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Abstract

Human behavior is influenced by the presence of others, which scientists also call 'the audience effect'. The use of social control to produce more cooperative behaviors may positively influence road use and safety. This study uses an online questionnaire to test how eyes images affect the behavior of pedestrians when crossing a road. Different eyes images of men, women and a child with different facial expressions-neutral, friendly and angry-were presented to participants who were asked what they would feel by looking at these images before crossing a signalized road. Participants completed a questionnaire of 20 questions about pedestrian behaviors (PBQ). The questionnaire was received by 1,447 French participants, 610 of whom answered the entire questionnaire. Seventy-one percent of participants were women, and the mean age was 35 ± 14 years. Eye images give individuals the feeling they are being observed at 33%, feared at 5% and surprised at 26%, and thus seem to indicate mixed results about avoiding crossing at the red light. The expressions shown in the eyes are also an important factor: feelings of being observed increased by about 10-15% whilst feelings of being scared or inhibited increased by about 5% as the expression changed from neutral to friendly to angry. No link was found between the results of our questionnaire and those of the Pedestrian Behavior Questionnaire (PBQ). This study shows that the use of eye images could reduce illegal crossings by pedestrians, and is thus of key interest as a practical road safety tool. However, the effect is limited and how to increase this nudge effect needs further consideration.
RESEARCH ARTICLE
Eye image effect in the context of pedestrian safety: a French
questionnaire study [version 1; peer review: awaiting peer
review]
Cédric Sueur 1,2, Anthony Piermattéo3, Marie Pelé3
1IPHC, UMR7178, Université de Strasbourg, CNRS, Strasbourg, France
2Institut Universitaire de France, Paris, China
3ETHICS EA7446, Lille Catholic University, Lille, France
First published: 23 Feb 2022, 11:218
https://doi.org/10.12688/f1000research.76062.1
Latest published: 23 Feb 2022, 11:218
https://doi.org/10.12688/f1000research.76062.1
v1
Abstract
Human behavior is influenced by the presence of others, which
scientists also call ‘the audience effect’. The use of social control to
produce more cooperative behaviors may positively influence road
use and safety. This study uses an online questionnaire to test how
eyes images affect the behavior of pedestrians when crossing a road.
Different eyes images of men, women and a child with different facial
expressions -neutral, friendly and angry- were presented to
participants who were asked what they would feel by looking at these
images before crossing a signalized road. Participants completed a
questionnaire of 20 questions about pedestrian behaviors (PBQ). The
questionnaire was received by 1,447 French participants, 610 of whom
answered the entire questionnaire. Seventy-one percent of
participants were women, and the mean age was 35 ± 14 years. Eye
images give individuals the feeling they are being observed at 33%,
feared at 5% and surprised at 26%, and thus seem to indicate mixed
results about avoiding crossing at the red light. The expressions
shown in the eyes are also an important factor: feelings of being
observed increased by about 10-15% whilst feelings of being scared or
inhibited increased by about 5% as the expression changed from
neutral to friendly to angry. No link was found between the results of
our questionnaire and those of the Pedestrian Behavior Questionnaire
(PBQ). This study shows that the use of eye images could reduce
illegal crossings by pedestrians, and is thus of key interest as a
practical road safety tool. However, the effect is limited and how to
increase this nudge effect needs further consideration.
Keywords
prosociality, road crossing, reputation, accident prevention,
pedestrian behavior
Open Peer Review
Approval Status AWAITING PEER REVIEW
Any reports and responses or comments on the
article can be found at the end of the article.
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F1000Research 2022, 11:218 Last updated: 23 FEB 2022
Corresponding author: Cédric Sueur (cedric.sueur@iphc.cnrs.fr)
Author roles: Sueur C: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project
Administration, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation; Piermattéo A: Investigation,
Methodology, Validation, Writing – Review & Editing; Pelé M: Conceptualization, Formal Analysis, Funding Acquisition, Investigation,
Methodology, Project Administration, Supervision, Validation, Writing – Review & Editing
Competing interests: No competing interests were disclosed.
Grant information: The author(s) declared that no grants were involved in supporting this work.
Copyright: © 2022 Sueur C et al. This is an open access article distributed under the terms of the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
How to cite this article: Sueur C, Piermattéo A and Pelé M. Eye image effect in the context of pedestrian safety: a French
questionnaire study [version 1; peer review: awaiting peer review] F1000Research 2022, 11:218
https://doi.org/10.12688/f1000research.76062.1
First published: 23 Feb 2022, 11:218 https://doi.org/10.12688/f1000research.76062.1
Page 2 of 16
F1000Research 2022, 11:218 Last updated: 23 FEB 2022
1. Introduction
Whatever the size and complexity of the society we live in, social life involves respecting rules or norms in order to
maintain peace and cohesion (Coleman, 1994;Elster, 1989;Fehr and Fischbacher, 2004). Violating these social norms
can unbalance the public good insofar that law-breakers will gain more benefits than their honest counterparts, or other
individuals will be put at risk. In order to balance costs and benefits, punishment or police behaviors have evolved in
humans societies (Fehr and Gächter, 2000,2002), and in other primate societies (Boyd and Richerson, 1992;Flack et al.,
2006;Mendes et al., 2018;Riedl et al., 2012). Like other primate species, humans have developed emotional bases for
prosocial behaviors allowing cooperation. These emotions are concerns, empathy and a sense of morality and of
reputation (De Waal, 2006;Jensen et al., 2014;Keltner and Anderson, 2000;Penner et al., 2005;Tomasello and Vaish,
2013) and they are defined as moral emotions by Haidt (2003). Being concerned or empathic enables humans to recognize
when they are doing something wrong and correct their behavior in order to maintain a prosocial reputation and continue
interacting cooperatively with their conspecifics (Alexander, 1987;Bateson et al., 2006;Burnham and Johnson, 2005).
Our behavior is therefore influenced by the presence of others, which scientists also call the audience effect(Filiz-Ozbay
and Ozbay, 2014;Kurzban et al., 2007;Zuberbühler, 2008). Feeling observed by real persons or an imagined audience,
therefore has an impact on human behavior. Embarrassment, for instance, is defined as the acute state of flustered,
awkward, abashed chagrin that follows events that increase the threat of unwanted evaluations from real or imagined
audiences(Eller et al., 2011;Miller, 1996). Being observed also tends to make individuals more compliant (Dear et al.,
2019;Rodriguez Mosquera et al., 2011). With this in mind, Bateson et al. (2006) conducted an experiment to test the
effect of eye images on cooperative behavior. In the coffee area of a building at the University of Newcastle, an honesty
box was used for people to make contributions to the coffee fund. The experiment consisted of placing pictures of eyes or
flowers close to this box and assessing whether they led to differences in the contributions made. The authors found that in
the presence of eye images, subjects paid on average 2.76 times more than when flowers images were displayed. Still
comparing the effect of these flowers images as control to those of eye images, other studies have found similar prosocial
effects during everyday events. For example, eye images had prosocial effects such as cooperation for the clearing of trays
in a university cafeteria (Ernest-Jones et al., 2011) and for waste sorting at a bus stop (Francey and Bergmüller, 2012).
Similar results were found in experiments in more specific contexts such as blood donations: While eye images on flyers
did not result in differences in pledge with a logo as control, more realdonations were made by students who got the
flyers with eyes image (Sénémeaud et al., 2017). Other than the activation of a sense of being seen(Pfattheicher and
Keller, 2015) or the desire to maintain a pro-social reputation (Bateson et al., 2006), it is important to note that humans
possess neurons that respond to faces and eyes and activate such prosocial behaviors (Emery, 2000;Haxby et al., 2000).
Bateson et al. (2013) suggest that eye images induce more pro-social behavior regardless of local norms, thus suggesting
that the application of eye images could be a means to combat anti-social behavior by triggering a feeling of shame
(Nugier et al., 2007).
The use of social control to produce more cooperative behaviors may positively influence road use and safety. The
limitation of conflicts and accidents on road infrastructures is directly dependent upon the respecting of rules by the
numerous pedestrians and drivers. However, more than 8000 pedestrians die in road accidents in Europe every year,
25% of whom die when using crosswalks (Guéguen et al., 2015,2016). These lethal accidents are due to cars not stopping
at signalized intersections but also to pedestrians crossing illegally at the red signal (Sueur et al., 2013). Past studies show
that individuals do not cross illegally when other pedestrians are present (Pelé et al., 2017,2019a,2019b). Visual
communication between two individuals can lead to a change in the receiver's behavior and this effect can be found in a
road context. For example, eye contact is an important element. Some studies explored the effect of gaze and smile on the
propensity of drivers to stop at signalized intersections and allow pedestrians to cross (Guéguen et al., 2015,2016). A
pedestrian waiting at the edge of an unmarked crosswalk has a greater likelihood to cross if s/he seeks to make visual
contact with an approaching driver than if s/he is not looking towards the approaching car, with 67.7% of cars stopping
versus 55.1%, respectively (Guéguen et al., 2015). If in addition to this visual contact the pedestrian smiles, 62.9% of
drivers stopped compared to 50.1% if the pedestrian sought visual contact with a neutral face (Guéguen et al., 2016).
These studies show that visual contact can modify the behavior and speed of drivers, and highlight that the facial
expression of the pedestrian also has an impact (Ren et al., 2016). However, these studies are rare and more research is
needed on how human facial expressions affect the probability that pedestrians will cross the road illegally. This research
may have great potential in terms of applications in the field of road safety, especially regarding the regulation of
pedestrian behaviors.
This study aims to test the effect of eye images on the behaviors of pedestrians crossing at the red light. We collected
different images of eyes from five different persons (two men, two women and one child) expressing different facial
expressions (neutral, friendly and angry) and one image of flowers for use as a control (Figure 1). These images were used
in an online questionnaire that asked participants what they would feel if they saw these images before crossing a road.
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Would they feel observed, scared or surprised? Would the images discourage or encourage them to cross at the red light?
These questions were chosen with caution in order to make the questionnaire valid and are based on previous studies
(Bateson et al., 2013;Ernest-Jones et al., 2011;Francey and Bergmüller, 2012;Saeed et al., 2020). We compared
their answers to these questions with sociodemographic variables (gender, age, geographical zone and city size) and also
with a previous well-known questionnaire called Pedestrian Behavior Questionnaire(hereafter referred to as PBQ,
Appendix A: Deb et al., 2017;Granié et al., 2013), which tested the propensity of pedestrians to violate rules, make errors
or lapses, or show positive or aggressive behaviors. The angry eye images are expected to have a stronger emotional
impact on participants and thus prevent them from crossing illegally (Bateson et al., 2006). We also expect a gender effect
and an age effect, with a stronger impact of eye images on women and younger individuals. Indeed, women might feel
more observed or scared than men when looking at the eye images; studies have shown women to be more empathic and
thus more receptive in different situations (Flynn, 2000;Furnham et al., 2003;Kellert & Berry, 1987;Miller et al., 2009).
In the context of road crossing, fewer illegal and risky behaviors are observed in women than in men (Holland and Hill,
2007;Pelé et al., 2017;Sueur et al., 2013;Tom and Granié, 2011), with more positive behaviors and fewer rule violations
or aggressive behaviors (Deb et al., 2017;Tom and Granié, 2011). Studies show that younger people are less respectful of
rules (Holland and Hill, 2007;Pelé et al., 2017;Pfeffer and Hunter, 2013). We also expect regional differences in
reactions to eye images, as interregional differences were observed in road accidents (Eksler et al., 2008;Lassarre and
Thomas, 2005). Correlations are expected between the responses to the eye image questionnaire and the PBQ. Indeed,
participants showing higher scores for violations or aggressive behaviors in the PBQ (i.e. the less prosocial individuals)
should be less affected by eye images than participants who show more positive behaviors (i.e., the more empathic and
cooperative individuals).
2. Methods
2.1 Questionnaire
The questionnaire was designed in three steps: 1. Eye images, 2. Pedestrian behavior questionnaire and 3. Socio-
demographic questions.
Figure 1. Images used to understand the effect of eye images on pedestrian behaviors. The flower image is a
control. The effect of eye images was tested using three different expressions (friendly, neutral and angry respec-
tively from left to right) in the eyes of five different persons. Five questions were asked about feelings for each eye
image: Do you feel observed, scared, inhibited, surprised or encouraged? (See methods for details). The score for
each feeling and each image is the average score for all participants. Image credits: Cédric Sueur.
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Eye images: To test the effect of eye images, pictures were taken of five different individuals (one child, two women and
two men) with three different facial expressions (friendly, neutral and angry). A picture of flowers was used as a control.
Each picture was taken in black and white with the same contrast and brightness calibration (see Figure 1). For each of the
16 pictures (15 eyes and 1 control), participants were asked to answer five questions about their feelings when looking at
the picture:
1. Observation: Do you feel observed when looking at this picture? Answer: yes/maybe/no
2. Fear: Are you scared when looking at this picture? Answer: yes/maybe/no
3. Inhibition: Would looking at this picture prevent you from crossing at the red light? Answer: yes/maybe/no
4. Surprise: Would this picture make you feel surprised if you saw it before crossing the road? Answer:
yes/maybe/no
5. Incitation: Would seeing this picture encourage you to cross at the red pedestrian signal? Answer: yes/maybe/no
These five questions were chosen with caution. In order to check the validity of the questionnaire, we chose the four first
questions to evaluate the negative effects of the eyes images and the fifth question to evaluate a positive effect. This means
that we expected to have the scores of the fourth first questions to be positively correlated between them but negatively
correlated with the fifth question. The questionnaire was validated with results (Section 3.1.) following our predictions.
As five questions for 16 pictures results in a very long questionnaire of 80 questions, 4 images (with for each image the
five questions) were randomly selected for the participants to answer. We checked for a homogeneous distribution of
questions to participants by making packages of 20 questions (for 4 images).
Sociodemographic questions: Participants were asked to indicate their gender (female or male), age (as numeric) and
French postal code. The French postal code provides the city population size (DataNova Opensource, 2014 census) and
the Defense and Security Zones (French Homeland Ministrysee https://doi.org/10.5281/zenodo.5745446.
Pedestrian behavior questionnaire (PBQ): Participants completed a questionnaire of 20 questions about pedestrian
behaviors by Deb et al. (2017). We used a pedestrian behavior questionnaire (PBQ) with 20 questions, developed by (Deb
et al., 2017) and taken from (Tom and Granié, 2011). The order of questions was randomly attributed. In parenthesis, the
averagestdv of the score (from 1 never - to 6 always -) attributed by participants to each question (N=611).
Violations (V)
V1 I cross the street even though the pedestrian light is red. (3.73 1.35)
V2 I cross diagonally to save time. (3.45 1.42)
V3 I cross outside the pedestrian crossing even if there is one (crosswalk) less than 50 m away. (3.51 1.49)
V4 I take passageways forbidden to pedestrians to save time. (2.35 1.36)
Errors (E)
E1 I cross between vehicles stopped on the roadway in traffic jams. (3.34 1.43)
E2 I cross even if vehicles are coming because I think they will stop for me. (2.36 1.19)
E3 I walk on cycling paths when I could walk on the sidewalk. (1.91 0.96)
E4 I run across the street without looking because I am in a hurry. (1.27 0.63)
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Lapses (L)
L1 I realize that I have crossed several streets and intersections without paying attention to traffic. (1.52 0.82)
L2 I forget to look before crossing because I am thinking about something else. (1.65 0.78)
L3 I cross without looking because I am talking with someone. (1.65 0.85)
L4 I forget to look before crossing because I want to join someone on the sidewalk on the other side. (1.45 0.70)
Aggressive Behaviors (A)
A1 I get angry with another road user (pedestrian, driver, cyclist, etc.), and I yell at him. (1.94 1.17)
A2 I cross very slowly to annoy a driver. (1.45 0.87)
A3 I get angry with another road user (pedestrian, driver, cyclist, etc.), and I make a hand gesture. (2.08 1.20)
A4 I have gotten angry with a driver and hit their vehicle. (1.25 0.70)
Positive Behaviors (P, Reverse-scaled items)
P1 I thank a driver who stops to let me cross. (5.55 0.82)
P2 When I am accompanied by other pedestrians, I walk in single file on narrow sidewalks so as not to bother the
pedestrians I meet. 4.05 1.46)
P3 I walk on the right-hand side of the sidewalk so as not to bother the pedestrians I meet. (4.04 1.48)
P4 I let a car go by, even if I have the right of way, if there is no other vehicle behind it. (3.60 1.49)
Questions were presented in random order. PBQ was the first complete questionnaire to study a broad range of aspects of
pedestrian behavior on the road for all age groups. This questionnaire was originally developed by Tom and Granié
(2011) with 47 questions. We chose the short version, which is considered as reliable as the long version according to Deb
et al. (2017) and Tom and Granié (2011), in order to avoid demotivating participants. The 20 questions are categorized
into five items as followed:
1. Transgression: deliberate deviation from social rules without intention to cause injury or damage, Reason et al.,
1990;
2. Error: deficiency in knowledge of traffic rules and/or in the inferential processes involved in making a decision,
Rasmussen, 1980;Reason et al., 1990;
3. Lapse: unintentional deviation from practices related to a lack of concentration on the task; forgetfulness,
Reason et al., 1990;
4. Aggressive Behavior: tendency to misinterpret other road usersbehavior, resulting in the intention to annoy or
endanger, Baxter et al., 1990;Lawton et al., 1997;
5. Positive Behavior: behavior that seeks to avoid violation or error and/or seeks to ensure traffic rule compliance,
Özkan and Lajunen, 2005.
The participants were required to answer the questions using a 6-point Likert scale (1-very infrequently or never, 2-quite
infrequently, 3-infrequently, 4-frequently, 5-quite frequently, 6-very often or always).
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2.2 Survey administration and participants
The survey was created using LimeSurvey (Engard, 2009;Jayasundara et al., 2010;LimeSurvey Project Team, 2012) and
administered online to the French population through mails and social media (Facebook and Twitter).
The questionnaire was received by 1,447 participants, 610 of whom answered the entire questionnaire (all three steps).
The resulting dataset was used in our analyses (N=610). The mean time to answer the entire questionnaire was seven
minutes. 71% of participants were women, and the mean age was 35 14 years. The geographic repartition of the
population is as follows: Hauts-de-France (N=17), Ile-de-France (N=100), Ouest (N=92), Est (N=219), Sud-Ouest
(N=18), Sud-Est (N=75), Sud (N=89). These factors were included in statistical analyses to avoid selection biases
selection.
2.3 Research ethics
All data were anonymous, and participants were given sequential numerical identities corresponding to the moment they
answered the questionnaire. Participants could obtain information about the study and its results by contacting the authors
via an email address provided at the end of the questionnaire. We followed the ethical guidelines of our institution
(CNRS-IPHC, Strasbourg, France). This study received ethical approval from the road security direction of the French
Homeland Ministry (RefCNRS190529).
Formal written agreement or parental consent was obtained from the five people photographed for the eye images. They
were aware of, and agreed to, the intended use of the photographs in the questionnaire and in the publication.
2.4 Statistical analyses
We first calculated the mean score for each question and each image. This score ranges from 0 to 1, where 0 indicates
100% of participants answering Noto the question and 1 corresponds to 100% answering Yes. The scores concerning
all three answers [Yes, maybe, No] and the scores concerning only two answers [Yes, No] are correlated at 95% (linear
regression, P<0.0001, R
2
= 0.95, N = 80). For this reason and in order to simplify statistical analyses, we only used the
[Yes, No] answers.
A Pearson correlation test was used to check the correlation between each feeling. We then performed a principal
component analysis (PCA), followed by Hierarchical Clustering on Principal Components (HCPC) in order to assess
which images resulted in higher scores. Following these analyses, the incitation question was excluded from the next
statistical tests (see results) and a mean score was calculated for each participant, combining all four remaining questions.
We assessed whether this score is influenced by the age, gender, geographical zone of their city or the city population size
(categorized according to quartiles). A general linear model (glm) with a normal law was used with the R package
MultCompfor multiple comparisons (Bretz et al., 2016). A separate GLM tested the interactions between age/sex
factors and the expression of eye images (i.e., neutral, angry, friendly). The conditions of application (normality and
homoscedasticity of residuals) were graphically verified.
The 20 PBQ questions with values from 1 to 6 were analyzed using a PCA with a varimax rotation (Package R Psych;
Revelle, 2011;Revelle & Revelle, 2015), following the procedure explained in Granié et al. (2011,2013). In order to fit
with the PCA axes of these studies, we set a maximum number of four loadings (as a preliminary analysis of five PCA
dimensions shows a division of the positive behaviors in dimensions 4 and 5, we combined both dimensions as described
by Granié et al., 2011,2013). The coordinates of participants in each dimension (five with loadings higher than 1.00) were
then compared with the eye images mean score using a Pearson correlation test. GLM analysis was also carried out to test
coordinates with gender, age and city data of participants using. The four dimensions were scaled and normalized using
the scale function in R. For the zonevariable, an Anova followed by a Tukey posthoc test was performed on the GLM
residuals.
All tests were carried out with R 3.6 (R Development Core Team, 2009). The significance level was set at 0.05. Results are
indicated with mean stdv.
3. Results
3.1 What do participants feel when seeing the eye images?
Whatever the eye image, the mean score for the Observationquestion is 0.33 0.14 (meaning that 33% of participants
answered Yesto this question and therefore feel observed). The mean score for the Fearquestion is 0.05 0.07.
The mean score for the Inhibitionquestion is 0.10 0.08. The mean score for the Surprisequestion is 0.26 0.12.
Finally, the mean score for the Incitationquestion is 0.02 0.01. Scores for each image and each question are provided
in Table 1 and are shown in Figure 1.
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Pearsons correlation tests between scores (Figure 2) showed a high correlation between all feelings (r > 0.8, p < 0.001)
excluding incitation (r < 0.39, p > 0.05). Indeed, a PCA showed that dimension 1 was composed of four feelings
(Observation, Fear, Inhibition and Surprise, r > 0.85see https://doi.org/10.5281/zenodo.5745446 for details) and
explained 72.73% of variance in the scores, whilst dimension 2, which was solely composed of the Incitation feeling
(r = 0.89), explained 19.28% of variance. These results validate our questionnaire with participants answering coherently
following our predictions. A HCPC following this PCA produced five clusters, without including picture 2 (Childs
friendly eyes). When the incitation question was removed from analyses, dimension 1 (r > 0.88) explained 87.06% of
score variance whilst dimension 2 (r < 0.45) explained only 7.28%. The resulting HCPC produced four clusters, with
picture 2 regrouped with another cluster. In view of these results and the aims of our study, we decided to discard the
incitation question for the following analyses. The images were then grouped into four clusters. The first cluster includes
the flower, and a man and a woman with neutral expressions. It has a lower influence on the scores (see green elements
in Table 1; participants do not feel surprised, scared, observed or inhibited). The fourth and last cluster (see red elements
in Table 1) is composed of the child and a man with an angry expression. This cluster shows the highest scores
(i.e. participants felt observed, scared, surprised and inhibited). Gender is therefore equally distributed in these four
clusters, but the picture of a childs eyes has a strong effect on feelings (ranked 10, 14 and 15 on the 16 images, Table 1).
Moreover, whilst the first cluster includes only neutralimages and the second cluster includes friendlyand neutral
images, all the angry images are found in the third and fourth clusters, meaning that these expressions lead to stronger
feelings.
3.2 How did the influence of eye images on the feelings of participants vary according to their
sociodemographic factors?
The mean score for each participant was influenced by gender and age (Figure 3) but not by the geographical zone or the
city population size (see Table 2 for statistical values). Men have a lower score than women, meaning that they feel less
scared or observed by eyes. Age negatively influences the score, meaning that older participants have a lower score and
feel less observed or scared than youngers. When we tested the effect on the mean score of the interactions of age and sex
with the eye expressions (i.e., neutral, angry or friendly), we did not found any interaction between age and eye expression
(|t-value| < 1.086, p > 0.277) or between sex and eye expression (|t-value|< 1.449, p > 0.147).
3.3 Answers of participants to the pedestrian behavior questionnaire
The 20 PBQ questions were analyzed in the same way as those in referenced studies (Deb et al., 2017;Granié et al., 2013;
Tom and Granié, 2011). Results are in accordance with the precited studies (see https://doi.org/10.5281/zenodo.5745446
Table 1. Mean score for each eye image (except the flower control image) and each feeling-related question.
Images are ranked according to their values in dimension 1 of the PCA, without the incitation variable. Colors
indicate the clusters assessed by the HCPC, from the least intense (green) to the most intense (red) feeling.
Category Facial
expression
Observation Fear Inhibition Surprise Incitation PCA Dim1
Coord
Flower - 0.00 0.00 0.04 0.14 0.04 -2.45
Man Neutral 0.13 0.00 0.00 0.14 0.02 -2.25
Woman Neutral 0.19 0.00 0.04 0.13 0.02 -1.80
Woman Neutral 0.31 0.01 0.02 0.14 0.01 -1.41
Man Neutral 0.29 0.01 0.03 0.17 0.00 -1.37
Man Friendly 0.26 0.02 0.02 0.22 0.04 -1.29
Man Friendly 0.27 0.01 0.08 0.22 0.02 -0.95
Woman Friendly 0.34 0.03 0.06 0.21 0.03 -0.72
Woman Friendly 0.41 0.04 0.07 0.24 0.02 -0.25
Child Neutral 0.38 0.01 0.12 0.25 0.02 -0.10
Woman Angry 0.42 0.07 0.14 0.31 0.03 0.75
Woman Angry 0.46 0.07 0.13 0.36 0.04 1.01
Man Angry 0.43 0.09 0.18 0.37 0.00 1.41
Child Friendly 0.46 0.06 0.25 0.51 0.09 2.24
Child Angry 0.53 0.22 0.25 0.47 0.03 3.52
Man Angry 0.49 0.21 0.25 0.55 0.04 3.66
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for details). The results explain 49.7% of total variance. The Kaiser-Meyer-Olkin measure of sampling adequacy was
satisfactory (0.80) and the Bartletts test of sphericity was significant (p < 0.0001). The first dimension explains 15.7%
of variance and corresponds to lapses(following a loading above 0.4). The second dimension explained 14.7% of
variance and corresponds to transgressions, meaning violations and errors. The third dimension explains 11.2%
of variance and corresponds to aggressive behaviors. Finally, the fourth dimension explains 8.2% of variance and
corresponds to positive behaviors.
Figure 2. Chart of correlations between the feelings scores. Values indicate the Pearson correlation. Stars
indicate statistical significance (Absence = p > 0.05; *** = p < 0.001).
Figure 3. Influence of gender (a.) and age (b.) on the mean score of the feelings of participants on seeing the
eye images.
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3.4 How did the PBS axes influence participants according to their sociodemographic factors?
All statistical results are indicated in Table 3. Posthoc Tukey multiple comparisons are detailed in https://doi.org/10.5281/
zenodo.5745446. The lapsesdimension is only influenced by the city population size, with a larger city population size
leading to higher occurrence of unintentional deviation from rules. The transgressionsdimension (violations and
errors) is influenced by age, with younger people making more transgressions, and also by geographical zones, with a
higher transgressions score for the Sud(South) Zone than the Est(East) area (however, see the Tukey test for further
details). The aggressive behaviorsaxis is influenced only by the zone but the posthoc test revealed no differences.
Finally, the positive behaviors dimension is influenced by gender, with women showing more positive behavior than
men do.
Table 2. Statistical values for the general linear model with the mean score of the eye images questionnaire.
HDF for Hauts-de-France, IDF for Ile-de-France.
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.4071361 0.0306555 13.281 <0.001
Gender [Male] -0.050761 0.0164812 -3.08 0.0198
Age -0.0022274 0.0005603 -3.975 <0.001
ZoneHDF 0.0089592 0.0461934 0.194 1
ZoneIDF 0.0398962 0.0221828 1.799 0.4841
ZoneOuest 0.0538668 0.023243 2.318 0.172
ZoneSud 0.0259914 0.02306 1.127 0.9272
ZoneSudEst -0.0156868 0.0246333 -0.637 0.9986
ZoneSudOuest -0.0018212 0.0449061 -0.041 1
Population size -0.0030641 0.0066881 -0.458 0.9999
Table 3. Statistical values of the general linear models concerning the PBQ dimensions. HDF for Hauts-de-
France, IDF for Ile-de-France. QuartilePop indicates size of towns populations as quartiles.
Estimate Std. Error t value Pr(>|t|)
Lapses dim.
(Intercept) 0.9604247 0.027685 34.691 <2e-16
Gender [Male] -0.0289539 0.0177072 -1.635 0.1025
Age -0.0002319 0.0003504 -0.662 0.5083
ZoneHDF -0.0567546 0.0495352 -1.146 0.2524
ZoneIDF -0.0104238 0.0238461 -0.437 0.6622
ZoneOuest 0.0060215 0.0249557 0.241 0.8094
ZoneSud 0.0128254 0.0247557 0.518 0.6046
ZoneSudEst -0.0014063 0.0264376 -0.053 0.9576
ZoneSudOuest 0.0219626 0.0481632 0.456 0.6486
QuartilePop 0.0142764 0.0071656 1.992 0.0468
Trangressions dim.
(Intercept) 0.9636292 0.0273093 35.286 <2e-16
Gender [Male] 0.0299833 0.0174669 1.717 0.08657
Age -0.0008977 0.0003456 -2.597 0.00962
ZoneHDF 0.0116038 0.048863 0.237 0.81237
ZoneIDF -0.0023133 0.0235225 -0.098 0.92169
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F1000Research 2022, 11:218 Last updated: 23 FEB 2022
3.5 Link between feelings on seeing the eye images and PBQ axes
We did not identify any statistical correlation between the eye image questionnaire score and the four PBQ dimensions
(Figure 4). Lapses dimension: r= 0.05, t = 1.2843, df = 608, p-value = 0.1995; Transgressions dimension: cor = 0.02,
t = 0.47498, df = 608, p-value = 0.635, Aggressive behaviors dimension: r = -0.07, t = -1.8156, df = 608, p-value =
0.06993; Positive behaviors dimension: r = 0.05, t = 1.129, df = 608, p-value = 0.2593.
4. Discussion
This study tested the potential impact of different eye images friendly, neutral, and angry and one image of flowers as a
control on pedestrians crossing at the red light. In their responses to an online questionnaire about their feelings on seeing
these images, participants revealed that they initially felt observed (about 33%), then surprised (26%), then inhibited to
cross at the red signal (10%). Finally, few were scared (5%) or felt encouraged to cross at the red signal (2%). Eye images
encourage cooperative behavior because unlike pictures of flowers, they make participants feel like they are being
watched (Pfattheicher and Keller, 2015). Our results are in line with this explanation, as flowers obtained a null score for
being observed or scared. Moreover, the expression conveyed by the eyes also affects participants, as feelings of being
observed increased by about 10-15% whilst feelings of being scared or reluctant to cross the road increased by about 5%
as the expression changed from neutral to friendly to angry. This study confirms that humans react to faces but rates of
negative reactions to the eyes are low, indicated a mixed effect. Indeed, we expected more participants to answer that
they feel observed, afraid or surprised by the eyes images. Previous studies have shown that this reaction can play a role
Table 3. Continued
Estimate Std. Error t value Pr(>|t|)
ZoneOuest -0.0323359 0.0246171 -1.314 0.1895
ZoneSud 0.0659362 0.0244198 2.7 0.00713
ZoneSudEst 0.0156855 0.0260788 0.601 0.54776
ZoneSudouest -0.0083361 0.0475096 -0.175 0.86078
QuartilePop 0.0131719 0.0070683 1.864 0.06288
Aggressive behav. dim.
(Intercept) 9.47E-01 2.77E-02 34.149 <2e-16
Gender [Male] 2.26E-02 1.77E-02 1.273 0.2037
Age 9.01E-05 3.51E-04 0.257 0.7976
ZoneHDF 3.28E-02 4.96E-02 0.661 0.5087
ZoneIDF 1.32E-02 2.39E-02 0.552 0.581
ZoneOuest 1.88E-02 2.50E-02 0.752 0.4521
ZoneSud 5.82E-02 2.48E-02 2.345 0.0194
ZoneSudEst 2.34E-02 2.65E-02 0.881 0.3785
ZoneSudOuest 3.34E-02 4.83E-02 0.693 0.4888
QuartilePop 1.69E-03 7.18E-03 0.235 0.814
Positive behav. dim.
(Intercept) 1.00474 0.0276472 36.341 <2e-16
Gender [Male] -0.0399417 0.017683 -2.259 0.0243
Age -0.0005901 0.0003499 -1.686 0.0922
ZoneHDF 0.0104337 0.0494676 0.211 0.833
ZoneIDF 0.015133 0.0238136 0.635 0.5254
ZoneOuest 0.0030133 0.0249217 0.121 0.9038
ZoneSud 0.0129851 0.0247219 0.525 0.5996
ZoneSudEst -0.0044673 0.0264015 -0.169 0.8657
ZoneSudOuest 0.057924 0.0480975 1.204 0.2289
QuartilePop 0.0006763 0.0071558 0.095 0.9247
Page 11 of 16
F1000Research 2022, 11:218 Last updated: 23 FEB 2022
in maintaining the cooperative behaviors that are essential to life in societies (Alexander, 1987;Bateson et al., 2006;
Burnham and Johnson, 2005) and it is interesting to put in light our results with these studies.
As predicted by our hypothesis, we found that the gender and age of participants affected their feelings when looking at
the eye images. Women were more affected by the images than men were, and younger participants also reacted more
than older individuals. Few studies have analyzed the effect of these two variables on the reaction to eye images, maybe
because of anonymity in questionnaires or the low number of participants. Two studies report that gender did not
influence reactions to eye images (Rodriguez Mosquera et al., 2011;Sparks and Barclay, 2013). However, it is not
surprising to find that women felt more observed or scared than men when looking at the eye images, as studies showed
women to be more empathic and therefore more receptive in different situations (Flynn, 2000;Furnham et al., 2003;
Kellert & Berry, 1987;Miller et al., 2009). In the context of road crossing, women show also fewer illegal andrisky
behaviors than men (Holland and Hill, 2007;Pelé et al., 2017;Sueur et al., 2013;Tom and Granié, 2011). Women also
show more positive behaviors, as shown in our study, and fewer violations or aggressive behaviors than men do (Deb
et al., 2017;Tom and Granié, 2011).
Although we expected the effect of eye images on feelings to increase with age, the contrary was observed. To our
knowledge, this is the first study on the effect of eye images to date that has reported such a result. However, studies of
Figure 4. Relation between the eye image feeling score and the four dimensions of the pedestrian behavior
questionnaire (PBQ).
Page 12 of 16
F1000Research 2022, 11:218 Last updated: 23 FEB 2022
pedestrian
behaviors
showed
younger
people
to
be
less
respectful
of
rules
(Holland
and
Hill,
2007;
Pelé
et
al.,
2017;
Pfeffer
and
Hunter,
2013).
This
is
confirmed
in
our
study,
which
shows
more
violations
by
younger
participants
than
by
their
older
counterparts.
Elder
pedestrians
can
also
display
some
illegal
behaviors
but
these
are
generally
due
to
cognitive
or
physical
disorders
(Laurin
et
al.,
2001;
Yaffe
et
al.,
2001).
Contrary
to
past
studies
using
PBQ
(Deb
et
al.,
2017;
Granié
et
al.,
2013),
we
did
not
find
an
age
effect
on
each
pedestrian
behavior
axis.
Our
results
show
an
effect
of
the
city
population
size
on
the
lapses
dimension,
meaning
that
citizens
living
in
big
cities
show
more
unintentional
violations
and/or
are
more
distracted
than
the
inhabitants
of
small
cities,
probably
because
of
the
density
of
the
population
or
visual
distractions
such
as
shops,
signs
or
public
transport.
Participants
from
the
South
of
France
seem
to
display
more
violations
and
aggressive
behaviors
than
those
from
the
rest
of
France.
No
link
was
found
between
the
results
of
our
questionnaire
and
those
of
the
Pedestrian
Behavior
Questionnaire
(PBQ).
We
expected
participants
showing
high
scores
for
violations
and
aggressive
behaviors
in
the
PBQ
(or
low
scores
for
positive
behaviors)
to
feel
less
observed
or
scared
by
the
eye
images.
However,
no
correlation
was
found.
This
can
be
explained
by
a
number
of
reasons.
A
participant
may
feel
concerned
by
the
eye
image
but
will
behave
aggressively
towards
a
driver
because
as
a
pedestrian,
s/he
considers
that
the
driver
is
wrong
(and
thus
seeks
to
communicate
this
anger).
Alternatively,
a
participant
may
not
feel
concerned
by
the
eyes
image
because
irrespective
of
who
is
watching
him/her,
s/he
will
always
behave
well
and
consider
the
behavior
of
the
driver
-
if
the
latter
also
behaves
well
-
as
reciprocal
(Burnham
and
Johnson,
2005;
Fehr
and
Fischbacher,
2004).
This
absence
of
correlation
may
be
due
to
differences
in
the
empathic
profiles
of
the
population,
ranging
from
people
who
are
naturally
cooperative
and/or
react
to
eye
images
in
order
to
ensure
they
are
seen
in
a
positive
light,
to
those
who
are
not
cooperative
at
all
and
are
more
likely
to
react
to
punishment
(Boyd
and
Richerson,
1992;
Fehr
and
Gachter,
2000).
Although
we
expected
people
who
followed
the
rules
to
be
non-
violent,
the
two
traits
may
not
be
correlated.
In
other
words,
a
person
may
react
to
eye
images
and
be
cooperative
or
follow
the
rules
but
be
aggressive
towards
people
who
do
not
do
likewise.
Conversely,
another
person
may
be
indifferent
to
others,
and
will
therefore
not
react
to
eye
images
or
behave
aggressively
towards
people
who
do
not
respect
the
rules.
A
second
explanation
is
that
the
questionnaire
is
based
on
a
virtual
situation
that
did
not
affect
participants
feelings
in
the
same
way
as
the
real
situation,
thus
decreasing
the
potential
correlation
between
our
variables
(Francey
and
Bergmüller,
2012).
Past
studies
have
indeed
shown
a
great
variability
of
participants
responding
to
eye
images
according
to
the
experimental
setup
that
is
used
(see
for
instance
Fehr
&
Schneider,
2010;
Northover
et
al.,
2017
for
negative
results).
Anonymity
also
has
a
negative
impact
on
the
effect
of
eye
images
(Lamba
and
Mace,
2010),
and
this
could
have
an
impact
in
our
study.
5.
Conclusion
A
better
understanding
of
human
cooperative
behavior
in
real
life
is
of
key
interest
for
social
management,
from
both
theoretical
and
practical
perspectives.
The
present
study
shows
that
the
use
of
eye
images
could
help
to
reduce
illegal
crossings
by
pedestrians.
However,
this
effect
is
limited
as
not
a
majority
of
participants
answered
that
they
felt
observed,
afraid
and
so
on.
Drivers
react
to
the
smiles
and
gaze
of
pedestrians
by
permitting
them
to
cross
a
road
(Guéguen
et
al.,
2015,
2016).
Pedestrians
behaving
in
this
way
could
also
be
more
cooperative.
The
mechanisms
involved
in
maintaining
a
good
reputation
can
also
produce
investments
to
serve
the
common
good
(Bshary
and
Bshary,
2010;
Francey
and
Bergmüller,
2012).
Our
findings
are
of
practical
interest
for
those
designing
honesty-based
systems,
or
wishing
to
maximize
contributions
to
public
commodities
and
services.
In
a
meta-analysis
of
15
experiments
from
13
research
papers
(Dear
et
al.,
2019),
found
a
35%
reduction
in
the
risk
of
antisocial
behavior
when
eye
images
are
present.
In
contrast,
systematic
reviews
have
suggested
that
CCTV
cameras
reduce
crime
by
only
16%.
Settling
such
eyes
images
nudges
on
pedestrian
signals
could
have
an
effect,
even
a
small
one,
but
this
could
be
enough
to
decrease
significantly
accidents,
particularly
considering
the
group
effect
of
crossings
(Pelé
et
al.,
2017).
However,
how
such
effect
persist
in
time
as
pedestrians
could
get
habituated
to
the
eyes
as
reprimand
is
absent.
However,
our
study
is
based
on
a
questionnaire
and
this
nudge
needs
to
be
tested
in
real
situations.
We
encourage
authorities
to
adopt
the
use
of
eye
image
systems
in
crossing
signals
in
order
to
decrease
the
number
of
illegal
crossings
and
increase
pedestrian
safety.
Field
research
as
well
as
more
ecologically
valid
situations
must
be
added
to
laboratory-based
studies
to
show
the
real
effect
of
these
eye
images
on
human
cooperative
behaviors.
Data
availability
Zenodo.
Dataset
for
Eye
image
effect
in
the
context
of
pedestrian
safety:
a
French
questionnaire
study.
DOI:
https://doi.
org/10.5281/zenodo.5745446
Page 13 of 16
F1000Research 2022, 11:218 Last updated: 23 FEB 2022
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Followership is generally defined as a strategy that evolved to solve social coordination problems, and particularly those involved in group movement. Followership behavior is particularly interesting in the context of road-crossing behavior because it involves other principles such as risk-taking and evaluating the value of social information. This study sought to identify the cognitive mechanisms underlying decision-making by pedestrians who follow another person across the road at the green or at the red light in two different countries (France and Japan). We used agent-based modelling to simulate the road-crossing behaviors of pedestrians. This study showed that modelling is a reliable means to test different hypotheses and find the processes underlying decision-making when crossing the road. We found that two processes suffice to simulate pedestrian behaviors: personal motivation and imitation. Importantly, the study revealed differences between the two nationalities and between sexes in the decision to follow and cross at the green and at the red light. Japanese pedestrians showed a greater mimetic behavior at the red light but the process takes into account both the number of crossing and waiting pedestrians, contrary to French citizens. Finally, the simulations are revealed to be similar to observations, not only for the departure latencies but also for the number of crossing pedestrians and the rates of illegal crossings. The conclusion suggests new solutions for safety in transportation research.
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Pedestrians are ideal subjects for the study of decision-making, due to the inter-individual variation in risk taking. Many studies have attempted to understand which environmental factors influence the number of times pedestrians broke the rules at road-crossings, very few focused on the decision-making process of pedestrians according to the different conditions of these variables, that is to say their perception and interpretation of the information they receive. We used survival analyses and modeling to highlight the decision-making process of pedestrians crossing the road at signalized crossings in France and in Japan. For the first pedestrians to step off the kerb, we showed that the probability to cross the road follows three different processes: one at the red signal, one just before the pedestrian signal turns green, and one after the signal has turned green. Globally, the decision of the first pedestrian to cross, whether he or she does so at the green or at the red signal, is influenced by their country of residence. We identify the use of cognitive processes such as risk sensitivity and temporal discounting, and propose new concepts based on the results of this study to decrease the incidence of rule-breaking by pedestrians.
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Eye cues have been shown to stimulate rapid, reflexive, unconscious processing and in many experimental settings to cue increased prosocial and decreased antisocial behaviour. Eye cues are being widely applied in public policy to reduce crime and antisocial behaviour. Recently, failed replication attempts and two meta-analyses examining the eye cue effect on generosity have raised doubts regarding earlier findings. Much of the wider evidence on eye cues has still not been systematically reviewed, notably that which is most relevant to its practical application: the effect of eye cues on antisocial behaviour. Given the evidence of humans' heightened sensitivity to threat and negative information, we hypothesized that the watching eyes effect would be more consistent in studies examining antisocial behaviour. In our meta-analysis of 15 experiments from 13 research papers we report a reduction in the risk of antisocial behaviour of 35% when eye cues are present. By contrast, systematic reviews have suggested CCTV cameras reduce crime by only 16%. We conclude that there is sufficient evidence of a watching eyes effect on antisocial behaviour to justify their use in the very low-cost and potentially high-impact real-world interventions that are proliferating in public policy, particularly in the UK. Public significance statement Our meta-analysis of 15 experiments involving 2035 participants shows that photographs and/or stylized images of eyes reduced antisocial behaviour by 35%. Our findings support public policy initiatives employing pictures of ‘watching eyes’ to reduce crime. Furthermore, in an age when we are watched more than at any time in modern history – both online and on the street – our findings highlight an urgent need to fully understand the effect that perceived surveillance, feeling watched, has on our decisions and actions.
Article
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Article
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Given many known and unknown uncertainties, it is hard to forecast reliably the mode choices, expected to prevail with autonomous vehicle (AV) technology; however, the key to getting some idea lies in understanding the preferences of end users. In this vein, a random parameters logit model is employed to study the consumers’ preferences in small- and medium-sized metropolitan areas, based on their travel behavior and household characteristics, socio-demographic features, awareness about AV technology and new travel choices, psychological factors, and built environment features. Most of the past studies hypothesize that due to a wide range of anticipated benefits, there would be increased use of AVs especially as a shared service where multiple travelers use the same AV concomitantly. However, the results from this study do not support the hypothesis that vehicle ownership will be an obsolete model at least during the early phase of transitioning to the self-driving era (when roads are expected to contain vehicles with and without human drivers). The findings of this study reveal key factors influencing consumer preferences and offer important insights to technology developers and service providers in understanding the ways consumers would like to use this technology and hence, defining the business model.
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The aim of this study was to develop and validate a self-reporting Pedestrian Behavior Questionnaire (PBQ) for the U.S. population to measure frequency of risky behaviors among pedestrians. The PBQ includes 50 survey items that allow respondents to rate the frequency with which they engage in different types of road-using behaviors as pedestrians. The validation study was conducted on 425 participants (228 males and 197 females) between the ages of 18 and 71. Confirmatory factor analysis differentiated pedestrian behaviors into five factor categories: violations, errors, lapses, aggressive behaviors, and positive behaviors. A short version of the PBQ with 20 items was also created by selecting four items with high factor loadings from each of the five factor categories. Regression analyses investigated associations with scenario-based survey behavioral responses to validate the five-factor PBQ subscale scores and composite score. For both long and short versions, each of these five individual factor scales were found to be reliable (0.7 < Cronbach’s alpha (α) < 0.9) and valid (significant association with p < 0.0001), except in the case of positive behaviors (α < 0.6) which requires further expansion. The effects of gender and age on the PBQ scores were investigated and found to be consistent with previous research. This PBQ can serve as an instrument of pedestrian self-assessment in educational and training contexts as well as can be useful to all researchers investigating pedestrian safety for all age groups.
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Many studies have seemingly demonstrated that anonymous individuals who are shown artificial cues of being watched behave as if they are being watched by real people. However, several studies have failed to replicate this surveillance cue effect. In light of these mixed results, we conducted two meta-analyses investigating the effect of artificial observation cues on generosity. Overall, our meta-analyses found no evidence to support the claim that artificial surveillance cues increase generosity, either by increasing how generous individuals are, or by increasing the probability that individuals will show any generosity at all. Therefore, surveillance cue effects should be interpreted cautiously.