Archive for category Transportation & Safety
We are excited to present our studies in the 10th University Transportation Centers Spotlight Conference on Pedestrian and Bicycle Safety to be held December 1-2 ,2016 in the Keck Center, Washington DC.
Here is a link to a short description of the BGU pedestrian laboratory.pedestrian-lab-brochure and to a short brief about the work we are presenting (Child Pedestrians’ perceived risk of the crossing place).
Yisrael Parmet, Lee Shoham and Tal Oron-Gilad
Presentation at the ICTTP 2016.
link to presentation: How full vehicle automation affects…
DESCRIPTION: The purpose of this study was to examine the effects of full vehicle automation on performance and behavior, specifically the transition from a fully automated mode to manual driving, under the influence of alcohol and without it. Previous studies have revealed a deterioration in driving performance while transitioning from an automated mode to manual driving and further suggested that automated driving may result in a degraded situation awareness. It was therefore hypothesized that the performance of secondary driving related tasks would deteriorate during the automated phase, while performance of secondary non-driving related tasks would improve, in comparison to manual driving. It was further hypothesized that the transition from automated to manual driving would damage driving performance and that alcohol, while affecting performance of all driving conditions, would affect the manual phase following the automated phase to a greater extent. Method. A fixed base driving simulator was used. The design contained a first manual phase, an automated phase and another manual phase, under the influence of BAC 0.05% alcohol and without it. The study involved 16 participants. Two type of secondary tasks were introduced to the participants, driving and non-driving related tasks and the precision (% of success) and response time (RT) were measured. Driving quality indices such as speed and lane position were measures along the drive as well. Results. In the nondriving related secondary task we found significant differences in the response time only, the response time under the placebo condition were on average 15% higher than the response time under the alcohol condition. In the driving related secondary task we found significant difference in both measures, the participants on average were 5% more accurate and 13% faster while they drove manually. The results of the driving quality indices indicate a deterioration in precision of driving related secondary tasks, and a decrease in driving velocity after an automated phase, the latter being moderated by alcohol, which causes an increase in driving velocity. Conclusion. As hypothesized the performance of secondary driving related tasks deteriorated during the automated phase but contrary to our hypothesis, the automation had no influence on the performance of the non-driving secondary task. Opposing to our hypothesis, we found no evidence that alcohol deteriorates the drivers’ performance in the two types of secondary tasks. The last results might be due to the low level of alcohol that was used in the experiment. As expected we found that driving quality decreases after automated phase and while performing secondary tasks.
Can traffic violations be traced to gender-role, sensation seeking, demographics and driving exposure?
Background: Traffic safety is often expressed as the ‘inverse of accidents’. However, it is
more than the mere absence of accidents. Past studies often looked for associations
between accidents and self-reports like the Driver Behaviour Questionnaire
(DBQ; Reason, Manstead, Stradling, Baxter, & Campbell, 1990). The focus in this study
changed from counting accidents to quantifying unsafe acts as violations. The objective
was to show that drivers’ specific violations can be traced to personal characteristics such
as sensation seeking (SSS-V; Zuckerman, 1994), gender role (BSRI; Bem sex role inventory,
Bem, 1974), demographics, and driving exposure.
Method: A web-based questionnaire was distributed, integrating several known questionnaires.
Five hundred and twenty-seven questionnaires were completed and analyzed.
Results: Sensation seeking, gender role, experience, and age predicted respondents’ score
on the DBQ, as well as the interaction of sensation seeking with gender and gender role.
Gender role was a more valid predictor of driver behavior than gender.
Conclusions: The effect of gender role on drivers’ self-reported violation tendency is the
most interesting and the most intriguing finding of this survey and indicates the need to
further examine gender role affects in driving.
This is our most recent publication, accepted for publication in Safety Science.
Please cite this article in press as: Tapiro, H., et al. Cell phone conversations and child pedestrian’s crossing behavior; a simulator study. Safety Sci. (2016), http://dx.doi.org/10.1016/j.ssci.2016.05.013
Cell phone conversations and child pedestrian’s crossing behavior; a simulator study
Hagai Tapiro, Yisrael Parmet and Tal Oron-Gilad
Child pedestrians are highly represented in fatal and severe road crashes and differ in their crossing behavior from adults. Although many children carry cell phones, the effect that cell phone conversations have on children’s crossing behavior has not been thoroughly examined. A comparison of children and adult pedestrians’ crossing behavior while engaged in cell phone conversations was conducted. In a semi-immersive virtual environment simulating a typical city, 14 adults and 38 children (11 children aged 7-8; 18 aged 9-10 and 9 aged 11-13), experienced road crossing related traffic-scene scenarios. They were requested to press a response button whenever they felt it was safe to cross. Eye movements were tracked. Results have shown that all age groups’ crossing behaviors were affected by cell phone conversations. When busy with more cognitively demanding conversation types, participants were slower to react to a crossing opportunity, chose smaller crossing gaps, and allocated less visual attention to the peripheral regions of the scene. The ability to make better crossing decisions improved with age, but no interaction with cell phone conversation type was found. The most prominent improvement was shown in ‘safety gap’; each age group maintained a longer gap than its predecessor younger age group. In accordance to the current study, it is safe to say that cell phone conversations can hinder child and adult pedestrians’ safety. Thereby, it is important to take those findings in account when aiming to train young pedestrians for road-safety and increase public awareness.
Interested in seeing an interactive visualization app of our data?https://eyemove.shinyapps.io/cell-phone/
Here we report upon results of a validation study conducted on our unique pedestrian simulator.
The simulator validation study confirms the simulator’s ability to correctly simulate the real road environment, and strengthens the reliability as a source for statistical Inference. The goal of this work was to investigate whether the Dome simulator successfully simulates typical pedestrian environment in a manner that will elicit people to act in the same manner as they would in the real world crossing situations. Data analysis shows that the simulator delivers more reliable results concerning speeds rather than distances. Questionnaires analyses show that the simulator’s faith to reality regarding the display, sound effect and perspective is medium.
One more publication within the child pedestrian’s realm of road crossing co-authored by Anat Meir and Yisrael Parmet published in Safety Science, Vol. 80, pages 33-40 (2015)
we explored child-pedestrians’ HP skills employing hazard detection task in virtual settings (our Dome lab). We used the same approach that we have used previously in the driving HP domain to study novice drivers. As pedestrians’ age increased their awareness toward potential hazards increased. 7–9-year-olds reported less instances of FOV obscured by parked vehicles. 7–9-year-olds lingered more in identifying instances of FOV obscured by parked vehicles.
Background. Child-pedestrians are more prone to fail in identifying hazardous situations. Aiming to better understand the development of hazard-perception abilities in dynamic road situations we examined participants’ hazard detection abilities in a virtual environment.
Method. Experienced-adult participants and child-pedestrians observed typical road crossing related scenarios from a pedestrian’s point of view and engaged in a hazard detection task.
Results. Consistent with our hypotheses, less instances of obscured field of view by parked vehicles were reported as hazardous by 7–9-year-olds, who were also prone to linger more in identifying situations depicting field of view partially obscured by parked vehicles compared to all other age groups. Reports of obscured field of view by road curvature as hazardous increased with age.
Conclusions. Understanding child-pedestrians’ shortcomings in evaluating traffic situations contribute to the effort of producing intervention techniques which may increase their attentiveness toward potential hazards and lead toward reduction in their over-involvement in crashes.
Hagai Tapiro, Anat Meir, Yisrael Parmet & Tal Oron-Gilad
Presentation at HFES-EU Annual meeting, Torino 2013
Children are over-represented in road accidents, often due to their limited ability to perform well in road crossing tasks. The present study examined children’s visual search strategies in hazardous road-crossing situations. A sample of 33 young participants (ages 7-13) and 21 adults observed 18 different road-crossing scenarios in a 180° dome shaped mixed reality simulator. Gaze data was collected while participants made the crossing decisions. It was used to characterize their visual scanning strategies. Results showed that age group, limited field of view, and the presence of moving vehicles affect the way pedestrians allocate their attention in the scene. Adults tend to spend relatively more time in further peripheral areas of interest than younger pedestrians do. It was also found that the oldest child age group (11-13) demonstrated more resemblance to the adults in their visual scanning strategy, which can indicate on a learning process that originates from gaining experience and maturation. Characterization of child pedestrian eye movements can be used to determine readiness for independence as pedestrians. The results of this study, emphasize the differences among age groups in terms of visual scanning. This information can contribute to promote awareness and training directions.
Dirichlet regression model and analysis
For each scenario, five areas of interest were defined (as shown in the Figure). The close range central area was defined as the 10 meters of road in each side from the pedestrian’s point of view (AOI 3). Then symmetrically areas to the right of the center and to the left were defined. The medium right/left range (AOIs 2/4) was the part of the road distant at least 10 meter to the right/left of the point of view but less than 100 meters away. The far right/left range (AOIs 1/5) was the part of the road at least 100 meter or more to the right/left of the pedestrian point of view.
- For each participant and scenario, the total Gaze distribution over the five AOI’s sums up to one.
- Therefore Gaze distribution is compositional data i.e., non-negative proportions with unit-sum.
- These types of data arise whenever we classify objects into disjoint categories and record their resulting relative frequencies, or partition a whole measurement into percentage contributions from its various parts.
- Attempts to apply statistical methods for unconstrained data often lead to inappropriate inference.
- Dirichlet regression suggested by Hijazi and Jernigan (2009) is more suitable for such cases.
How to use?
- The Dirichlet regression model was fitted using DirichletReg package, in R Language. Applying a backward elimination procedure found the best fitting model has three significant main effects.
What did we find?
- The dependent variable was the vector of AOIs and the independent variables were Age-group, POV and FOV; all of them were statistically significant (p <0.05). Predicted means for the percentage of time spent in each AOI for each age group based on the Dirichlet regression model are shown in the following figure and reveal differences among age groups. Note how children aged 9-10 spend more time gazing at the central area, note also the differences between mid-left and mid-right.