in the first 30 minutes of the procedure and 80.9% lead to level II/III distraction. Although no complications were directly attributable to the observed distractions, the mean prolongation of operating time was 18.46 minutes/case. Understanding their effects on theatre environment enables appropriate measures to be taken so that theatre productivity and patient safety are optimised.

PRACTITIONER SUMMARY

This observational study of 35 elective cases shows a mean interruption rate of 26 episodes/case with 80.9% affecting > one member of operating team, leading to mean prolongation of 18.46 minutes/case. Theatre staff should be aware of these findings and appropriate measures taken to optimise theatre productivity and patient safety.