Towards understanding child-pedestrians’ hazard perception abilities in a mixed reality dynamic...

Transportation Research Part F 20 (2013) 90–107

Contents lists available at SciVerse ScienceDirect

Transportation Research Part F

journal homepage: www.elsevier .com/locate / t r f

Towards understanding child-pedestrians’ hazard perceptionabilities in a mixed reality dynamic environment

1369-8478/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.trf.2013.05.004

⇑ Corresponding author. Tel.: +972 54 7480955; fax: +972 8 647 2958.E-mail addresses: [email protected] (A. Meir), [email protected] (Y. Parmet), [email protected] (T. Oron-Gilad).

Anat Meir ⇑, Yisrael Parmet, Tal Oron-GiladDept. of Industrial Engineering & Management, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva 84105, Israel

a r t i c l e i n f o a b s t r a c t

Article history:Received 9 August 2012Received in revised form 5 May 2013Accepted 22 May 2013

Keywords:Mixed reality dynamic environmentDome-settingsHazard-perceptionChild-pedestriansCrossing skills

Child-pedestrians, especially those in the age range of 5–9-years, are amongst the mostvulnerable road users. These youngsters are highly represented in fatal and severe injuryroad crashes, despite relatively low levels of exposure to traffic. The present research inves-tigated child and adult pedestrians’ perception of hazards utilizing a crossing decision task.Twenty-one adults (20–27 years-old) and twenty-five young-children (eight 7–9-year-olds, five 9–10-year-olds and twelve 10–13-year-olds) were requested to observe traffic-scene scenarios presented in a mixed reality dynamic environment simulating a typicalIsraeli city from a pedestrian’s perspective, and to press a response button whenever theyassumed it was safe to cross. Results have shown that as pedestrians’ age and experience-level increased their attentiveness towards potential hazards increases and their ability toanticipate upcoming events while engaging in a road-crossing task was enhanced. Further-more, both the 9–10-year-olds and the 10–13-year-olds presented a less decisive perfor-mance compared to both the experienced-adult pedestrians and the 7–9-year-olds.Understanding child-pedestrians’ shortcomings in evaluating traffic situations may con-tribute to the effort of producing intervention techniques which may increase their atten-tiveness towards potential hazards and pave the way for reducing their over-involvementin road crashes. Implications for training novice road users will be discussed.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Developing countermeasures for improving the safety of street crossing among child-pedestrians is a crucial problem inthe road traffic domain. Research has shown that child-pedestrians, especially those in the age range of 5–9-years, are highlyrepresented in fatal and severe injury traffic crashes (e.g., National Highway Traffic Safety Administration., 2008; Tabibi &Pfeffer, 2003; Whitebread & Neilson, 2000), in spite of relatively low levels of exposure to traffic (Thomson et al., 2005). Sincea general agreement among worldwide traffic safety professionals states that children under the age of 9 or 10 should notcross roads alone (e.g., Percer, 2009), one might have argued that prohibiting children’s from crossing the road alone until theage of 9 may suffice in order to reduce their over-involvement in pedestrian crashes. Yet, research has shown that elemen-tary-school children do tend to cross the road unaccompanied by adults, mainly on their way to and from school (e.g., Mac-pherson, Roberts & Pless, 1998; Martin, Lee, & Lowry, 2007; McDonald, 2008; van der Molen, 1981). For example, McDonald(2008) had shown that 48% of elementary and middle schoolers in the age range of 5–13, living less than 1 mile from theirschool tended to walk there. Moreover, studies have suggested that some parents tend to overestimate their children’sroad-crossing abilities (e.g., MacGregor, Smiley & Dunk, 1999; Gielen et al., 2004). For example, interviewing well-educatedmidsize, urban community’s Canadian parents have shown that some parents consider children as young as 7.6 years as old

http://crossmark.dyndns.org/dialog/?doi=10.1016/j.trf.2013.05.004&domain=pdf

http://dx.doi.org/10.1016/j.trf.2013.05.004

mailto:[email protected]



http://dx.doi.org/10.1016/j.trf.2013.05.004

http://www.sciencedirect.com/science/journal/13698478

http://www.elsevier.com/locate/trf

A. Meir et al. / Transportation Research Part F 20 (2013) 90–107 91

enough to cross a quiet residential street corner without any traffic controls or stop signs accompanied by their same-agefriends, and children as young as 7.9 years as old enough to cross a road by themselves (MacGregor et al., 1999). Gielenet al. (2004) found, through a survey of 732 elementary school parents in four urban USA neighborhoods, that while parentstended to report that they teach their children pedestrian safety skills, 30% of them stated that they let their child, who isyounger than 10 years of age, walk to school alone and 47% of them stated that they did not supervise their children whenplaying outdoors. Indeed, according to Morrongiello and Barton (2009), parents tend not to spend significant amount of timeteaching their own children safe pedestrian skills.

Evidently, in the effort to reduce traffic crashes among child-pedestrians it is insufficient to assume that youngsters willavoid crossing roads unaccompanied; rather, there is a pressing need for exploring and assessing the skills and knowledgerequired for children to deal safely with the traffic environment, so as to provide them with means for enhancing these capa-bilities (Hill, Lewis, & Dunbar, 2000).

1.1. Pedestrians abilities

Studies have indicated that young children tend to suffer from poor pedestrian skills and visual search strategies, as wellas other perceptual and cognitive deficiencies interfering with their ability to safely interact with the traffic environment(e.g., Ampofo-Boateng & Thomson, 1991; Barton & Morrongiello, 2011; Tolmie, Thomson, Foot, McLaren, & Whelan, 1998;Whitebread & Neilson, 2000). Safe street-crossing requires the development of a variety of complex perceptual and cognitiveskills (e.g., Egan, 2012; Schwebel & McClure, 2010). The literature have noted several underlying abilities pedestrians shouldrequire in order to safely interact with traffic: (1) Evaluating whether a specific crossing place should be defined as ‘safe’ or‘dangerous’, (2) Identifying the presence of traffic which could serve as a source of danger, and (3) Integrating informationfrom different elements and directions of the traffic environment into a holistic appreciation of the situation (Thomson, Tol-mie, Foot, & McLaren, 1996; Whitebread & Neilson, 2000). Reviewing the three abilities suggested by Thomson et al. (1996)as necessary for inducing safe road behavior, it may be argued that the common denominator between them is the fact thatthey are all hazard-perception-related. Hazard perception may be described as the process of evaluating the hazardousnessof a traffic situation (Benda & Hoyos, 1983). It may also be defined as the ability to ‘read the road’ and anticipate forthcomingevents; a situation awareness for hazardous situations in the traffic environment (Horswill & McKenna, 2004). Situationawareness (SA) was termed as a state of knowledge which enables holistic perception of the environment (Endsley,1995). Attaining SA involves a three-stage process which includes the perception of elements in the environment, compre-hension of their integrated meaning to create a holistic appreciation of the present situation and projection of their status inorder to predict near future events (Endsley, 1995).

1.2. Child-pedestrians abilities

The first ability required for pedestrians in order to interact safely with traffic (Thomson et al. 1996) is the process of eval-uating whether a specific crossing place should be defined as ‘safe’ or ‘dangerous’. This process, which may be viewed as cor-responding to Endsley’s (1995) SA perception stage, requires the capacity to detect potential hazards that are not physicallyand momentarily present in the environment. It was argued that the ability to identify safe and dangerous road-crossingsites tends to increase with age (e.g., Tabibi & Pfeffer, 2003). Earlier, Ampofo-Boateng and Thomson (1991) have shown,using both on-road and off-road experiments, that 5–7 year-olds demonstrate poor skill in identifying dangerous road-cross-ing sites, where their judgments relies almost exclusively on the visible presence of cars in the vicinity. While other trafficelements such as complex junctions, blind summits, and obscuring obstacles were not recognized by these youngsters asthreatening. Nine-year-olds displayed a higher level of evaluation skills and 11-year-olds demonstrated a rather good abilitymaking these judgments correctly (Ampofo-Boateng & Thomson, 1991).

The second ability which was declared as critical for safe road behavior by Thomson et al. (1996) is the ability to identifythe presence of traffic which could serve as a source of danger. This process, which may be viewed as corresponding to Ends-ley’s (1995) SA comprehension stage, is mainly based on the link between traffic features and the specific environment inwhich they are most likely to appear. Research has shown that the ability to resist interference from irrelevant stimuli tendsto increase with age (e.g., Tabibi & Pfeffer, 2003). For example, 4–9-year-olds were shown to have difficulty focusing on thefeatures that made a road-crossing situation dangerous; i.e., paying attention to relevant information while ignoring irrele-vant information (Hill et al., 2000). Similarly, Tolmie et al. (1998) presented traffic scenarios to 5, 7, 9, 11 year-olds and adultsin three different settings-computer simulation, video technique, and roadside. In all contexts, older children were muchmore attuned to traffic-relevant features than younger children. The authors concluded that young children encounter dif-ficulties in differentiating between the relevant and irrelevant features to road crossing thus may experience difficulties inprioritizing their performance according to task requirements.

Finally, the third ability required for safe crossing (Thomson et al., 1996) is the ability of integrating information such asroad elements and directions of the traffic into a holistic appreciation of the situation. This process may be viewed as cor-responding to Endsley’s (1995) SA projection stage. Underwood, Dillon, Farnsworth, and Twiner (2007) asked 7–8, 9–10 and11–12 year olds to categorize photos displaying traffic scenes according to their own self-selected criteria and to re-catego-rize the same photos according to the overall safety-level displayed in each scene. Since age differences were evident in theformer but not in the latter task, it was suggested that the younger children may be highly influenced by cueing. The authors

92 A. Meir et al. / Transportation Research Part F 20 (2013) 90–107

concluded that younger children were more inclined towards displaying a specific, idiosyncratic perspective than older chil-dren, who were more inclined towards observing the road from a holistic, integrated perspective. Similarly, Whitebread andNeilson (2000) studied the development of visual search strategies among child-pedestrians utilizing intersection movie pre-sentations and photographs. While 4–5-year-olds demonstrated difficulties in identifying safe places to cross, a major shiftin the ability to effectively apply visual information search skills took place around the age of 7–8, where participants suc-cessfully identified safe places. Another key development was found at the strategic level of information processing aroundthe age of 10–11, where pedestrians tended to look towards a center screen (where a side road merged into the main road)much more often than other younger groups and for shorter durations, suggesting that their ability to infer traffic emergesquickly from that direction.

1.3. Child-pedestrians similarities to young-novice drivers

Another novice population whose high traffic crash rate was suggested as correlated with its members’ deficient hazardperception abilities and lack of driving experience (e.g., Horswill & McKenna, 2004) is the young-novice drivers’ population.Notably, this population’s impairments bear high resemblance to those previously described regarding child-pedestrians. In-deed, research had indicated that young-novice drivers’ encounter difficulties in identifying potential hazards (e.g., Armsby,Boyle, & Wright, 1989; Borowsky, Shinar, & Oron-Gilad, 2010). For example, photographs displaying pedestrians’ road-cross-ing (a salient hazard) were rated as being more hazardous among novice drivers than among experienced drivers, while pho-tographs displaying fog situations (a potential hazard) were rated as less hazardous among novice drivers than amongexperienced drivers (Armsby et al., 1989). Thus, as was the case of child-pedestrians (e.g., Ampofo-Boateng & Thomson,1991), it seems that young-novice drivers’ lack of experience limits their attentiveness towards potential hazards, allowingthem to attend to hazards only when salient and physically present in the environment.

Moreover, the resemblance between child-pedestrians and novice drivers with respect to their ability to pay attention torelevant information while ignoring irrelevant information should be acknowledged. Benda and Hoyos (1983) for example,found that novice drivers attended to unimportant details and therefore were unable to extract important road-related infor-mation. This finding resembles Hill et al.’s (2000), which have showed that 4–9-year-old children have difficulty focusing onthe features which are creating a dangerous road-crossing situation.

Lastly, there are similarities between the two populations with regards to their ability to integrate information into aholistic appreciation of the situation. Borowsky, Oron-Gilad, and Parmet (2009) found that while novice drivers assess traffichazards on the basis of a single characteristic, so that all situations which share a certain characteristic (e.g., pedestrians) areperceived by them as equally dangerous, experienced drivers perceive situations more holistically, on the basis of multiplecharacteristics. Similarly, Borowsky et al. (2010) have shown that novices, as opposed to experienced drivers, rarely fixate onmerging roads when driving a car. As in the case of child-pedestrians (e.g., Whitebread & Neilson, 2000), these young-novicedrivers’ ability of integrating information from different parts of the relevant traffic environment, is poor. Instead, they tendto focus on the most salient factor (Ampofo-Boateng & Thomson, 1991; Demetre & Gaffin, 1994; Foot, Tolmie, Thomson,McLAren, & Whelan, 1999).

1.4. Implications for the present research

The current research utilized a conceptual approach taken from the driving domain (e.g., Borowsky et al., 2010; Meir,Borowsky, Oron-Gilad, Parmet, & Shinar., 2010; ; Meir, Borowsky & Oron-Gilad, in press), towards examining child-pedes-trians’ hazard perception while crossing abilities. Previous research conducted in our lab (e.g., Borowsky et al., 2010) hadsuggested that engaging in a hazard detection task of observing hazard-perception scenarios and pressing a response buttonwhenever a hazard is detected, is a validated tool for differentiating between young-novice and experienced-adult drivers. Asthere is a tight link between young-novice drivers’ poor ability to identify hazards and child-pedestrians’ poor hazard-per-ception road-crossing abilities, it was attempted to apply the similar set of theoretical and methodological tools which wasfound to be highly efficient in evaluating and training young-novice drivers’ hazard perception skills (e.g., Borowsky et al.,2010; Meir et al., 2010; Meir et al., in press), in order to create a basis for developing an efficient tool to explore and later ontoenhance child-pedestrians’ hazard perception skills.

Although one could suggest that extensive practice of road-crossing skills, occurring in real life settings may be effectivein decreasing child-pedestrians’ injury risk, such training methodology may pose serious ethical and practical risks, and re-quire extensive resources (Schwebel, Gaines, & Severson, 2008). Since real life, outdoor traffic environments are unpredict-able, making it difficult to control all relevant variables and to design a training program which will comprise a variety oftraffic situations (e.g., Novak, 2009; Barton & Morrongiello, 2011), it was decided to examine these skills in a standardized,regulated environment without jeopardizing the participants. This was achieved, as in several earlier studies (e.g., Schwebelet al., 2008; Novak, 2009), by creating a systematic manipulation of factors in a virtual environment. Virtual reality (VR) maybe described as a computer or video generated environment, designed to mimic real world situations by providing a userwith a sense of being immersed in a displayed virtual world through realistic graphics, high-quality sound and the abilityto interact with the virtual world (e.g., Reid, 2002; Schwebel et al., 2008; Novak, 2009). Virtual reality offers an alluring alter-native which enables participants to be repeatedly exposed to a variety of realistic hazardous situations without the threat ofenduring injury and without the need for adult supervision (e.g., Schwebel, Davis & O’Neal, 2011; Schwebel et al., 2008).


Moreover, the utilization of virtual environment for training can aid in reproducing identical situations for each of the par-ticipants and controlling confounding variables (e.g., Schwebel et al., 2008; Novak, 2009). Evidence suggests that virtual real-ity environment might be utilized as an appropriate methodology, both for etiological research on the sources for child-pedestrians’ injuries and for intervention research (e.g., Schwebel et al., 2008). Mixed Reality (MR) is a particular subsetof virtual reality -related technologies which involves the merging of real and virtual worlds along a ‘‘virtuality continuum’’,which connects completely real environments to completely virtual ones (Milgram & Kishino, 1994). According to Hughes,Stapleton, Hughes and Smith, (2005), a mixed reality experience takes place when a user is positioned in an interactive set-ting which is either real, containing virtual asset augmentation (augmented reality), or virtual, containing real-world aug-mentation (augmented virtuality). Novak, (2009) suggests that training programs, attempting to enhance child-pedestrians skills, need to enable the generalization of knowledge and the transformation of behaviors learned in the train-ing into real-life situations. Indeed, it was suggested that the blurring of the boundary between real and unreal, made pos-sible with virtual reality and mixed reality, may help to transfer the material learnt into the real world (Carlin, Hoffman &Weghorst, 1997). On the basis of these arguments, it seems that using a Dome projection environment technique, which en-ables accurate, controllable and immersive simulation of diverse crossing environments, will be particularly beneficial inenhancing child-pedestrians’ hazard perception skills. Accordingly, hazard perception-based scenario-array was tailoredon the basis of the experience gained from training young-novice drivers in hazard perception, and its effectiveness tested.

1.4.1. Definition of hazardous situationsThe literature suggests that there are various environmental factors associated with increased risk of pedestrian collisions

(e.g., Loukaitou-Sideris, Liggett & Sung, 2012; Petch & Henson, 2000). Subsequently, the hazard perception scenarios data-base utilized in the present research was shaped according influential factors found to contribute to the occurrence of pe-destrian crashes and the factors aiming to discriminate between pedestrians at various age and experience levels. Priorresearch had noted that the majority of child-pedestrians are injured on non-arterial roads, particularly in residential areas,near home (e.g., Lawson, 1990; Roberts, Keall, & Frith, 1994; Tester, Rutherford, Wald, & Rutherford, 2004). Research con-ducted in the USA and Canada had indicated of higher pedestrians’ road crashes rates on two-way streets as compared withone-way streets (Zegeer, 1991). However, others found no evident difference in the level of pedestrians’ crash risk betweenone-way and two-way roads with the same cross-section (e.g., Summersgill & Layfield, 1998).

Zebra crossings (of the same kind that was utilized in the present study) were defined by Martin (2006) as ‘‘formal cross-ings where the pedestrian is given legal priority over vehicles without the use of traffic signal controls. . . a zebra crossing ismarked on the carriageway with alternate black and white stripes’’ (p. 29). Ekman (1996) have argued that marked crossingwith no other facilities (e.g., Zebra crossing) possess a high risk for road crashes. Indeed, it was reported that marked crossingwith no other facilities provide pedestrians with a false sense of security (e.g., Ekman, 1996; Koepsel et al., 2002) as they arenot visible to vehicles, and likewise vehicles are not visible to pedestrians. Additionally, misinterpretation of zebra crossing’spurpose has been observed to result in traffic crashes (e.g., van der Molen, 1981; Vinjé, 1981).

Furthermore, evidence has proposed that pedestrians’ traffic crashes occurring in populated regions tend to take place inlocations where the visibility is restricted, e.g., at curves, near stationary vehicles (Ampofo-Boateng & Thomson, 1990).Hence, on-street vehicle parking presents a particular risk for child-pedestrians (Petch & Henson, 2000). It interferes withthese youngsters’ ability to detect approaching vehicles while also narrowing the motor vehicle drivers’ field of view, pre-venting them from noticing child-pedestrian who may be masked by the stationary vehicles parking along the road (e.g.,Aoki & Moore, 1996; Petch & Henson, 2000).

Ampofo-Boateng and Thomson (1991) have noted that the younger child-pedestrians are, the more they are likely to basetheir evaluation of the safety-level of a crossing site on a single factor – the presence or absence of vehicles on the road. Chil-dren aged 5 and 7 were found to determine the safety-level of a site purely on whether they can see cars on the road, wherethe mere presence of a vehicle (even remotely in the vicinity of the location) was correlated with these youngsters’ identi-fication of the situation as dangerous.

In order to explore the hazard perception abilities and deficiencies of child-pedestrians in the context of road-crossing,and in order to try and gain a clearer understanding regarding the implications of their lack of experience as pedestriansfor their road safety behavior, we created a taxonomy of factors (i.e., presence of zebra crossing, presence of vehicles andfield of view) which will be able to differentiate among pedestrians at different age and experience levels. It was hoped thatthe creation of this taxonomy will lay the ground towards a better understanding of child-pedestrians’ hazard perceptionability and would provide an initial step towards the process of producing proper guidelines for future hazard perceptiontraining program regarding this population. Since the majority of children are injured on non-arterial roads, particularlyin residential areas (e.g., Lawson, 1990; Roberts et al., 1994), scenarios were all placed in a generic residential simulatedenvironment. Each scenario displayed a specific road-crossing environment and was presented (and later viewed) from apedestrian’s point of view (i.e., as if the participants were pedestrians standing on one side of the pavement intending tocross over to the other side).

1.4.2. Target participantsAccording to Piaget’s (1969) influential theory of development, cognitive development is composed of four main stages,

during each, critical cognitive abilities are achieved. Pre-operational children (i.e., children under the age of 7) are charac-terized by the lack of ability to focus on two dimensions of a task at the same time. Furthermore, they often encounter


difficulties in assuming the perspective of another person. Around the age of 7, children progress to the concrete operationalstage, and begin to think logically about concrete events. Moreover, research on theory of mind (i.e., one’s ability to compre-hend that others have different inner worlds inhabited by thoughts, beliefs, and intentions that are dissimilar to his or herown) has suggested that by approximately 7 years of age, children’s understanding of the meaning of different perspectivestend to be in place (e.g., Flavell, 2000; Flavell, 2004). Accordingly, these youngsters’ abilities of differentiating time and spaceand of understanding drivers’ intentions, also tend to progress. Indeed, evidence from the road safety domain demonstratedthe success of training a variety of road crossing skills to young children (e.g., Demetre et al., 1992; Thomson et al., 1996;Thomson et al., 1992; Rothengetter, 1981). It was concluded that 7–11 years of age are the most formative stage for thedevelopment of road crossing skills among children (e.g., Foot et al., 2006; Thomson et al., 1996).

Moreover, choosing the target population for the research, the general agreement among worldwide traffic safety profes-sionals that children under the age of 9 or 10 should not cross roads alone (e.g., Percer, 2009) was taken into account. Takentogether with the evidences indicating that child-pedestrians’ crashes are a substantial cause of injury mainly between theages of 5 and 9 (e.g., NHTSA, 2008; Tabibi & Pfeffer, 2003; Whitebread & Neilson, 2000), the present study focused on pri-mary-school children at the age range of 7–13. The age-group allocation would refer to young children, under the age of 9(which has been suggested should not engage in road-crossing alone), children in the age-range of 9–10 and older children(over the age of 10).

1.4.3. Task selection and means of responsePedestrians’ decision making process involve several levels: a strategic level which take place prior to getting into traffic,

a tactical level of decisions which relates to the route selection, and an operational level of decisions that are made whilebeing on the route and are mostly related to traffic situations and environmental conditions (e.g., Sombekke & Katteler,2008). Notably, while decision skills in the tactical level, such as finding a safe place to cross the road, are important andare being researched (e.g., Ampofo-Boateng et al. 1993; Thomson et al., 1992) the present study aimed to explore pedestri-ans’ operational level. That is, given a certain, specific crossing place, if, when and why pedestrians decide to cross.

Research suggests that child-pedestrians training programs should be designed so that the knowledge gained in trainingwould be intuitively generalized and transferred to behaviors in real-life situations (Novak, 2009). Indeed, one may arguethat pressing a response button is not as intuitive as crossing a road (as is, for example, stepping onto a staged zebra cross-ing) though it may be faster and more accurate. Comparing these two alternatives via a preliminary, pilot experiment sug-gested that pressing a response button was more easily understood by young participants and enabled participants tobecome more engaged in the task. Thus, button press was determined as the appropriate means of response to be used whenit was assumed it was safe to cross.

To summarize, this experiment aimed to examine pedestrian crossing decisions in a dynamic environment. Structuredscenarios were designed based on the factors specified. Seven-to-thirteen-year-olds children and adults observed road cross-ing scenarios displaying a variety of traffic situations from the point of view of pedestrians and were asked to perform a road-crossing task, i.e., to press a button each time they thought it is safe to cross the road. Research hypotheses stated that (1)Experienced-adult pedestrians would be more sensitive to potential hazards compared to child-pedestrians. (2) The older achild-pedestrian, the more he or she would pay attention to potential hazards.

2. Method

2.1. Stimuli and design

During the experiment each participant was instructed to observe an array of 18 typical urban scenarios from a pedes-trian’s point of view. I.e., as if they were pedestrians standing on one side of the pavement intending to cross over to theother side. Each of the scenarios presented a specific road-crossing environment by portraying a different combination of:presence of zebra crossing, presence of vehicles, and field of view (see Table 1). Participants observed each scenario andpressed a response button if and whenever they assumed it was safe to cross.

Scenarios’ length varied between 10 and 45 s, each presenting participants with at least one safe-crossing opportunity.Two additional scenarios were used for practice and accommodation to the task and to the experimental system. Notably,the distinction between a one-way street and a two-way street was conveyed to the participant in the form of motion,i.e., a two-way street presented vehicles arriving from both sides of the road, heading in opposite directions, while a one-way street presented vehicles arriving only from one direction to the other.

The crossing-scenario database utilized a custom built three-dimensional generic model (a Vega prime model) of a typicalIsraeli city and a scenario generator (DI-Guy). Cars, trees, billboards and various other urban elements were also designeduniquely for this environment. Using the VT-Mak applications (http://www.mak.com/) VR-Vantage and VR-Forces, the arrayof scenarios were developed and projected.

Overall, the experimental design included one between-group fixed effect – Pedestrians’ age-group (7–9-year-olds, 9–10-year-olds, 10–13-year-olds and experienced-adult pedestrians), and three within-group fixed effects: (1) Presence of Zebra-crossing (with zebra-crossing, without zebra-crossing), (2) presence of vehicles (no moving vehicles, one-way street wheremoving vehicles are traveling in one direction, two-way street where moving vehicles are traveling in two directions), and

http://www.mak.com/

Table 1Scenario database. Each scenario was portrayed as a different combination of values given to the dynamic factor: (1) Presence of vehicles; and to the staticfactors: (1) Presence of zebra crossing and (2) field of view.

Scenario # Presence of zebra crossing Presence of vehicles Field of view

S1 No Zebra crossing No moving vehicles UnrestrictedS2 Zebra crossing No moving vehicles UnrestrictedS3 No Zebra crossing No moving vehicles Restricted by parked vehiclesS4 Zebra crossing No moving vehicles Restricted by parked vehiclesS5 No Zebra crossing Vehicles traveling in one direction UnrestrictedS6 Zebra crossing Vehicles traveling in one direction UnrestrictedS7 No Zebra crossing Vehicles traveling in one direction Restricted by parked vehiclesS8 Zebra crossing Vehicles traveling in one direction Restricted by parked vehiclesS9 No Zebra crossing Vehicles traveling in two directions UnrestrictedS10 Zebra crossing Vehicles traveling in two directions UnrestrictedS11 No Zebra crossing Vehicles traveling in two directions Restricted by parked vehiclesS12 Zebra crossing Vehicles traveling in two directions Restricted by parked vehiclesS13 No Zebra crossing No moving vehicles Restricted by the road’s curvatureS14 Zebra crossing No moving vehicles Restricted by the road’s curvatureS15 No Zebra crossing Vehicles traveling in one direction Restricted by the road’s curvatureS16 Zebra crossing Vehicles traveling in one direction Restricted by the road’s curvatureS17 No Zebra crossing Vehicles traveling in two directions Restricted by the road’s curvatureS18 Zebra crossing Vehicles traveling in two directions Restricted by the road’s curvature

Fig. 1. The Field of View factor as displayed in the Dome scenarios: (1) Unrestricted (above); (2) partially obscured by the road’s curvature (middle); and (3)partially obscured by parked vehicles (below).



(3) field of view (unrestricted, partially obscured by the road’s curvature, or partially obscured by parked vehicles, see Fig. 1).The dependent variables were response sensitivity, response time, and verbal description.

2.2. Participants

Forty-six participants, 21 experienced-adult participants (20–27-year-olds; mean age = 25.1, SD = 1.9) and 25 child-pedestrians (eight 7–9-year-olds with mean age = 7.9, SD = 0.70; five 9–10-year-olds with mean age = 9.7, SD = 0.3; andtwelve 10–13 year-olds with mean age = 11.5, SD = 1.0) completed this experiment in exchange for an educational compen-sation equivalent of 30 NIS (approx. $10) or bonus credit in an introductory to ergonomics course. Child-pedestrians wererecruited via mass mail which was sent to the IEM department of the Ben-Gurion University of the Negev’s academic staff.Experienced-adults were all students in the department, all but two above 24 years of age. All participants had normal vision,with uncorrected Snellen static acuity of 6/12 or better and normal contrast sensitivity. Participants were all requested tosign an informed consent form, approving their participation in the experiment. In addition, parental consent was requiredfor participants under the age of 18.

2.3. Apparatus

2.3.1. Dome projection facilityThe research was conducted in the Virtual Environment Simulation Laboratory (Dome Projection Facility) at the Ergo-

nomics complex in Ben-Gurion University. The 3D Perception™ ‘‘CompactClick’’ Dome screen system consists of a 180�spherical screen 3.25 m in radius aligned with a very accurate projection system consisting of three projectors ideal for sim-ulation. This array allows a simultaneous projection from three different sources to be tailored into a single wide angle 3dimensional view. The screen is supported by a steal frame which combines the screen parts and is adaptable to changesin the screen size. The screen comprises a number of elements; each consists of 15 sensors which enable the precise calibra-tion of the screen parts. The system is equipped with operational software, essential for calibrating and managing the pro-jectors and the screen.

The facility is both temperature- and noise-controlled. The dome projection system integrates the natural visual and mo-tor skills of an operator into the system he or she is controlling. The dome is large enough to have participants and theirworkstation immersed within its circumference. In addition, physical movement can be added to enhance strain and to im-prove simulation fidelity. The dome can be used as a simulation of reality, as an extension of human senses through telepres-ence, and as an information enhancer through augmented reality.

2.3.2. Designated softwareA Visual Basic designated program was developed and installed in the Dome laboratory. The program allowed the syn-

chronization between the main five control-units applied in the study: (1) the main management unit-placed on the maincomputer, utilized to serve the experimenter and to run the Data Logger. The main management unit control the other 4computers through TCP/IP communication based on LAN; (2) VR-Vantage main display computer; (3–4) 2 computers run-ning VR-Vantage Display Engines; and (5) a computer utilized for running the Eye Tracking System (ETS). Overall, this des-ignated program allowed presenting the scenarios, recording the participants’ responses (utilizing designated means ofresponse) and analyzing the responses on a single frame basis.

2.3.3. Eye trackerParticipants’ eye-movements were recorded throughout the experiment utilizing an ASL High-Speed Head Mounted Eye

Tracker (Model H6-HS, Eyetrack 6000) Head-Mounted Eye Tracking System (ETS), thus allowing an investigation of the at-tended features which might have assisted the participants in reaching their decision. Eye movements’ data can be used as acomplementary measurement in order to understand and confirm ambiguous responses (e.g., situations where participantsdo not response but gaze on the relevant regions of interest).

2.4. Procedure

Participants were invited individually to the Dome facility (see Fig. 2) for an hour and a half session. They were each askedto provide the experimenter with a signed informed consent form approving his or her participation in the experiment. Asrequired, parental consent was also given for participants under the age of 18.

The laboratory was kept at the same temperature and illumination conditions throughout the entire experiment,in order to maintain the experimental setting standardized throughout the entire study. Each participant was intro-duced to the laboratory, then, he or she went through Snellen static acuity test and contrast sensitivity test. Participantswho had uncorrected static acuity of 6/12 or better and normal contrast sensitivity were able to participate in theexperiment.

Subsequently, participants went through a stage of eye calibration, after which their eye-movements were recorded viathe ETS. The experimenter then uploaded the experiment’s designated software. Participants were then requested to settle in

Fig. 2. Simulated environment from a child-pedestrian’s point of view, situated in the Ben-Gurion University of the Negev’s Dome projection facility.


front of the laboratory’s computer monitor at an approximate distance of 2.5 m. Participants either read or were read (ifneeded) the instructions which included a comprehensive explanation of the experiment and the experimental task.

Each participant was instructed to observe several typical urban scenarios from a pedestrian’s point of view)i.e., as if theywere pedestrians standing on the pavement facing the road and intending to cross over to the other side, see Fig. 2) and toengage in a crossing decision. Thus, participants had to determine whether it was safe or unsafe to cross by pressing a re-sponse button each time he or she believed it was safe to cross the road. Participants were asked to respond as quickly aspossible once decided to cross, and further instructed to press only once for each crossing decision. Participants were alsotold that pressing the button would symbolize the crossing action that would have taken place.

Two practice scenarios were then used in order to familiarize participants with the experimental task; participants wereto observe them and respond accordingly. After making sure that the participants understood the task and were ready tocontinue, the experiment begun.

Participants were instructed to observe the 18 scenarios in a random order without knowing the exact number of scenar-ios they were about to see. Prior to each hazard perception training scenario, participants were to observe a fixation screen.Once the experimenter saw (via the ETS) that the participant’s eye fixated on the fixation mark situated in the middle of thefixation screen, he or she activated the scenario by pushing the ‘‘start’’ button. If the participant pressed the response button(i.e., indicating of his or her decision to cross the road), a pop-up window would reveal containing the phrase: ‘‘You havedecided to cross the road. Why have you decided to cross?’’ However, if the participant did not press the response buttonuntil the end of the scenario (i.e., indicative of his or her decision that it was not possible to cross the road safely) a pop-up window would reveal the phrase: ‘‘You have decided not to cross the road. Why have you decided not to cross?’’. Eitherway, participants were then instructed to fill in (either by themselves or with the experimenter’s assistance utilizing a key-board that was placed on a pedestal beside them) the blank field with their reason of why they had decided whether or not tocross (see Fig. 3). At the end of the session, participants were asked to fill in a computerized demographic questionnaire.Then they were debriefed, compensated and the experiment ended.

Fig. 3. Pop-up windows appearing after a button press was made (right) and after no button press was made throughout the scenario (left). Text boxesappear in Hebrew.


3. Results

The present study aimed to examine experienced-based hazard perception differences in road-crossing performanceamong pedestrians with varied levels of road-crossing experience, in the hope of demonstrating how better awareness tohazardous factors becomes established along the continuum of road crossing experience. Towards achieving the goal, threetypes of analyses were applied on participants’ responses: (1) Response sensitivity-examining whether a specific group ofpedestrians tended to cross more often than other groups and which types of hazardous factors were identified as relevantto the crossing decision by pedestrians with varied levels of road-crossing experience; (2) response time analysis-examiningwhether a specific group of pedestrians tended to decide to cross faster than other groups. Notably, only pedestrians whoresponded by crossing were considered in this analysis; and (3) verbal description analysis was applied to pedestrians’descriptions of the reasons why they had decided whether or not to cross, in order to decipher which dimensions of the traf-fic environment were identified by each of the pedestrian groups as hazardous. The following section depicts the results con-cerning the participants’ Response sensitivity, Response time analysis and Verbal description measurements; results derivedfrom the eye scanning measurement are not provided here.

3.1. Response sensitivity analysis

A logistic regression with a logit link function within the framework of Generalized Estimating Equations (GEEs) modelwas applied. The dependent variable was binary distributed (a decision to cross = 1, a decision not to cross = 0). The between-group fixed effect was Pedestrians’ age-group and the within-group fixed effects were (1) Presence of Zebra-crossing, (2)presence of vehicles, and (3) field of view. All second-order interactions were included in the model. Participants were in-cluded as a random effect to account for individual differences among participants. Post-hoc analyses all used SequentialBonferroni correction (Holm, 1979).

Overall, the results suggested that given time, pedestrians prefer crossing to not crossing (estimated average likeli-hood = 0.74, Standard Error = 0.04). Using a backward elimination procedure, the final model yielded three main effects:

(1) Post-hoc analysis for the Presence of Zebra-crossing main effect (Wald v21 ¼ 12:15; p ¼ 0) revealed that in situations

depicting zebra-crossing pedestrians’ likelihood to cross (estimated average likelihood = 0.81, Standard Error = 0.04)was significantly higher (p = 0.001) than in situations depicting no zebra-crossing (0.66,0.05).

(2) A post hoc analysis regarding the Field of view main effect (Wald v22 ¼ 28:50; p ¼ 0) revealed that when the field of

view was unrestricted, pedestrians’ likelihood to cross (estimated average likelihood = 0.86, Standard Error = 0.03) wassignificantly higher (p = 0.02, p < 0.001, respectively) than when it was partially obscured by the road’s curvature(0.74,0.06) or when it was partially obscured by parked vehicles (0.57,0.07). Moreover, pedestrians’ likelihood to crosswas significantly higher (p = 0.01) when the field of view was partially obscured by the road’s curvature than when itwas partially obscured by parked vehicles.

(3) Lastly, post hoc analysis for the Pedestrians’ age-group main effect (Wald v22 ¼ 11:68; p ¼ 0:01) revealed that experi-

enced-adult pedestrians’ likelihood to cross (estimated average likelihood = 0.87, Standard Error = 0.04) was signifi-cantly higher (p = 0.04, p = 0.03, respectively) than that of 9–10-year-olds’ (0.50,0.13) and that of 10–13-year-olds’(0.70, 0.05). No other significant difference was found in the tendency to cross between the various Pedestrians’age-groups.

Furthermore, several interactions were statistically significant:

(1) Presence of Zebra-crossing and Presence of vehicles (Wald v22 ¼ 10:40; p ¼ 0:01). Results of the post hoc analysis of

this interaction (see Table 2) suggest that in situations depicting zebra-crossing, pedestrians’ likelihood to crosswas significantly higher when presented with moving vehicles, either vehicles traveling in one direction (0.85,0.05)or vehicles traveling in two directions (0.80,0.05) compared to situations involving no vehicles (0.76,0.05). However,in situations depicting no zebra-crossing, pedestrians’ likelihood to cross was significantly lower when presented withmoving vehicles, either vehicles traveling in one direction (0.60,0.06) or vehicles traveling in two directions(0.62,0.08) compared to situations involving no vehicles (0.76,0.05).

Table 2The interaction between Presence of Zebra-crossing and Presence of vehicles.

Presence of zebra-crossing

Presence of vehicles

No movingvehicles

One-way street-vehicles traveling in onedirection

Two-way street-Vehicles traveling in twodirections

Zebra-crossing 0.76, 0.05 0.85, 0.05 0.80, 0.05No zebra-crossing 0.76, 0.05 0.60, 0.06 0.62, 0.08

Note: Values stand for the estimated average likelihood of button presses and their standard errors, respectively.


(2) Presence of Zebra-crossing and Field of view (Wald v22 ¼ 18:76; p ¼ 0). Results of the post hoc analysis of this inter-

action (see Table 3) suggest that while pedestrians’ likelihood to cross in situations depicting unrestricted field of viewand zebra-crossing (0.91,0.03) did not differ from situations depicting unrestricted field of view with no zebra-cross-ing (0.76,0.05). While pedestrians’ likelihood to cross in situations depicting field of view partially obscured by parkedvehicles and zebra-crossing (0.54,0.06) was not found to differ from situations depicting field of view partiallyobscured by parked vehicles with no zebra-crossing (0.60,0.08), pedestrians’ likelihood to cross in situations depictingfield of view partially being obscured by the road’s curvature was significantly higher when these situations includedzebra-crossing (0.85,0.05) than when these situations included no zebra-crossing (0.58,0.08).

(3) Presence of vehicles and Pedestrians’ age-group (Wald v22 ¼ 16:56; p ¼ 0:01). Results of the post hoc analysis of this

interaction (see Table 4) suggest that child-pedestrians in the age range of 9-to-10-years’ likelihood to cross was sig-nificantly higher (p = 0.04) in situations presenting no moving vehicles (0.68,0.10) than in those presenting vehiclestraveling in one direction (0.35,0.14).

(4) Field of view and Pedestrians’ age-group (Wald v22 ¼ 19:00; p ¼ 0). Results of the post hoc analysis of this interaction

(see Table 5) suggest that in situations depicting unrestricted field of view, experienced-adult pedestrians tended tocross (0.97,0.02) significantly (p = 0.04) more often than child-pedestrians in the age range of 9–10-years (0.61,0.11).Furthermore, experienced-adult pedestrians decided to cross significantly (p < 0.001) more often in situations eitherdepicting unrestricted field of view (0.97,0.02) or depicting field of view partially being obscured by the road’s curva-ture (0.89,0.04) than in situations depicting field of view partially obscured by parked vehicles (0.49,0.06); and child-pedestrians in the age range of 9–10-years tend to cross significantly (p < 0.001) more often in situations depictingunrestricted field of view (0.61,0.11) than in situations depicting field of view partially obscured by parked vehicles(0.36,0.15).

3.2. Response time analysis

Next, response times for those who responded were examined. Since response times are not normally distributed theywere log-transformed. Then, a Linear Mixed Model (LMM) including a backward elimination procedure was utilized. Aswas the case in the response sensitivity analysis, the between-group fixed effect included in the model was Pedestrians’age-group, and the within-group fixed effects included: Presence of Zebra-crossing, Presence of vehicles and Field of view.All second-order interactions were included in the model. The dependent variable was the participants’ log transformed re-

Table 3The interaction between Presence of Zebra-crossing and Field of view.

Presence of Zebra-crossing Field of view

Unrestricted Restricted by parked vehicles Restricted by road curvature

Zebra-crossing 0.91, 0.03 0.54, 0.06 0.85, 0.05No zebra-crossing 0.79, 0.05 0.60, 0.08 0.58, 0.08


Table 4The interaction between the Presence of vehicles and Pedestrians’ age-group.

Presence of vehicles Age-group [years]

7–9 9–10 10–13 Adults

No moving vehicles 0.76, 0.09 0.68, 0.10 0.66, 0.08 0.88, 0.04One-way street-vehicles traveling in one direction 0.85, 0.09 0.35, 0.14 0.74, 0.05 0.89, 0.04Two-way street-vehicles traveling in two directions 0.84, 0.09 0.47, 0.17 0.69, 0.07 0.81, 0.05


Table 5The interaction between Field of view and Pedestrians’ age-group.

Field of view Age-group [years]

7–9 9–10 10–13 Adults

Unrestricted 0.86, 0.06 0.61, 0.11 0.83, 0.04 0.97, 0.02Restricted by parked vehicles 0.81, 0.12 0.36, 0.15 0.57, 0.10 0.49, 0.06Restricted by road curvature 0.79, 0.11 0.53, 0.20 0.64, 0.10 0.89, 0.04



sponse time: the time that elapsed from the beginning of the event up until the pressing took place. Participants were in-cluded as a random effect to account for individual differences among participants.

Applying a Linear Mixed Model (LMM) revealed three significant main effects:

(1) Presence of vehicles (F2,534.1 = 95.23, p < 0.001). LSD pair-wise comparisons analysis suggested that pedestrians tended tocross significantly (p < 0.001) faster (Mean crossing time = 6.23 s, Mean log transformed crossing time = 1.83 s, Mean logtransformed standard error = 0.06) when encountering situations involving no vehicles than when presented with mov-ing vehicles, either vehicles traveling in one direction (11.94,2.48,0.06) or vehicles traveling in two directions(13.33,2.59,0.06). However, no significant difference was found between participants’ responses to the latter two.

(2) Field of view (F2,543.6 = 33.06, p < 0.001). Conducting LSD post hoc pair-wise comparisons analysis revealed that pedes-trians tended to cross significantly (p < 0.001) faster (Mean crossing time = 7.77 s, Mean log transformed crossingtime = 2.05, Mean log transformed standard error = 0.06) when encountering limited field of view caused by a curvethan when encountering limited field of view caused by parked vehicles (10.18,2.32,0.06) or an unrestricted field ofview (12.43, 2.52, 0.06). Moreover, pedestrians tended to cross significantly (p = 0.001) faster when encountering lim-ited field of view caused by parked vehicles than when encountering unrestricted field of view.

(3) Pedestrians’ age-group (F3,46.2 = 4.82, p < 0.01). LSD pair-wise comparisons analysis revealed that the youngest child-pedestrians (7–9-year-olds) tended to cross (Mean crossing time = 7.77 s, Mean log transformed crossing time = 2.05,Mean log transformed standard error = 0.09) significantly faster (p < 0.001, p = 0.03; respectively) than the oldestchild-pedestrians (i.e., 10–13-year-olds; 12.30, 2.51, 0.08) and the experienced-adult pedestrians (9.97,2.30,0.06). More-over, experienced-adult pedestrians tended to cross significantly (p = 0.04) faster than the oldest child-pedestrians.

The LMM also revealed three significant two-way interactions:

(1) Presence of Zebra-crossing and Field of view (F2,530.6 = 4.92, p < 0.01). As can be seen in Fig. 4, while the differencebetween pedestrians’ crossing times when presented with unrestricted field of view in situations depicting nozebra-crossing (Mean crossing time = 11.02 s, Mean log transformed crossing time = 2.40, Mean log transformed stan-dard error = 0.07) and in those depicting zebra-crossing (14.15,2.65,0.06) was large, the difference between pedestri-ans’ crossing times when presented with field of view partially being obscured by the road’s curvature in situationsdepicting no zebra-crossing (7.85,2.06,0.08) and in those depicting zebra-crossing (7.77,2.05,0.06) was much smaller.

(2) Presence of Zebra-crossing and Pedestrians’ age-group (F3,534.3 = 2.73, p = 0.04). As can be seen in Fig. 5, while theyoungest child-pedestrians (7–9-year-olds) tended to cross faster (Mean crossing time = 8.33 s, Mean log transformedcrossing time = 2.12, Mean log transformed standard error = 0.10) in situations depicting zebra-crossing than in situ-ations depicting no zebra-crossing (7.24,1.98,0.10), pedestrians in all other age-groups tended to linger more in sit-uations depicting zebra-crossing (11.59,2.45,0.14; 13.20,2.58,0.09; 10.59,2.36,0.06) than in situations depicting nozebra-crossing (9.39,2.24,0.16; 11.36,2.43,0.09; 9.39,2.24,0.07).

(3) Field of view and Pedestrians’ age-group (F6,540.4 = 5.35, p < 0.001). As can be seen in Fig. 6, experienced-adult pedes-trians tended to linger more in situations depicting field of view partially obscured by parked vehicles (Mean crossingtime = 12.55 s, Mean log transformed crossing time = 2.53, Mean log transformed standard error = 0.08) than in situ-ations depicting unrestricted field of view (11.47,2.44,0.07), while pedestrians in all other age-groups tended to cross

Fig. 4. The interaction between Presence of Zebra-crossing and field of view on response time to cross.


faster in situations depicting field of view partially obscured by parked vehicles (6.62,1.89,0.11; 11.13,2.41,0.19;11.59,2.45,0.10) than in situations depicting unrestricted field of view (11.13,2.41,0.11; 12.06,2.49,0.16;15.80,2.76,0.09).

Fig. 5. The interaction between Presence of Zebra-crossing and Pedestrians’ age-group on response time to cross.

Fig. 6. The interaction between Field of view and Pedestrians’ age-group on response time to cross.

Table 6Distribution of the dimensions of the traffic environment identified by each of the pedestrians’ age-groups as hazardous based on their verbal descriptions ofthe road-crossing scenarios.

Verbal description 7–9 9–10 10–13 Adults Total

Presence of Zebra-crossing 26 (3.3) 25 (5.0) 7 (0.6) 46 (2.2) 104 (2.3)Presence of vehicles 140 (17.5) 76 (15.2) 200 (16.7) 294 (14.0) 710 (15.4)Field of view 8 (1.0) 17 (3.4) 41 (3.4) 94 (4.5) 160 (3.5)Elements related to Time 4 (0.5) 3 (0.6) 4 (0.3) 19 (0.9) 30 (0.7)136 (3.0)Elements related to Distance 27 (3.4) 5 (1.0) 18 (1.5) 86 (4.1)Elements related to Speed 5 (0.6) 2 (0.4) 6 (0.5) 42 (2.0) 55 (1.2)

Note: Numbers represent the total number of trials in which each age-group responded to a crossing scenario. The numbers in the parenthesis represent theaverage number of responses in each age-group relative to the number of participants in each group. For example, on average 7–9 year olds noted thepresence of vehicles 17.5 times while they noted the field of view for example on average only 1 time.


3.3. Verbal description analysis

Next, participants’ verbal descriptions of the reasons why they had decided whether or not to cross were examined inorder to decipher which dimensions of the traffic environment were identified by each of the pedestrian groups as hazardous(see Table 6). Notably, some descriptions related to more than one dimension (e.g., limited field of view and presence of vehi-cles – ‘‘it is not safe to cross since a curve obstructs the view from the left and a vehicle approaches from the right’’), and werecounted several times.

Examining participants’ verbal descriptions regarding the Presence of Zebra-crossing (e.g., ‘‘It is safe to cross at a zebra-crossing’’), a significant difference was found between the pedestrians’ age-groups (v2 = 35.04, p < 0.001). Pair-wise compar-isons analysis indicated that the 10–13-year-olds mentioned the Presence of zebra-crossing significantly less (average num-ber of descriptions = 0.58) than the 7–9-year-olds (average number of descriptions = 3.25), 9–10-year-olds (5) and theexperienced-adult pedestrians (2.19) (v2 = 20.69, p < 0.001; v2 = 36.58, p < 0.001; v2 = 12.28, p < 0.001, respectively). More-over, experienced-adult pedestrians suggested significantly less verbal descriptions regarding the Presence of Zebra-crossingin comparison to the 9–10-year-olds (v2 = 11.67, p < 0.01).

Furthermore, a significant difference (v2 = 20.16, p < 0.001) was found between pedestrians’ age-groups regarding theirverbal descriptions of the Field of view factor (e.g., ‘‘it is not safe to cross since parked cars obstruct the view. Pair-wise com-parisons analysis indicated that the 7–9-year-olds suggested significantly less (average number of descriptions = 1.00) verbaldescriptions regarding Field of view in comparison to the 9–10-year-olds (average number of descriptions = 3.40), to the 10–13-year-olds (3.42) and to the experienced-adult (4.48) pedestrians (v2 = 9.22, p < 0.01; v2 = 11.44, p < 0.001; v2 = 19.90,p < 0.001, respectively).

Next, since participants’ tended to relate to additional factors, it was decided to explore the differences in their perfor-mance. Examining participants’ verbal descriptions regarding the Time factor (e.g., ‘‘I have sufficient time to cross’’), no sig-nificant difference was found between the pedestrians’ age-groups (v2 = 4.23, N.S.). Notably, however, relatively fewstatements were made altogether regarding the Time factor.

Conversely, examining participants’ verbal descriptions regarding the Distance factor (e.g., ‘‘vehicles are far away – it issafe to cross’’), a significant difference was found between the pedestrians’ age-groups (v2 = 24.77, p < 0.001). Pair-wisecomparisons analysis indicated that the 9–10-year-olds suggested significantly less (average number of descrip-tions = 1.00) verbal descriptions regarding the Distance factor in comparison to the 7–9-year-olds (average number ofdescriptions = 3.38) and to the experienced-adult (4.10) pedestrians (v2 = 7.05, p < 0.01; v2 = 11.05, p < 0.01, respectively).Moreover, the 10–13-year-olds suggested significantly less (1.50) verbal descriptions regarding the Distance factor in com-parison to the 7–9-year-olds and to the experienced-adult pedestrians (v2 = 7.50, p < 0.01; v2 = 16.32, p < 0.001,respectively).

Lastly, examining participants’ verbal descriptions regarding the Speed factor (e.g., ‘‘vehicles are approaching slowly’’), asignificant difference was found between the pedestrians’ age-groups (v2 = 21.05, p < 0.001). Pair-wise comparisons analysisindicated that the experienced-adult pedestrians suggested significantly more (average number of descriptions = 2.00) ver-bal descriptions regarding the Speed factor in comparison to the 7–9-year-olds (0.63), to the 9–10-year-olds (0.40) and to the10–13-year-olds (0.50) (v2 = 6.76, p < 0.01; v2 = 6.11, p < 0.05; v2 = 11.81, p < 0.001, respectively).

Since time, distance and speed are different indicators aiming to measure the same underlying concept, participants’verbal descriptions combining all three categories were examined, and, a significant difference was found among thePedestrians’ age-groups (v2 = 44.66, p < 0.001). Pair-wise comparisons analysis indicated that the experienced-adultpedestrians suggested significantly more (average number of descriptions = 7.00) verbal descriptions regarding the Time,Distance and Speed factors in comparison to the 7–9-year-olds (average number of descriptions = 4.50), to the 9–10-year-olds (2.00) and to the 10–13-year-olds (2.33) (v2 = 5.74, p < 0.05; v2 = 16.72, p < 0.001; v2 = 31.36, p < 0.001, respectively).Moreover, the 7–9-year-olds suggested significantly more verbal descriptions regarding the Time, Distance and Speedfactors in comparison to the 9–10-year-olds and to the 10–13-year-olds (v2 = 5.43, p < 0.05; v2 = 7.04, p < 0.01,respectively).

3.4. Summary of results

When examining the results, several issues should be noted:

1. Over time and given the need, pedestrians eventually prefer crossing to not crossing within the time allowed in this study.2. The presence of a zebra crossing facilitated crossing. Examining response times, it can be seen that when presented with

unrestricted field of view, pedestrians tended to cross faster in the absence of zebra-crossing than in its presence. Thismay be due to the sense of security its presence tends to provide to pedestrians (Ekman, 1996) in contrast to a senseof urgency which may be generated in its absence. It may also be that zebra-crossing was utilized by participants as adecision support tool. Hence, while not affected by the Presence of Zebra-crossing when they need not seek help in decid-ing when to cross (i.e., regarding unambiguous situations, e.g., when encountered by unrestricted field of view or whenencountered by field of view partially obscured by parked vehicles), participants tended to rely on and be affected by thePresence of Zebra-crossing in their decision to cross regarding limited field of view partially being obscured by road’s cur-vature (i.e., more ambiguous situation).


3. Experienced-adult pedestrians were more inclined to cross than either the 9–10-year-olds or the 10–13-year-olds, how-ever, as a group they did not differ from the 7–9-year-olds in their readiness to cross. These youngest child-pedestrians(7–9-year-olds) also tended to cross faster than the experienced-adult pedestrians and the 10–13-year-olds. Indeed, 7–9-year-olds tended to cross more often, as well as faster, compared to the other child-pedestrians’ groups. Thus it can besaid that their performance bore some resemblance to that of experienced-adult pedestrians. However, it should be notedthat, in contrast to the 7–9-year-olds, the experienced-adult pedestrians have demonstrated higher level of awareness topotential hazards (e.g., presented lower likelihood toward crossing in situations depicting limited field of view by parkedvehicles) which may indicate of the underlying difference between their calculated, informed decision to cross to the 7–9-year-olds spontaneous responses of fast crossing, regarding primarily the presence of approaching vehicles as a criterionfor crossing decision. This pattern also emerged from examining participants’ verbal descriptions-indeed, 7–9-year-oldsreferred much less to the potential hazardousness of the field of view factor in comparison to all other age-groups.

4. Discussion

Pedestrian traffic crashes are amongst the most significant causes of death, injury and long-term disability among chil-dren, particularly among children in the age range of 5-to 9-years (e.g., Tabibi & Pfeffer, 2003; Whitebread & Neilson, 2000),who endure higher injury rate than adults despite of lower levels of exposure to traffic (Thomson et al., 2005). Early researchhad indicated that young children’s higher accident rate may be correlated with their relative inability to perceive hazardscorrectly (Martin and Heimstra, 1973). According to Hill et al. (2000) there is evidence that young children are poor at iden-tifying unsafe situations. These researchers argue that the high accident rate for child-pedestrians is a result of the failure torecognize a potential danger when unprompted, thus, they suggested that young children’s understanding of danger is notrobust (Hill et al. 2000). The present study aimed to examine experienced-based hazard perception differences in road-cross-ing performance among child pedestrians with varied levels of road-crossing experience, in the hope of explaining how bet-ter awareness to hazardous factors becomes established along the continuum of road crossing experience. Researchhypotheses stated that (1) Experienced-adult pedestrians would be more sensitive to potential hazardousness comparedto child-pedestrians. (2) The older a child-pedestrian, the more he or she would pay attention to potential hazardousness.

The research had met its aims and supported both of its hypotheses. The results demonstrated the different responses ofeach experimental group towards each of the road environment factors (by means of response sensitivity, response time andverbal description). The following will discuss the main results of the study with reference to the literature reviewed. Wewill end with a discussion about optional direction for future research and some of the current research limitations.

4.1. Performance on the specific dependent measures

Forty-six participants were allocated into four experimental groups: three young-novice pedestrian-groups and one expe-rienced pedestrians’ group. Participants were all requested to observe 18 typical urban scenarios, filmed from a pedestrian’sperspective, and to engage in a crossing decision – i.e., to determine whether it is safe or unsafe to cross by pressing a re-sponse button each time he or she believed it was safe to cross the road.

It was decided to explore road factors which may contribute to pedestrians’ crossing performance, and specifically, tochild-pedestrians’ performance. Three main factors were chosen: Presence of Zebra-crossing, Presence of vehicles and Fieldof view. As past research had shown, the majority of children are injured on non-arterial roads, particularly in residentialareas (e.g., Lawson, 1990; Roberts et al., 1994). Thus, the Dome-scenarios were all located in residential areas.

4.1.1. Zebra-crossing factorOverall, it can be said that the presence of a zebra crossing facilitated crossing. Indeed, it was revealed that in situations

depicting zebra-crossing pedestrians’ likelihood to cross was higher than in situations depicting no zebra-crossing. It can alsobe suggested that, as was viewed in past research, the presence of zebra-crossing provided participants with a sense of secu-rity (Ekman, 1996), helping them to feel they can cross the road safely. Indeed, in situations depicting zebra-crossing pedes-trians’ likelihood to cross was higher when presented with moving vehicles compared to situations involving no vehicles.However, in situations depicting no zebra-crossing, pedestrians’ likelihood to cross was lower when presented with movingvehicles compared to situations involving no vehicles.

Zebra-crossing was also applied as a decision support tool for ambiguous situations. Indeed, it was evident that whilepedestrians’ likelihood to cross in situations depicting unrestricted field of view and zebra-crossing was not found to differfrom situations depicting unrestricted field of view with no zebra-crossing, and while pedestrians’ likelihood to cross in sit-uations depicting field of view partially obscured by parked vehicles and zebra-crossing was not found to differ from situ-ations depicting field of view partially obscured by parked vehicles with no zebra-crossing, pedestrians’ likelihood to cross insituations depicting field of view partially being obscured by the road’s curvature was higher when these situations includedzebra-crossing than when these situations included no zebra-crossing.

It should also be noted that the younger children, namely the 7–9-year-olds and the 9–10-year-olds tended to refer to thePresence of zebra-crossing in their verbal descriptions compared to the older participants. A plausible explanation may sug-gest that for the youngest children the zebra-crossing provided with a salient cue, helping them in their decision whether tocross. Children in the age range of 9–10 still in the initial stage of road-crossing skill acquisition, namely, are just being


taught of the rules of the road, thus their knowledge is still only in the declarative level and was not practiced enough tosolidify and transform into a procedural level (Anderson, 1995).

4.1.2. Moving vehicles factorOverall, the presence of vehicles was taken into account as a main factor in the participants’ road-crossing decision. In-

deed, most (42%) of the 1694 factors referred to in the participants’ verbal descriptions regarded the Presence of vehicles (i.e.,710 descriptions). However, being a salient factor, addressed broadly by most of the participants, not many differences werefound between the pedestrians age-groups’ performance to this dimension. The main finding suggested that pedestrianstended to cross faster when encountering situations involving no vehicles than when presented with moving vehicles, eithervehicles traveling in one direction or vehicles traveling in two directions, reminiscing of Summersgill and Layfield’s (1998)research which have indicated that there is no difference in the level of pedestrians’ crash risk between one-way and two-way roads with the same cross-section.

4.1.3. Field of view factorPast research had also suggested that pedestrians’ traffic crashes which occur in populated areas tend to take place in

locations where visibility is restricted (e.g., at curves or near parked vehicles) (Ampofo-Boateng & Thomson, 1990). Indeed,it was revealed that when the field of view was unrestricted pedestrians’ likelihood to cross was higher than when it waspartially obscured by the road’s curvature or than when it was partially obscured by parked vehicles. Moreover, pedestrians’likelihood to cross was higher when the field of view was partially obscured by the road’s curvature than when it was par-tially obscured by parked vehicles. Although it may be argued that this effect is due to fact that the parked vehicles depictedwere a good deal closer to the crossing site than the road’s curvature in the road, these results are consistent with previousfindings indicating that on-street vehicle parking presents a particular risk for child-pedestrians (Petch & Henson, 2000).Parked cars interfere with their ability to detect oncoming vehicles, while also obstructing the motor vehicle drivers’ vision,preventing them from noticing child-pedestrian who may be masked by stationary vehicles along the road (e.g., Aoki &Moore, 1996; Petch & Henson, 2000). In consistence with the literature, it was revealed that experienced-adult pedestrianstended to linger more in situations depicting field of view partially obscured by parked vehicles than in situations depictingunrestricted field of view, while pedestrians in all other age-groups tended to cross faster in situations depicting field of viewpartially obscured by parked vehicles than in situations depicting unrestricted field of view. It can also be suggested that the7–9-year-olds were the least aware of the field of view aspect, suggesting less verbal descriptions regarding Field of view incomparison to the 9–10-year-olds to the 10–13-year-olds and to the experienced-adult pedestrians.

4.1.4. Age-group factorExamining age-group differences may reveal two main modes of responses: (1) the older child-pedestrians responses (i.e.,

children in the age range of 9–13) and (2) the younger children and the experienced-adult pedestrians. Both the 9–10-year-olds and the 10–13-year-olds tended to present a less decisive performance compared to both the experienced-adult pedes-trians and the 7–9-year-olds. Overall, they tended to be less likely to cross and less likely to cross fast when decided to do so.Comparing between these two age-groups, both were more inclined to take the time to decide what to do and when to do it,however, while 9–10-year-olds tended to wait and not engage in action, 10–13-year-olds were more incline to wait but tocross afterwards. Since reaction times were calculated according to those crossing, this pattern of responses sometimes ledto higher likelihood of crossing with longer response time in 10–13-year-olds compared to the 9–10-year-olds.

Examining age-group patterns it can be seen that experienced-adult pedestrians were more inclined to cross than eitherthe 9–10-year-olds or the 10–13-year-olds, however, did not differ from the 7–9-year-olds in their readiness to cross. Theseyoungest child-pedestrians (7–9-year-olds) also tended to cross faster than the experienced-adult pedestrians and the 10–13-year-olds. Indeed, 7–9-year-olds tended to cross more often, as well as faster, compared to the other child-pedestrians’groups, thus it can be said that while their performance bore some resemblance to that of experienced-adult pedestrians(which have shown a marked resemblance to experienced drivers’ characteristics in previous studies – see Borowskyet al., 2010 for example for details), the 7–9-year-olds have demonstrated a lower level of awareness to potential hazards(e.g., presented higher likelihood towards crossing in situations depicting limited field of view by parked vehicles) whichmay indicate of the underlying difference between the experienced-adult pedestrians’ calculated, informed decision to crossconsidering potential hazards to the 7–9-year-olds’ spontaneous responses of fast crossing. This pattern also emerged fromexamining participants’ verbal descriptions-indeed, 7–9-year-olds referred much less to the potential hazardousness of thefield of view factor (referred to the presence of vehicles 17.5 times more) in comparison to all other age-groups (e.g., theexperienced-adult pedestrians referred to the presence of vehicles only 3.13 times more than to the field of view factor). Fur-thermore, the 7–9-year-olds tended to rely more heavily on the zebra-crossing cue, crossing faster in situations depictingzebra-crossing than in situations depicting no zebra-crossing, compared to pedestrians in all other age-groups.

5. Conclusions

McKenna (2010) had noted that few educational interventions aiming to improve road-safety are designed on the basis ofstructured theory or formal body of evidence. The current study, aiming to offer a basis for creating road-crossing training


intervention for child-pedestrians, utilized a conceptual approach taken from the driving hazard perception domain (e.g.,Borowsky et al., 2010; Meir, et al., in press; Meir et al., 2010). The research had suggested that engaging in a road-crossingdecision task, of observing hazard perception scenarios and pressing a response button each it is safe to engage in crossing,can differentiate between child-pedestrians and experienced-adult pedestrians – i.e., this methodology may be utilized as avalidated tool for differentiating between diverse levels of road-crossing experience.

Examining the differences between the various age-groups’ responses by a variety of performance measurements (re-sponse sensitivity, response time, verbal descriptions) it was evident that experienced-adult pedestrians are be more sensi-tive to potential hazardousness compared to child-pedestrians, and that the older a child-pedestrian, the more he or she paysattention to potential hazardousness. Overall, finding indicated that the older children, both the 9–10-year-olds and the 10–13-year-olds, tended to present a less decisive performance compared to both the experienced-adult pedestrians and the 7–9-year-olds. The 9–10-year-olds tended to wait and not engage in action, while the 10–13-year-olds tended to linger, butcross nonetheless, leading to higher likelihood of crossing with longer response for the later. Experienced-adult pedestrianswere more inclined to cross than either the 9–10-year-olds or the 10–13-year-olds but did not differ from the 7–9-year-oldsin their readiness to cross. Indeed, 7–9-year-olds tended to cross more often, as well as faster, compared to the other child-pedestrians’ groups; however, they have demonstrated a lower level of awareness to potential hazards (e.g., referred muchless to field of view limited by parked vehicles) indicative of the underlying difference between their rushed response andthe experienced-adult pedestrians’.

Results serve applicable meaning – the differences that emerged between the various children age groups reinforce thatchild pedestrians cannot be trained as a group but rather that training needs to be adjusted to the level of experience thechild has gained. To conclude, the current research had met its aims and supported the hypotheses which suggested thatthe utilization of the Dome-projection settings may be effective as a tool for differentiating between pedestrians varyingin their experience level of road-crossing’s ability to detect hazards prior to their materialization.

5.1. Research limitations

The current study involved several limitations:The sample size might be considered a limitation for this study. However, it should be noted that as we were working

with a pre-determined and common a = 5%, the probability of finding false positive was set a priori and the sample size af-fected the power of our procedures, the probability for true positive. Despite the relative small sample size significant effectswere found, which strengthen the findings. Still due to the low power resulting from the small sample size, additional effectsmay have been missed.

The way of recruitment may have created a bias in the results, as the random sample might have not been a represen-tative one of the entire child pedestrian population. Replication of the results with a larger and more diverse sample wouldprovide further support for the findings.

Another limitation regards the confounding variables of age and experience, which are common confounding variables inthe road safety domain (e.g., Mcknight & Mcknight, 2003; Horswill & McKenna, 2004). Due to the coincidental timing of thetwo, it is difficult to assess whether participants’ behavior stems from their age or level of experience. Although age is animportant factor to consider, research has shown that road crossing skills are not utterly dependent upon maturational fac-tors (Ampofo-Boateng et al., 1993) and that the experience factor plays an important role as well. Moreover, it may be sug-gested that the age level of the experience-adult population participated in the current study is insufficient since braindevelopment is a process yet to reach its completion in the early twenties. However, in the current study, all but two par-ticipants were above 24 years of age, an age where most regions of the brain had reached full maturity (e.g. Lebel, Walker,Leemans, Phillips & Beaulieu, 2008). All of the adult pedestrians were students, with at least 11 years of experience as pedes-trians. Thus, although it can be assumed that they’re highly mature and experienced in the road-crossing task, future re-search may further examine this issue by comparing their performance to that of older, more experienced pedestrians.

Furthermore, the current study faced the limitation of external, ecological validity. As in all experimental studies, the gen-eralization of the current study’s results to the external environment is limited. Real-life road-crossing is a complex taskwhich involves, besides the necessity to identify potential and actual hazards, various other components. These components,such as making roadside timing judgments, and the necessity to deal with various external components (e.g., familiarity withcertain crossing areas, crossing-vehicles’ drivers-attentions), as well as, internal factors (e.g., distraction, fatigue and stresslevels), create unique conditions which are very hard to replicate in a laboratory settings. Moreover, it may be argued thatthese settings allow participants to take more risk, as they know and realize that nothing serious can happen, thus biasingtheir performance. Nevertheless, literature suggested that the blurring of the boundary between real and unreal, made pos-sible with virtual reality and mixed reality, may help in bridging the gaps between the laboratory-setting to the real world(Carlin, 1997). There is also evidence of transfer between the knowledge gained within the simulated traffic environmentsand real world settings (e.g., McComas et al., 2002; Foot et al., 2006).

Lastly, the study has also shown that children, as of the age of 7 are capable of undergoing a long session in the mixedreality environment. However, preliminary testing with younger children around 6 years old showed that those had difficul-ties in maintaining focused in this mixed reality environment. Future research aiming for this age-range should take this lim-itation into account.


5.2. Directions for future research

Taken together, the current study’s results may suggest an initial evident to the notion stating that the more a pedestrianresponds to events and experiences road-crossing, the more he or she learns how to estimate the probabilities that hazardsmight appear in specific environments and how to accurately assess the possible outcomes such a hazard may have based onpast experience. It seems that in this case experience indeed enables pedestrians, as it was found to do in the case of drivers(e.g., Borowsky et al., 2010), to integrate elements in the environment and predict future events (Endsley, 1995).

The experimental environment which has been developed allowed for generating a plethora of simple and complex sce-narios in a typical Israeli urban environment. Enabling the development of very structured situations with multiple elementsmay be valuable for training, as it has been suggested that exposure to a variety of actual hazards may increase young-noviceroad-users’ awareness to potential hazardousness in the road-safety realm (Meir et al., 2010; Meir et al., in press).

To summarize, the research aimed to lay the foundations for examining whether training child-pedestrians’ hazard per-ception skills while crossing a road may enhance their ability to perceive potentially hazardous situations and to predict haz-ards prior to their materialization. It served as an important first step in the process of constructing an interventiontechnique which may reduce child-pedestrians’ over-involvement in traffic crashes. Its significance stems from the oppor-tunity it offers to engage in a novel training methodology concerning child-pedestrians’ road safety behavior in an off-roadsettings, without exposing them to the risks of getting injured and without jeopardizing their lives. Moreover, the currentresearch provided an evident suggesting that children young as the age of 7 are capable of undergoing a long session inthe mixed reality environment, thus helping to promote the idea of utilizing hazard perception scenarios as a tool to trainchild-pedestrians to detect hazards and to predict hazardous situations prior to their materialization. Indeed, utilizing theparadigms and sophistications developed in this research to produce a comprehensive, coherent intervention (as was inthe case of Meir et al., 2010; Meir et al., in press), may result in creating a type of intervention which may become a beneficialcomponent in the Israeli road safety system, as part of the effort of reducing child-pedestrians’ involvement in traffic crashes.

Acknowledgment

This research was supported by the Ran Naor Foundation, Tsippy Lotan, Ph.D, technical monitor.

References

Ampofo-Boateng, K., & Thomson, J. A. (1990). Child pedestrian accidents: a case for preventive medicine. Health Education Research, 5(2), 265–274.Ampofo-Boateng, K., & Thomson, J. A. (1991). Children’s perception of safety and danger on the road. British Journal of Psychology, 82, 487–505.Ampofo-Boateng, K., Thomson, J. A., Grieve, R., Pitcairn, T., Lee, D. N., & Demetre, J. D. (1993). A developmental and training study of children’s ability to find

safe routes to cross the road. British Journal of Developmental Psychology, 11, 31–45.Anderson, J. R. (1995). Learning and Memory: An Integrated Approach. New York: John Wiley & Sons, Inc..Aoki, M., & Moore, L. (1996). KIDSAFE: A young pedestrian safety study. ITE Journal, 36–45.Armsby, P., Boyle, A. J., & Wright, C. C. (1989). Methods for assessing drivers’ perception of specific hazards on the road. Accident Analysis & Prevention, 21,

45–60.Barton, B. K., & Morrongiello, B. A. (2011). Examining the impact of traffic environment and executive functioning on children’s pedestrian behaviors.

Developmental Psychology, 47(1), 182–191.Benda, H. V., & Hoyos, C. G. (1983). Estimating hazards in traffic situations. Accident Analysis & Prevention, 15, 1–9.Borowsky, A., Oron-Gilad, T., & Parmet, Y. (2009). Age and skill differences in classifying hazardous traffic scenes. Transportation Research Part F, 12, 277–287.Borowsky, A., Shinar, D., & Oron-Gilad, T. (2010). Age, skill, and hazard perception in driving. Accident Analysis and Prevention.Carlin, A. S., Hoffman, H. G., & Weghorst, S. (1997). Virtual reality and tactile augmentation in the treatment of spider phobia: a case report. Behav Res Ther,

35, 153–158.Demetre, J. D., & Gaffin, S. (1994). The salience of occluding vehicles to child pedestrians. British Journal of Educational Psychology, 64, 243–251.Demetre, J. D., Lee, D. N., Pitcairn, T. K., Grieve, R., Thomson, J. A., & Ampofo-Boateng, K. (1992). Errors in young children’s decisions about traffic gaps:

Experiments with roadside simulations. British Journal of Psychology, 83, 189–202.Egan, C. D. (2012). Children’s gaze behaviour at real-world and simulated road crossings. PhD thesis, Edinburgh Napier University.Ekman, L. (1996). On the treatment of flow in traffic safety analysis, Bulletin 136. Sweden: University of Lund.Endsley, M. R. (1995). Toward a theory of situation awareness in dynamic systems. Human Factors, 37, 32–64.Flavell, J. H. (2000). Development of children‘s knowledge about the mental world. International Journal of Behavioral Development, 24(1), 15–23.Flavell, J. H. (2004). Theory-of-mind development: Retrospect and prospect. Merrill-Palmer Quarterly, 50, 274–290.Foot, H. C., Thomson, J. A., Tolmie, A., Whelan, K., Morrison, S., & Sarvary, P. (2006). Children’s understanding of drivers’ intentions. British Journal of

Developmental Psychology, 24, 681–700.Foot, H. C., Tolmie, A., Thomson, J. A., McLAren, B., & Whelan, K. (1999). Recognising the danger. The Psychologist, 12, 400–402.Gielen, A. C., DeFrancesco, S., Bishai, D., Mahoney, P., Ho, S., & Guyer, B. (2004). Child pedestrians: the role of parental beliefs and practices in promoting safe

walking in urban neighborhoods. Journal of Urban Health, 81(4), 545–555.Hill, R., Lewis, V., & Dunbar, G. (2000). Young children’s concepts of danger. British Journal of Developmental Psychology, 18, 103–120.Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70.Horswill, M. S., & McKenna, F. P. (2004). Drivers’ hazard perception ability: Situation awareness on the road. In S. Banbury & S. Tremblay (Eds.), A Cognitive

Approach to Situation Awareness (pp. 155–175). Aldershot, UK: Ashgate.Hughes, C. E., Stapleton, C. B., Hughes, D. E., and Smith. E. M. Mixed reality in education, entertainment, and training. IEEE Computer Graphics and

Applications, 2005. pp. 24–30, Nov./Dec.Koepsel, T., McCloskey, L., Wolf, M., Vernez Moudon, A., Buchner, D., Krauss, J., et al (2002). Crosswalk markings and the risk of pedestrian-motor vehicle

collisions in older pedestrians. Journal of the American Medical Association, 288(17), 2136–2148.Lawson, S. (1990). Accidents to young pedestrians: Distributions, Circumstances. Consequences and scope for Countermeasures. Basingstoke: AA foundation for

Road Safety Research and Birmingham City council.Lebel, C., Walker, L., Leemans, A., Phillips, L., & Beaulieu, C. (2008). Microstructural maturation of the human brain from childhood to adulthood. Neuroimage,

40(3), 1044–1055.

http://refhub.elsevier.com/S1369-8478(13)00055-7/h0200





































Loukaitou-Sideris, A, Liggett. R., Sung, H. G. Death on the crosswalk: A study of pedestrian-automobile collisions in Los Angeles. J Plan Educ Res 2007, 26:338–351.

Macpherson, A., Roberts, I., & Pless, I. B. (1998). Children’s exposure to traffic and pedestrian injuries. J. Public Health, 88, 1840–1843.MacGregor, C., Smiley, A., and Dunk, W. Identifying gaps in child pedestrian safety: Comparing what children do with what parents teach.Transportation

Research Record: Journal of the Transportation Research Board, 1999, 1674(1), 32–40.Martin, A. (2006). Factors influencing pedestrian safety: A litriture review. TRL Report PPR 241. Crowthorne: Transport Research Laboratory.Martin, S. L., Lee, S. M., & Lowry, R. (2007). National prevalence and correlates of walking and bicycling to school. American Journal of Preventive Medicine, 33,

98–105.McComas, J., MacKay, M., & Pivik, J. (2002). Effectiveness of virtual reality for teaching pedestrian safety. Cyberpsychol Behav, 3, 185–190.McDonald, N. C. (2008). Children’s mode choice for the school trip: The role of distance and school location in walking to school. Transportation, 35, 23–35.McKenna, F. P. Education in Road Safety. Are we getting it right? RAC Foundation Report 10/113, 2010, UK.McKnight, A. J., & McKnight, A. S. (2003). Young Novice Drivers: Careless or Clueless? Accident Analysis and Prevention, 35(6), 921–925.Meir, A., Borowsky, A., Oron-Gilad, T., Parmet, Y., & Shinar., D. (2010). Act and anticipate hazard perception training for young-inexperienced drivers. In The

3rd international conference on applied human factors and ergonomics (AHFE), July 17–20.Meir A., Borowsky A., & Oron-Gilad T. (in press). Formation and Evaluating the AAHPT (Act and Anticipate Hazard Perception Training) intervention for

young-novice drivers. Traffic Injury prevention.Milgram, P., and Kishino, F. A taxonomy of mixed reality visual displays,’’ IE-ICE Trans. on Information and Systems (Special Issue on Networked

Reality),1994, vol.E77-D, no.12, pp. 1321–1329.Morrongiello, B. A., & Barton, B. K. (2009). Child pedestrian safety: Parental supervision, modeling behaviors, and beliefs about child pedestrian competence.

Accident Analysis and Prevention, 41, 1040–1046.National Highway Traffic Safety Administration (2008). Traffic safety facts 2007 data: Pedestrians. Washington, DC: NHTSA. (Publication No. DOT HS 810

994).Novak, S. Virtual environment pedestrian training programs for children: a review of the literature. Studies By Undergraduate Researchers At Guelph, 2009,

2(2).Percer, J. (2009). Child pedestrian safety education: Applying learning and developmental theories to develop safe street-crossing behaviors. Washington, DC:

National Highway Traffic Safety Administration.Petch, R. O., & Henson, R. R. (2000). Child road safety in the urban environment. Journal of Transport Geography, 8, 197–211.Piaget, J. (1969). The Mechanisms of Perception. London: Rutledge & Kegan Paul.Reid, D. (2009). Virtual reality and the person-environment experience. CyberPsychology and Behavior, 5, 559–564.Roberts, I. G., Keall, M. D., & Frith, W. J. (1994). Pedestrian exposure and the risk of child pedestrian injury. Journal of Paediatric Child Health, 30(3), 220–223.Rothengetter, J. A. (1981). Traffic safety education for young children, Lisse: Swets and Zeitlinger.Schwebel, D. C., Davis, A. L., & O’Neal, E. E. (2011). Child pedestrian injury: A review of behavioral risks and preventive strategies. American Journal of Lifestyle

Medicine.Schwebel, D. C., Gaines, J., & Severson, J. (2008). Validation of VR as a tool to understand and prevent child pedestrian injury. Accident; Analysis and

Prevention, 40, 1394–1400.Schwebel, D. C., & McClure, L. A. (2010). Using virtual reality to train children safe street-crossing skills. Injury Prevention, 16(1), e1–e5.Sombekke, E., & Katteler, H. (2008). Pedestrians: Needs, facilities and interventions. A report prepared for DVS, Nijmegen, The Netherlands: ITS-Radboud

University.Summersgill, I., & Layfield, R. (1998). Non-junction accidents on urban single-carriageway roads. TRL report TRL183. Crowthorne: Transport Research

Laboratory.Tabibi, Z., & Pfeffer, K. (2003). Choosing a safe place to cross the road: The relationship between attention and identification of safe and dangerous road

crossing sites. Child: Care, Health and Development, 29(4), 237–244.Thomson, J. A., Ampofo-Boateng, K., Pitcairn, T., Grieve, R., Lee, D. N., & Demetre, J. D. (1992). Behavioural group training of children to find safe routes to

cross the road. British Journal of Educational Psychology, 62(2), 173–183.Tester, J. M., Rutherford, G. W., Wald, Z., & Rutherford, M. W. (2004). A matched case–control study evaluating the effectiveness of speed humps in reducing

child pedestrian injuries. American Journal of Public Health, 94(4), 646–650.Thomson, J. A., Tolmie, A. K., Foot, H. C. & McLaren, B. (1996). Child development and the aims of road safety education. Road safety research report no. 1.

London: HMSO.Thomson, J. A., Tolmie, A. K., Foot, H. C., Sarvary, P. A., Whelan, K. M., & Morrison, S. (2005). Influence of virtual reality training on the roadside crossing

judgements of child pedestrians. Journal of Experimental Psychology: Applied, 11(3), 175–186.Tolmie, A., Thomson, J. A., Foot, H. C., McLaren, B., & Whelan, K. M. (1998). Problems of attention and visual search in the context of child pedestrian behavior.

Road safety research report no. 8. London: Department of Transport, Environment & The Regions.Underwood, J., Dillon, G., Farnsworth, B., & Twiner, A. (2007). Reading the road: The influence of age and sex on child pedestrians’ perceptions of road risk.

British Journal of Psychology, 98, 93–110.Van der Molen, H. H. (1981). Child pedestrian’s exposure, accidents and behavior. Accident Analysis and Prevention, 13, 193–224.Vinjé, M. P. (1981). Children as pedestrians: Abilities and limitations. Accident Analysis & Prevention, 13, 225–240.Whitebread, D., & Neilson, K. (2000). The contribution of visual search strategies to the development of pedestrian skills by 4–11 year-old children. British

Journal of Educational Psychology, 70, 539–557.Zegeer C. V. Synthesis of safety research- pedestrians. Highway Safety Research Center, University of North Carolina, HSRC-TR90. 1991.



































Date post:	09-Dec-2016
Category:	Documents
Upload:	tal
View:	213 times
Download:	0 times

Towards understanding child-pedestrians’ hazard perception abilities in a mixed reality dynamic...

Documents