Structural equation model analysis for the evaluation of overall driving performance: A driving simulator study focusing on driver distraction

Objective: The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures.

Methods: For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures.

Results: Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance.

Conclusions: The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance.     

融合分心维度的驾驶行为可靠性研究

分心驾驶容易影响驾驶行为,进而导致交通事故的问题。用理论建模方法,研究分心对驾驶行为及其可靠性的影响。介绍分心驾驶的定义和维度。基于驾驶行为理论,建立融合分心维度的驾驶行为模型,分析不同分心维度对驾驶行为的影响机理。基于可靠性理论,建立融合分心维度的驾驶行为可靠性模型,分析不同分心维度对驾驶行为可靠性的影响。结果表明:不同维度分心对驾驶行为的影响具有各自特点和交互性;减少影响系数高的分心维度,有助于提高驾驶行为可靠性;多维度分心比单维度分心会更大程度地降低驾驶行为可靠度,应减少或避免复合分心。     

Identification of aggressive driving from naturalistic data in car-following situations

Introduction: Aggressive driving has been associated as one of the causes for crashes, sometimes with very serious consequences. The objective of this study is to investigate the possibility of identifying aggressive driving in car-following situations on motorways by simple jerk metrics derived from naturalistic data. Method: We investigate two jerk metrics, one for large positive jerk and the other for large negative jerk, when drivers are operating the gas and brake pedal, respectively. Results: The results obtained from naturalistic data from five countries in Europe show that the drivers from different countries have a significantly different number of large positive and large negative jerks. Male drivers operate the vehicle with significantly larger number of negative jerks compared to female drivers. The validation of the jerk metrics in identifying aggressive driving is performed by tailgating (following a leading vehicle in a close proximity) and by a violator/non-violator categorization derived from self-reported questionnaires. Our study shows that the identification of aggressive driving could be reinforced by the number of large negative jerks, given that the drivers are tailgating, or by the number of large positive jerks, given that the drivers are categorized as violators. Practical applications: The possibility of understanding, classifying, and quantifying aggressive driving behavior and driving styles with higher risk for accidents can be used for the development of driver support and coaching programs that promote driver safety and are enabled by the vast collection of driving data from modern in-vehicle monitoring and smartphone technology.     

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.     

The use and safety of medications known to affect driving in Jordan: A cross-sectional study

Objectives: The aim of this study was to estimate the main driving-impairing medications used by drivers in Jordan, the reported frequency of medication side effects, the frequency of motor vehicle crashes (MVCs) while using driving-impairing medicines, as well as factors associated with MVCs.

Methods: A cross-sectional study involving 1,049 individuals (age 18–75 years) who are actively driving vehicles and taking at least one medication known to affect driving (anxiolytics, antidepressants, hypnotics, antiepileptics, opioids, sedating antihistamines, hypoglycemic agents, antihypertensives, central nervous system [CNS] stimulants, and herbals with CNS-related effects) was conducted in Amman, Jordan, over a period of 8 months (September 2013–May 2014) using a structured validated questionnaire.

Results: Sixty-three percent of participants noticed a link between a medicine taken and feeling sleepy and 57% stated that they experience at least one adverse effect other than sleepiness from their medication. About 22% of the participants reported having a MVC while on medication. Multiple logistic regression analysis showed that among the participants who reported having a crash while taking a driving-impairing medication, the odds ratios were significantly higher for the use of inhalant substance (odds ratio [OR] = 2.787, P = .014), having chronic conditions (OR = 1.869, P = .001), and use of antiepileptic medications (OR = 2.348, P = .008) and significantly lower for the use of antihypertensives (OR = 0.533, P = .008).

Conclusion: The study results show high prevalence of adverse effects of medications with potential for driving impairment, including involvement in MVCs. Our findings highlight the types of patient-related and medication-related factors associated with MVCs in Jordan, such as inhalant use, presence of chronic conditions, and use of antiepileptics.     

Overestimation of self-reported driving exposure: Results from the SHRP2 Naturalistic Driving Study

Objectives: The accuracy of self-reported driving exposure has questioned the validity of using self-reported mileage to inform research questions. Studies examining the accuracy of self-reported driving exposure compared to objective measures find low validity, with drivers overestimating and underestimating driving distance. The aims of the current study were to (1) examine the discrepancy between self-reported annual mileage and driving exposure the following year and (2) investigate whether these differences depended on age and annual mileage.

Methods: Two estimates of drivers’ self-reported annual mileage collected during vehicle installation (obtained via prestudy questionnaires) and approximated annual mileage driven (based upon Global Positioning System data) were acquired from 3,323 participants who participated in the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study.

Results: A Wilcoxon signed rank test showed that there was a significant difference between self-reported and annual driving exposure during participation in SHRP 2, with the majority of self-reported responses overestimating annual mileage the following year, irrespective of whether an ordinal or ratio variable was examined. Over 15% of participants provided self-reported responses with over 100% deviation, which were exclusive to participants underestimating annual mileage. Further, deviations in reporting differed between participants who had low, medium, and high exposure, as well as between participants in different age groups.

Conclusions: These findings indicate that although self-reported annual mileage is heavily relied on for research, such estimates of driving distance may be an overestimate of current or future mileage and can influence the validity of prior research that has utilized estimates of driving exposure.     

Self-regulation of driving speed among distracted drivers: An application of driver behavioral adaptation theory

Objective: The adaptive behavior of mobile phone–distracted drivers has been a topic of much discussion in the recent literature. Both simulator and naturalistic studies suggest that distracted drivers generally select lower driving speeds; however, speed adaptation is not observed among all drivers, and the mechanisms of speed selection are not well understood. The aim of this research was to apply a driver behavioral adaptation model to investigate the speed adaptation of mobile phone–distracted drivers.

Methods: The speed selection behavior of drivers was observed in 3 phone conditions including baseline (no conversation) and hands-free and handheld phone conversations in a high-fidelity driving simulator. Speed adaptation in each phone condition was modeled as a function of secondary task demand and self-reported personal/psychological characteristics with a system of seemingly unrelated equations (SURE) accounting for potential correlations due to repeated measures experiment design.

Results: Speed adaptation is similar between hands-free and handheld phone conditions, but the predictors of speed adaptation vary across the phone conditions. Though perceived workload of secondary task demand, self-efficacy, attitude toward safety, and driver demographics were significant predictors of speed adaptation in the handheld condition, drivers' familiarity with the hands-free interface, attitude toward safety, and sensation seeking were significant predictors in the hands-free condition. Drivers who reported more positive safety attitudes selected lower driving speeds while using phones.

Conclusion: This research confirmed that behavioral adaptation models are suitable for explaining speed adaptation of mobile phone distracted drivers, and future research could be focused on further theoretical refinement.     

未按规定导向车道行驶行为干预方法研究

为保障信号交叉口的正常交通秩序,充分遏制机动车未按规定导向车道行驶行为,亟需探究该行为的影响因素及干预方法。以北京市内4个信号交叉口处共35 h的1 666条监控视频数据为基础,对未按规定导向车道行驶行为进行定义并将其分为9类,分别对频率较高的5类未按规定导向车道行驶行为构建二元Logit模型,以确定其关键影响因素,并据此提出干预方法。结果表明,排队车辆数、大车比例、时段、车流量、照明条件等因素会不同程度地影响5类未按规定导向车道行驶行为的发生概率,其中排队车辆数及时间因素影响最为显著。在此基础上,从交通工程设施及驾驶人安全意识角度,提出优化交叉口渠化设计及信号配时、采用智能标线、强化监管力度及完善交通管控设施、加强驾驶人安全教育4种未按规定导向车道行驶行为干预方法。     

Covariability in three dimensions of teenage driving risk behavior: impaired driving,risky and unsafe driving behavior,and secondary task engagement

Objective: This research examined the extent to which teenagers who engaged in one form of risky driving also engaged in other forms and whether risky driving measures were reciprocally associated over time.

Methods: The data were from waves 1, 2, and 3 (W1, W2, and W3) of the NEXT Generation study, with longitudinal assessment of a nationally representative sample starting with 10th graders starting in 2009–2010. Three measures of risky driving were assessed in autoregressive and cross-lagged analyses: driving while alcohol/drug impaired (DWI), Checkpoints Risky Driving Scale (risky and unsafe driving), and secondary task engagement while driving.

Results: In adjusted autoregression models, the risk variables demonstrated high levels of stability, with significant associations observed across the 3 waves. However, associations between variables were inconsistent. DWI at W2 was associated with risky and unsafe driving at W3 (β = 0.21, P < .01); risky and unsafe driving at W1 was associated with DWI at W2 (β = 0.20, P < .01); and risky and unsafe driving at W2 is associated with secondary task engagement at W3 (β = 0.19, P < .01). Over time, associations between DWI and secondary task engagement were not significant.

Conclusions: Our findings provide modest evidence for the covariability of risky driving, with prospective associations between the Risky Driving Scale and the other measures and reciprocal associations between all 3 variables at some time points. Secondary task engagement, however, appears largely to be an independent measure of risky driving. The findings suggest the importance of implementing interventions that addresses each of these driving risks.     

Near crash characteristics among risky drivers using the SHRP2 naturalistic driving study

Problem: Previous research have focused extensively on crashes, however near crashes provide additional data on driver errors leading to critical events as well as evasive maneuvers employed to avoid crashes. The Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study contains extensive data on real world driving and offers a reliable methodology to study near crashes. The current study utilized the SHRP2 database to compare the rate and characteristics associated with near crashes among risky drivers. Methods: A subset from the SHRP2 database consisting of 4,818 near crashes for teen (16–19 yrs), young adult (20–24 yrs), adult (35–54 yrs), and older (70+ yrs) drivers was used. Near crashes were classified into seven incident types: rear-end, road departure, intersection, head-on, side-swipe, pedestrian/cyclist, and animal. Near crash rates, incident type, secondary tasks, and evasive maneuvers were compared across age groups. For rear-end near crashes, near crash severity, max deceleration, and time-to-collision at braking were compared across age. Results: Near crash rates significantly decreased with increasing age (p < 0.05). Young drivers exhibited greater rear-end (p < 0.05) and road departure (p < 0.05) near crashes compared to adult and older drivers. Intersection near crashes were the most common incident type among older drivers. Evasive maneuver type did not significantly vary across age groups. Near crashes exhibited a longer time-to-collision at braking (p < 0.01) compared to crashes. Summary: These data demonstrate increased total near crash rates among young drivers relative to adult and older drivers. Prevalence of specific near crash types also differed across age groups. Timely execution of evasive maneuvers was a distinguishing factor between crashes or near crashes. Practical Applications: These data can be used to develop more targeted driver training programs and help OEMs optimize ADAS to address the most common errors exhibited by risky drivers.     

A spectral power analysis of driving behavior changes during the transition from nondistraction to distraction

Objective: This article investigated and compared frequency domain and time domain characteristics of drivers' behaviors before and after the start of distracted driving.

Method: Data from an existing naturalistic driving study were used. Fast Fourier transform (FFT) was applied for the frequency domain analysis to explore drivers' behavior pattern changes between nondistracted (prestarting of visual–manual task) and distracted (poststarting of visual–manual task) driving periods. Average relative spectral power in a low frequency range (0–0.5 Hz) and the standard deviation in a 10-s time window of vehicle control variables (i.e., lane offset, yaw rate, and acceleration) were calculated and further compared. Sensitivity analyses were also applied to examine the reliability of the time and frequency domain analyses.

Results: Results of the mixed model analyses from the time and frequency domain analyses all showed significant degradation in lateral control performance after engaging in visual–manual tasks while driving. Results of the sensitivity analyses suggested that the frequency domain analysis was less sensitive to the frequency bandwidth, whereas the time domain analysis was more sensitive to the time intervals selected for variation calculations. Different time interval selections can result in significantly different standard deviation values, whereas average spectral power analysis on yaw rate in both low and high frequency bandwidths showed consistent results, that higher variation values were observed during distracted driving when compared to nondistracted driving.

Conclusions: This study suggests that driver state detection needs to consider the behavior changes during the prestarting periods, instead of only focusing on periods with physical presence of distraction, such as cell phone use. Lateral control measures can be a better indicator of distraction detection than longitudinal controls. In addition, frequency domain analyses proved to be a more robust and consistent method in assessing driving performance compared to time domain analyses.     

A framework for definition of logical scenarios for safety assurance of automated driving

Abstract

Objective: In order to introduce automated vehicles on public roads, it is necessary to ensure that these vehicles are safe to operate in traffic. One challenge is to prove that all physically possible variations of situations can be handled safely within the operational design domain of the vehicle. A promising approach to handling the set of possible situations is to identify a manageable number of logical scenarios, which provide an abstraction for object properties and behavior within the situations. These can then be transferred into concrete scenarios defining all parameters necessary to reproduce the situation in different test environments.

Methods: This article proposes a framework for defining safety-relevant scenarios based on the potential collision between the subject vehicle and a challenging object, which forces the subject vehicle to depart from its planned course of action to avoid a collision. This allows defining only safety-relevant scenarios, which can directly be related to accident classification. The first criterion for defining a scenario is the area of the subject vehicle with which the object would collide. As a second criterion, 8 different positions around the subject vehicle are considered. To account for other relevant objects in the scenario, factors that influence the challenge for the subject vehicle can be added to the scenario. These are grouped as action constraints, dynamic occlusions, and causal chains.

Results: By applying the proposed systematics, a catalog of base scenarios for a vehicle traveling on controlled-access highways has been generated, which can directly be linked to parameters in accident classification. The catalog serves as a basis for scenario classification within the PEGASUS project.

Conclusions: Defining a limited number of safety-relevant scenarios helps to realize a systematic safety assurance process for automated vehicles. Scenarios are defined based on the point of the potential collision of a challenging object with the subject vehicle and its initial position. This approach allows defining scenarios for different environments and different driving states of the subject vehicle using the same mechanisms. A next step is the generation of logical scenarios for other driving states of the subject vehicle and for other traffic environments.     

E-bikers’ braking behavior: Results from a naturalistic cycling study

Abstract

Objective: The number of e-bike users has increased significantly over the past few years and with it the associated safety concerns. Because e-bikes are faster than conventional bicycles and more prone to be in conflict with road users, e-bikers may need to perform avoidance maneuvers more frequently. Braking is the most common avoidance maneuver but is also a complex and critical task in emergency situations, because cyclists must reduce speed quickly without losing balance. The aim of this study is to understand the braking strategies of e-bikers in real-world traffic environments and to assess their road safety implications. This article investigates (1) how cyclists on e-bikes use front and rear brakes during routine cycling and (2) whether this behavior changes during unexpected conflicts with other road users.

Methods: Naturalistic data were collected from 6 regular bicycle riders who each rode e-bikes during a period of 2 weeks, for a total of 32.5?h of data. Braking events were identified and characterized through a combined analysis of brake pressure at each wheel, velocity, and longitudinal acceleration. Furthermore, the braking patterns obtained during unexpected events were compared with braking patterns during routine cycling.

Results: In the majority of braking events during routine cycling, cyclists used only one brake at a time, favoring one of the 2 brakes according to a personal pre-established pattern. However, the favored brake varied among cyclists: 66% favored the rear brake and 16% the front brake. Only 16% of the cyclists showed no clear preference, variously using rear brake, front brake, or combined braking (both brakes at the same time), suggesting that the selection of which brake to use depended on the characteristics of the specific scenario experienced by the cyclist rather than on a personal preference. In unexpected conflicts, generally requiring a larger deceleration, combined braking became more prevalent for most of the cyclists; still, when combined braking was not applied, cyclists continued to use the favored brake of routine cycling. Kinematic analysis revealed that, when larger decelerations were required, cyclists more frequently used combined braking instead of single braking.

Conclusions: The results provide new insights into the behavior of cyclists on e-bikes and may provide support in the development of safety measures including guidelines and best practices for optimal brake use. The results may also inform the design of braking systems intended to reduce the complexity of the braking operation.     

A simulator study of driving behavior and mental workload in mixed-use arterial road environments

Objectives: Mixed-use urban environments, such as arterial roads with adjacent commercial land uses, represent crash locations with the highest risk. These locations are often characterized by high volumes of motor vehicle traffic, on-street parking, and interactions with multiple road user groups such as pedestrians, cyclists, and public transportation. The objective of this study was to investigate previously identified crash risk factors for mixed-use urban environments and assess how parking occupancy, center medians, and cyclist volume influence performance and workload in a driving simulator study.

Methods: Thirty participants were recruited for the study. Participants completed 6 drives that presented different combinations of cyclist volume, median condition, and parking occupancy. Incorporated into the simulator drives was a secondary peripheral detection task (PDT) designed to measure mental workload. Participants provided subjective assessments of workload using the Rating Scale Mental Effort (RSME).

Results: Mean lateral lane position was found to significantly vary across the 3 independent variables of parking occupancy, cyclist volume, and median conditions. No significant changes were identified for mean speed across the conditions. Subjective and objective measures of workload identified changes due to the presence of cyclists with slower reaction times for the PDT task when cyclists were present.

Conclusion: The findings provide insight into the interaction of road design elements in mixed-use urban road environments and demonstrate that increasingly complex environments increase driver demand. This has important road design implications for mixed-use arterial roads, which are often characterized by complex interactions between multiple road user groups.     

Understanding crashes involving roadway objects with SHRP 2 naturalistic driving study data

Introduction: Crashes involving roadway objects and animals can cause severe injuries and property damages and are a major concern for the traveling public, state transportation agencies, and the automotive industry. This project involved an in-depth investigation of such crashes based on the second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study (NDS) data including detailed information and videos about 2,689 events. Methods: The research team conducted a variety of logistic regression analyses, complemented by Support Vector Machine (SVM) analyses and detailed case studies. Results: The logistic regression results indicated that driver behavior/errors, involvement of secondary tasks, roadway characteristics, lighting condition, and pavement surface condition are among the factors that contributed significantly to the occurrence and/or increased severity outcomes of crashes involving roadway objects and animals. Among these factors, improper turning movements (odds ratio = 88), avoiding animal or other vehicle (odds ratio = 38), and reaching/moving object in vehicle (odds ratio = 29) particularly increased the odds of crash occurrence. Factors such as open country roadways, sign/signal violation, unfamiliar with roadway, fatigue/drowsiness, and speeding significantly increased the severity outcomes when such crashes occurred. The sensitivity analysis of the three SVM classifiers confirmed that driver behavior/errors, critical speed, struck object type, and reaction time were major factors affecting the occurrence and severity outcomes of events involving roadway objects and animals. Practical Applications: The study provides insights on risk factors influencing safety events involving roadway objects, including their occurrence and the severity outcomes. The findings allow researchers and traffic engineers to better understand the causes of such crashes and therefore develop more effective roadway- and vehicle- based countermeasures.     

Inclusion of phone use while driving data in predicting distraction-affected crashes

Introduction: Given the tremendous number of lives lost or injured, distracted driving is an important safety area to study. With the widespread use of cellphones, phone use while driving has become the most common distracted driving behavior. Although researchers have developed safety performance functions (SPFs) for various crash types, SPFs for distraction-affected crashes are rarely studied in the literature. One possible reason is the lack of critical distracted behavior information in the commonly used safety data (i.e., roadway inventory, traffic, and crash counts). Recently, the frequency of phone use while driving (referred to as phone use data) is recorded by mobile application companies and has become available to safety researchers. The primary objective of this study is to examine if phone use data can potentially predict distracted-affected crashes. Method: The authors first integrated phone use data with roadway inventory, traffic, and crash data in Texas. Then, the Random Forest (RF) algorithm was applied to assess the significance of the feature - phone use while driving - for predicting the number of distraction-affected crashes on a road segment. Further, this study developed two SPFs for distraction-affected crashes with and without the phone use data, separately. Both SPFs were assessed in terms of model fitting and prediction performances. Results: RF results rank the frequency of phone use as an important factor contributing to the number of distraction-affected crashes. Performance evaluations indicated that the inclusion of phone use data in the SPFs consistently improved both fitting and prediction abilities to predict distracted-affected crashes. Practical Applications: The phone use data provide new insights into the safety analyses of distraction-affected crashes, which cannot be achieved by only using the conventional roadway inventory and crash data. Therefore, safety researchers and practitioners are encouraged to incorporate the emerging data sources in reducing distraction-affected crashes.     

Analysis of naturalistic driving videos of fleet services drivers to estimate driver error and potentially distracting behaviors as risk factors for rear-end versus angle crashes

Objective: The objective of this study was to estimate the prevalence and odds of fleet driver errors and potentially distracting behaviors just prior to rear-end versus angle crashes.

Methods: Analysis of naturalistic driving videos among fleet services drivers for errors and potentially distracting behaviors occurring in the 6 s before crash impact. Categorical variables were examined using the Pearson's chi-square test, and continuous variables, such as eyes-off-road time, were compared using the Student's t-test. Multivariable logistic regression was used to estimate the odds of a driver error or potentially distracting behavior being present in the seconds before rear-end versus angle crashes.

Results: Of the 229 crashes analyzed, 101 (44%) were rear-end and 128 (56%) were angle crashes. Driver age, gender, and presence of passengers did not differ significantly by crash type. Over 95% of rear-end crashes involved inadequate surveillance compared to only 52% of angle crashes (P < .0001). Almost 65% of rear-end crashes involved a potentially distracting driver behavior, whereas less than 40% of angle crashes involved these behaviors (P < .01). On average, drivers spent 4.4 s with their eyes off the road while operating or manipulating their cell phone. Drivers in rear-end crashes were at 3.06 (95% confidence interval [CI], 1.73–5.44) times adjusted higher odds of being potentially distracted than those in angle crashes.

Conclusions: Fleet driver driving errors and potentially distracting behaviors are frequent. This analysis provides data to inform safe driving interventions for fleet services drivers. Further research is needed in effective interventions to reduce the likelihood of drivers' distracting behaviors and errors that may potentially reducing crashes.     

Impact of perceptual countermeasures on driving behavior at curves using driving simulator

Objective: The probability of crash occurrence on horizontal curves is 1.5 to 4 times higher than that on tangent sections. A majority of these crashes are associated with human errors. Therefore, human behavior in curves needs to be corrected.

Methodology: In this study, 2 different road marking treatments, optical circles and herringbone patterns, were used to influence driver behavior while entering a curve on a 2-lane rural road section. A driving simulator was used to perform the experiment. The simulated road sections are replicas of 2 real road sections in Flanders.

Results: Both treatments were found to reduce speed before entering the curve. However, speed reduction was more gradual when optical circles were used. A herringbone pattern had more influence on lateral position than optical circles by forcing drivers to maintain a safe distance from opposing traffic in the adjacent lane.

Conclusion: The study concluded that among other low-cost speed reduction methods, optical circles are effective tools to reduce speed and increase drivers’ attention. Moreover, a herringbone pattern can be used to reduce crashes on curves, mainly for head-on crashes where the main problem is inappropriate lateral position.     

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.     

The theory of planned behavior (TPB) and texting while driving behavior in college students

Objective: To examine the role of intent and other theory of planned behavior (TPB) constructs in predicting college students' willingness to text while driving (TWD).

Methods: This was a cross-sectional study. 243 male and female college students enrolled in the 2013–2014 academic year in the College of Health, Human Services & Nursing completed a survey on TWD. Inclusion criteria: All races and ethnicities, ≥18 years of age, cell phone owner, and licensed driver.

Results: Over 70% of the sample (n = 243) reported talking on a cell phone and sending and receiving text messages “at least a few times” while driving within the past week. However, only 27% reported being stopped by police. Of these, 22% reported being fined. Within the past 30 days, 26% reported reading or sending TWD and having to slam on the brakes to avoid hitting another car or pedestrian(s) as a result. In all, 47% of the variance in intention to send TWD was accounted for by the full TPB model. Intention, in turn, predicted willingness to TWD. Intention also mediated the relationship between perceived behavioral control and willingness to TWD.

Conclusion: Attitude was found to be the strongest predictor of intention. In addition, intention was found to mediate the relationship of willingness to TWD on perceived behavioral control. These findings highlight potential factors that could be targeted in behavioral change interventions seeking to prevent TWD.