Archive for category pedestrians

Pedestrians’ road crossing decisions and body parts’ movements is now available online

the final version of your article Pedestrians’ road crossing decisions and body parts’ movements is now available online, containing full bibliographic details.

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Pedestrians’ road crossing decisions and body parts’ movements

A new publication in TR part F by Semyon Kalantarov, Raziel Riemer, and Tal Oron-Gilad.


  • Road-crossing simulator synched with a 3D motion capturing system was built
  • Time pressure and longer wait times cause riskier crossing decisions
  • Pedestrians adjusted posture, crossing speed and timing of crossing to the risk taken
  • Body parts’ movement prior to the crossing can be divided into four increments

In this study we examined pedestrians’ crossing decision, body parts’ movement and full body movement, just before and during road crossing in a simulated setup. To accomplish this, a novel experimental setup for analyzing pedestrians’ crossing behavior and motion was developed where the simulated display was synchronized with a 3D motion capturing system. Twenty participants, divided into control and an experimental time pressure group, observed sixteen short (less than 30 seconds) and long road (70 seconds or more) crossing scenarios with varying crossing opportunities. Based on the crossing opportunities they were asked to cross a 3.6 m wide one-lane one way urban road. It was found that the crossing initiation process consists of four incremental movements of body parts: the head and the shoulder first; the hip, wrist and elbow second; the knee as a separate joint, and finally the ankle. Results showed that pedestrians’ decision to cross and body parts movement are influenced by time pressure and wait time for a safe crossing opportunity. Specifically, pedestrians prepare their body parts earlier, initiate their crossing earlier, and adjust their speed to compensate for the risk taken in less safe or non-safe crossing opportunities. Within the control group, women tended to be more risk avoiding than men, however those differences disappeared in the time pressure group. Most importantly, the findings provide initial evidence that this novel simulation configuration can be used to gain precise knowledge of pedestrians’ decision-making and movement processes.

What did we learn about pedestrians crossing movement?
Pedestrians change their strategy as a function of internal and external reasons:

  • Take higher risk when crossing opportunities are sparse or when they are under time pressure

Initiate crossing, Kalantarov, Riemer, and Oron-Gilad for TRF

  • Prepare their movement in advance by adjusting body position

body parts movement, Kalantarov, Riemer, and Oron-Gilad for TRF

  • Change the timing of crossing as a function of perceived risk

timing of crossing, Kalantarov, Riemer, and Oron-Gilad for TRF

  • Adjust their crossing speed to the perceived risk

walking speed, Kalantarov, Riemer, and Oron-Gilad for TRF

Kalantarov, S. , Riemer, R., Oron-Gilad, T. (in press). Pedestrians’ road crossing decisions and body parts’ movements. Transportation Research Part F: Psychology and Behaviour.

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Child Pedestrians’ perceived risk of the crossing place

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).

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Cell phone conversations and child pedestrian’s crossing behavior; a simulator study

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),

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?


Interactive app to view the eye gaze data. Click on the link and follow the instructions shown above.

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Validation study: Dome Pedestrian Simulator


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.



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Are child-pedestrians able to identify hazardous traffic situations?

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)

Are Child pedestrians able to identify hazardous traffic situations?


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.

Pedestrians' crossing scenarios

Traffic scenes for pedestrian crossing (only the left part of the scene is shown). Top: no moving elements, Mid: road curvature obscuring FOV, Bottom: Parked vehicles obscuring FOV.

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Visual search strategies of child-pedestrians in road crossing tasks

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.



Open this link to see a sample video of a scenario as seen by a young pedestrian

Why Dirichlet?

  • 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.
Predicted means (in each AOI) using Dirichlet model across all scenarios per age group

Predicted means (in each AOI) using Dirichlet model across all scenarios per age group

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