Drivers’ illusions––no more risk

In this invited address to the International Congress of Applied Psychology, it is argued that traffic psychology has not had a major impact on accident prevention. The factors that have determined this are discussed. A review of the theories and models pertinent to drivers’ risk taking and road user behaviour in general is presented. It is argued that both risk-homeostasis theories and task capability model are not sufficiently precise to be used as a basis for preventive measures. Attitude–behaviour models derived from social psychology have proved to be powerful in identifying motivational factors influencing road user behaviour, but the majority of empirical evidence is based on self-reported rather than observed behaviour. It is argued that individual differences can provide a basis for accident prevention, in particular driver training.     

Investigating the effects of Rectangular Rapid Flash Beacons on pedestrian behavior and driver yielding on 25 mph streets: A quasi-experimental field study on a university campus

Rectangular Rapid-Flash Beacons (RRFBs) are safety measures that have become popular in recent years in the USA. Such equipment has demonstrated effectiveness in reducing vehicle speed and conflicts among road users, and increasing drivers’ yielding to pedestrians. However, RRFB effects on pedestrian behaviors are less well documented, and perhaps could produce contraindicated effects in crossing behavior. Specifically, RRFBs may give pedestrians a feeling of protection and induce them to more risk-taking when crossing the road. The current study was designed to investigate drivers and pedestrians’ reactions to a RRFB system installed at a university campus located in Virginia, USA. We deployed (a) field observation, using a multiple pretest/posttest non-equivalent control group quasi-experiment design and (b) interviews of students throughout the project’s multiple time periods. In total, 2454 pedestrians and 1312 drivers were observed and 265 students were interviewed. RRFB installations did not distinguish driver yielding likelihood between sites with or without RRFBs. However, driver yielding overall increased linearly over the five rounds of the study. Whether this was the result of the general presence of RRFBs on campus from the third round to the end of the fifth round is unknown. There is evidence from person interviews that students perceived increased safety for pedestrians over time. Being a RRFB chosen site or actual activation of the RRFBs did not have a significant relationship with pedestrian looking behavior either. The potential consequences of these results as well as the context of RRFB use on a university campus and generally low-speed roads are discussed.     

The effect of fatigue driving on car following behavior

Existing fatigued driving analysis methods mainly focus on lateral driving performance by using the measurements related to the steering wheel or lane position. There is a lack of research on longitudinal car following behavior. In this study, 40 professional drivers are invited to participate in field expressway driving experiment, lasting at least for 6 h. During the test, their performance is measured in terms of their self-reported fatigued driving level according to the Karolinska Sleepiness Scale (KSS), the PERcentage of eye CLOSures (PERCLOS) and the Time Headway (THW). Then the effects of the fatigued driving level on car following behavior are evaluated. The results indicate that the fatigue level (for both KSS and PERCLOS) has significantly impact on THW parameters, including the mean, standard deviation and minimum THW. An increase in KSS and PERCLOS leads to a lower mean and minimum THW. Meanwhile, the standard deviation of THW increases with the increase of KSS and PERCLOS. In conclusion, this study found that a higher fatigue level leads to the driver keeping a smaller THW when following another vehicle and choosing shorter THW to make lane change. More deviation of car following performance was also found with the increase of fatigue level. Therefore, the findings of this study can be used to explain fatigue as one of the major reasons for rear-end collisions. Also, the research findings demonstrate the impact of fatigue on driving behavior in terms of car following performance, which can be used as a measurement for monitoring fatigued drivers.     

Influence of following vehicle’s tailway and classification on subject driver’s response to the circular yellow indication

Traffic crashes at signalized intersections are frequently linked to driver behavior at the onset of the circular yellow (CY) indication. To better understand behavioral factors that influence a driver’s decision to stop or go at an intersection, this study analyzed the behavior of the driver of a subject vehicle at the onset of the CY indication. Driver performance data from 53 participants were collected in the Oregon State University Driving Simulator, simulating scenarios of driving through high-speed intersections under various conditions. Data included interactions where the driver stopped at the stop line (n = 644) or proceeded through the intersection (n = 628) in response to a CY indication. Data were analyzed as panel data while considering 12 indicator variables related to the driver’s stop/go decision. These indicator variables included time to stop line (TTSL), tailway time, following vehicle type, vehicle speed at the onset of the CY indication, and demographics (age, gender, driving experience, level of education, personal vehicle type, number of times driving per week, number of miles driving last year, participation in previous simulation studies. A random-parameter binary logit model was used to determine contributing factors for driver decision making at the onset of CY indication while accounting for unobserved heterogeneity. Four indicator variables were significantly related to the driver’s stop/go decision, but three factors varied across observations. Findings showed that a driver’s stop/go decision in response to a CY indication was associated with the time to the stop line (TTSL), tailway time to the following vehicle, subject vehicle speed at the onset of the CY indication, and driver’s age (20–36 years), but was not significantly associated with classification of the following vehicle. Also, the findings indicated that a shorter tailway increased a subject driver’s red-light running frequency. These findings provide insights into variables that affect driver decisions in a vehicle-following situation at the onset of the CY indication. This information can help make better decisions in smart traffic control systems such as to extend/decrease the green interval slightly to avoid decisions that are more difficult.     

Driving construals: Personal Construct Theory in a reckless driving context

With research revealing low road safety campaign efficacy and links between reckless driving behaviors and crash frequency, further investigation into the foundations and composition of driver education and training is required. Through two studies, the current research aimed to develop a measure that utilized the principles of Kelly’s (1955) Personal Construct Theory to (a) elicit constructs, or construals, specific to selected elements of reckless driving behaviors, (b) pilot a method in which the elicited constructs could be measured by asking participants to choose those they deemed most important, (c) group participants based on their constructs, and (d) assess between-group differences in self-reported reckless driving behavior. Results suggest that drivers can be categorized based on the constructs they use, and that rates of self-reported past engagement in reckless driving behavior, and willingness to do so in the future, vary systematically between these construal-based driver categories. Continuing research is required to develop and test applications of these findings.     

Which components of processing speed are affected in ADHD subtypes?

The term “processing speed” (PS) encompasses many components including perceptual, cognitive and output speed. Despite evidence for reduced PS in Attention-Deficit/Hyperactivity Disorder (ADHD), little is known about which component(s) is most impacted in ADHD, or how it may vary by subtypes. Participants included 151 children, ages 8–12 years, with ADHD Predominantly Inattentive Type, ADHD Combined Type and typically developing controls using DSM-IV criteria. All children completed four measures of processing speed: Symbol Search, Coding, Decision Speed, and simple reaction time. We found children with ADHD-PI and ADHD-C had slower perceptual and psychomotor/incidental learning speed than controls and that ADHD-PI had slower decision speed than controls. The subtypes did not differ on any of these measures. Mean reaction time was intact in ADHD. Hence, at a very basic output level, children with ADHD do not have impaired speed overall, but as task demands increase their processing speed becomes less efficient than controls’. Further, perceptual and psychomotor speed were related to inattention, and psychomotor speed/incidental learning was related to hyperactivity/impulsivity. Thus, inattention may contribute to less efficient performance and worse attention to detail on tasks with a higher perceptual and/or psychomotor load; whereas hyperactivity/impulsivity may affect psychomotor speed/incidental learning, possibly via greater inaccuracy and/or reduced learning efficiency. Decision speed was not related to either dimension. Results suggest that PS deficits are primarily linked to the inattention dimension of ADHD but not exclusively. Findings also suggest PS is not a singular process but rather a multifaceted system that is differentially impacted in ADHD.     

Identifying the main factors contributing to driving errors and traffic violations – Results from naturalistic driving data

Researchers have identified various factors that likely affect aberrant driving behaviors and therefore crash risk. However, it remains unclear which of these factors poses the greatest risk for either errors or violations under naturalistic driving conditions. This study investigated important variables contributing to driving errors and traffic violations based on naturalistic driving data from the second Strategic Highway Research Program (SHRP 2). In addition, this study identified factors determining the drivers’ willingness to perform common secondary tasks while driving, which have been associated with different degrees of crash risk. Results showed that anger, passenger presence, and persistent individual differences in driver behavior were the main factors associated with committed violations; surprise, high-risk visually distracting secondary tasks, and the driving task demand passing through an interchange were the main factors associated with errors. The willingness to engage in risky secondary tasks while driving appeared to be related to an overall tendency to engage in risky driving behaviors. However, drivers considered the driving context particularly when engaging in visually distracting secondary tasks. This study’s comprehensive approach should be a step towards generating a complete model of the variables that contribute to, or mitigate dangers in traffic.     

Age group differences in self-reported aggressive driving perpetration and victimization

Aim

This study examined whether the predictors of self-reported aggressive driving perpetration and victimization vary across age groups.

Method

Based on data from a general-population telephone survey conducted from July 2002 through June 2005, three groups of drivers were examined: 18–34 year-olds (N = 1522), 35–54 year-olds (N = 2726), and 55 years of age or older (N = 1883). For each age group sample, logistic regression analyses examined self-reported aggressive driving perpetration and victimization in the last 12 months by measures of driving exposure, heavy drinking, cannabis use, and drinking-driving, while controlling for demographic factors.

Results

The prevalence of aggressive driving perpetration within the past year was highest for the youngest age group (51%), followed by the middle-aged group (37%), and then the oldest age group (18%). The same pattern of results was found for prevalence of aggressive driving victimization (54%, 47%, and 31%, respectively). Controlling for demographic factors, the predictors of perpetration were generally consistent across the age groups. The logistic regression model for the youngest drivers revealed that those who reported stressful driving, heavy drinking, and cannabis use had significantly increased odds of reporting perpetration of aggressive driving. For middle-aged and older drivers, stressful driving, driving on busy roads, cannabis use, and driving after drinking were associated with perpetration. In addition, increased mileage contributed to perpetration in the oldest group. The findings for victimization by aggressive driving were similar. The logistic regression model for the youngest age group identified stressful driving, cannabis use, and higher annual mileage as being associated with victimization. For the oldest age group, these same variables were significant predictors of victimization, in addition to driving on busy roads. The logistic regression for the middle-aged group identified the same predictors as that of the oldest age group; however, interestingly driving after drinking was found to predict lower victimization among middle-aged drivers.

Conclusions

Although the prevalence of aggressive driving perpetration and victimization declined with age, the factors that contributed to aggressive driving remained generally stable across the lifespan. The results suggest that efforts to reduce aggressive driving among young drivers may prove to be effective for drivers from all age groups.     

Modeling and prediction of driver preparations for making a right turn based on vehicle velocity and traffic conditions while approaching an intersection

This paper focuses on modeling and predicting the influence of a vehicle’s velocity and the relative position between a driver’s vehicle and a vehicle to the front or rear on the onset of driver preparations for making a right turn at an intersection. Repeated experiments were carried out on a public road to measure driver preparations, including releasing the accelerator pedal, moving the right foot to cover the brake pedal, and activating the turn signal, as well as to record vehicle velocity and the relative distances to the leading and following vehicles. Structural equation modeling was applied to estimate these relationships quantitatively. Two separate latent variables accounting for the interaction between the driver’s vehicle and leading or following vehicles, and the onset point where driving behavior changes from straight mode to preparation mode before a right turn, were introduced in the specification of the structural equation model. The model estimates and testing indicate that the proposed structural equation model represents well the relationship hypothesized by observational analysis: that the vehicle velocity and the traffic conditions surrounding the driver’s vehicle strongly influence the driver’s deceleration and more weakly influenced turn signal activation. The proposed structural equation model contributes to the prediction of the onset locations of covering the brake pedal and activating the turn signal based on the vehicle velocity and the relative distances to the front and rear vehicles when the accelerator pedal is released. The prediction accuracy is high compared with predictions in which the vehicle velocity and the headway or rear distances are not taken into account or predictions using a single regression model with one independent variable, namely driving speed. Finally, a possible new addition to in-vehicle navigation systems that detects unusual driver behavior by predicting the driver’s preparatory maneuvers is discussed.     

Drivers’ attitudes toward imperfect distraction mitigation strategies

Studies were conducted to assess driver acceptance of and trust in distraction mitigation strategies. Previous studies have shown that in-vehicle tasks undermine driver safety, and that there is a need for strategies to reduce the effects of in-vehicle distractions. Trust and acceptance of such strategies strongly influence their effectiveness. Different strategies intended to reduce distraction were categorized in a taxonomy. Focus groups were conducted to help refine this taxonomy and explore driver acceptance issues related to these strategies. A driving simulator experiment was then conducted using two of the strategies: an advising strategy that warns drivers of potential dangers and a locking strategy that prevents the driver from continuing a distracting task. These strategies were presented to 16 middle-aged and 12 older drivers in two modes (auditory, visual) with two levels of adaptation (true, false). Older drivers accepted and trusted the strategies more than middle-aged drivers. Regardless of age, all drivers preferred strategies that provided alerts in a visual mode rather than an auditory mode. When the system falsely adapted to the road situation, trust in the strategies declined. The findings show that display modality has a strong effect on driver acceptance and trust, and that older drivers are more trusting and accepting of distraction mitigation technology even when it operates imperfectly.