Accident Analysis and Prevention 40 (2008) 1394–1400

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Accident Analysis and Prevention

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Validation of virtual reality as a tool to understand and prevent

child pedestrian injury

David C. Schwebela,∗, Joanna Gainesa, Joan Seversonb

a Department of Psychology, University of Alabama at Birmingham, 1300 University Boulevard, CH 415, Birmingham, AL 35294, United Statesb

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ate cocorreiabilisers’r virtting

Digital Artefacts, LLC, Iowa City, IA, United States

a r t i c l e i n f o

Article history:Received 17 October 2007Received in revised form 9 January 2008Accepted 7 March 2008

Keywords:Virtual realityPedestrian injuryPedestrian safetySimulationValidityChildren

a b s t r a c t

In recent years, virtual reaAmong the many benefitsin a safe environment whuseful for health-related rthe real world. This studyenvironment. A sample ofin both the virtual environconstruct validity was demworlds. Results also indicgent validity by showingvirtual world; internal relvalidity, as measured by uof generalizability to otheunderstanding and preven

1. Introduction

Over 11,000 children ages 7–9 visit the emergency room annu-ally due to a pedestrian injury in the United States (National Centerfor Injury Prevention and Control [NCIPC], 2007). Most pedestrianinjuries among this age group occur when the child is alone or withsame-age peers (Agran et al., 1994; Wills et al., 1997), and severalstudies suggest young children regularly negotiate dangerous streetenvironments alone or with other children, unsupervised by adults(Macpherson et al., 1998; Martin et al., 2007; Rivara et al., 1989).More recent reports suggest 43% of 9-year-old American childrenwho live within a mile of their school use “active travel” (includ-ing walking, bicycling, or other non-mechanized methods) to get toschool at least once per week (Martin et al., 2007). A study in Canadafound that 23% of fourth-graders walked alone to school and anadditional 34% walked with other pre-teen children (Macphersonet al., 1998). In that same sample, children ages 8–9 crossed a meanof 4.8 streets (S.D. = 5.3) to walk from home to school (Macphersonet al., 1998).

∗ Corresponding author. Tel.: +1 205 934 8745; fax: +1 205 975 6110.E-mail address: schwebel@uab.edu (D.C. Schwebel).

0001-4575/$ – see front matter © 2008 Elsevier Ltd. All rights reserved.doi:10.1016/j.aap.2008.03.005

as emerged as an innovative tool for health-related education and training.irtual reality is the opportunity for novice users to engage unsupervisede real environment might be dangerous. Virtual environments are onlych, however, if behavior in the virtual world validly matches behavior inesigned to test the validity of an immersive, interactive virtual pedestrianhildren and 74 adults was recruited to complete simulated road-crossingst and the identical real environment. In both the child and adult samples,rated via significant correlations between behavior in the virtual and realnstruct validity through developmental differences in behavior; conver-

lations between parent-reported child temperament and behavior in thety of various measures of pedestrian safety in the virtual world; and faceself-reported perception of realism in the virtual world. We discuss issuesual environments, and the implications for application of virtual reality topediatric pedestrian injuries.

© 2008 Elsevier Ltd. All rights reserved.

Clearly, strategies to prevent child pedestrian injury are ofurgent public health need. In fact, pediatric pedestrian injury hasbeen named a targeted priority by the National Center for Injury

Prevention and Control in the United States (NCIPC, 2002). Effortsto reduce child pedestrian rates have achieved mixed success. Envi-ronmental manipulations such as the installation of crosswalks andtraffic lights are generally viewed to be helpful (Retting et al., 2003;Tester et al., 2004), but are expensive and politically challengingto implement. Behavioral interventions targeting children and/ordrivers are typically less expensive, but recent reviews stronglycriticize the methodological quality of published outcome stud-ies (Duperrex et al., 2002). Further, those studies that have beenconducted achieve only moderate success at behavioral change insafe pedestrian activity, particularly when assessments are madeseveral months post-intervention (Duperrex et al., 2002).

1.1. Virtual reality

One promising strategy to reduce child pedestrian injury ratesis through virtual reality. Virtual reality, which is defined as acomputer- or video-generated environment that gives the user asense of being in a displayed virtual world through realistic images,high-quality sound, the feeling of immersion, and the ability tointeract with the virtual world (Reid, 2002), has become increas-

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D.C. Schwebel et al. / Accident Analy

ingly accessible as the cost of computing power has decreasedand the capacity of computer technology to portray high-qualitysimulations of the real world has increased. In fact, behavioralresearchers have begun to explore the utility of virtual reality tounderstand a wide range of health behaviors, ranging from train-ing and conducting high-risk medical surgery (McCloy and Stone,2001) to therapy for phobia of dental procedures (Sullivan et al.,2000).

Virtual reality offers a few distinct advantages over othertools available for health-related research and education, includingpedestrian safety education. Most prominent, it allows an indi-vidual to engage in an activity when the actual environment isdangerous. Thus, virtual reality is ideal for work with individu-als who are novice to or fearful of a situation or an environment,especially when acting in a competent, confident, and safe mannerduring immersion in that environment is critical. One such envi-ronment for most American children under age 10 is pedestriansituations.

Previous research suggests extensive practice in pedestrian set-tings under the direct supervision of an adult is effective in reducingpedestrian injury risk (Rothengatter, 1984), but it is extremelytime-consuming and pragmatically difficult. Virtual reality offersan appealing alternative to expose children repeatedly to realistictraffic situations, without also exposing them to risk of injury. Infact, such training could ultimately occur widely in school settingsand without adult supervision.

Research on virtual reality as a tool to teach children pedestriansafety is in its infancy. A number of laboratories have experimentedwith the use of virtual reality to study child pedestrian (Clancy et al.,2006; McComas et al., 2002; Simpson et al., 2003; Thomson et al.,2005) and bicycling safety (Peterson et al., 1994, 1995; Plumert etal., 2004), but almost all existing research relies on non-immersivevirtual environments.

The few attempts to use immersive and interactive VR sys-tems have proven successful in initial testing. By immersive, wemean that users are directly immersed into the traffic environment;they do not view the environment from a third-person “above” or“beside” perspective. By interactive, we mean that users interactdirectly with the street environment; when the user moves, thecomputer reacts.

One group has designed an immersive “cave” environment tostudy children’s bicycling behavior. Sitting atop of a stationary bicy-cle, children bike through a neighborhood and judge safety of aseries of intersections. Initial data from that system targeted devel-

opmental change, and found that children delayed their initiationacross safe gaps and took longer to cross traffic gaps than adults(Plumert et al., 2004, 2005, 2007). A second group used a head-mounted display VR system to study pediatric pedestrian safety(Clancy et al., 2006; Simpson et al., 2003). Initial work offeredsome validity of the system by reporting unsafe road-crossingsamong younger participants and safer behavior by adults (Simpsonet al., 2003); later work by a different research team at the sameuniversity reported greater risk-taking among a small sample ofadolescents with ADHD compared to a matched sample withoutADHD (Clancy et al., 2006).

A major limitation to existing research is the lack of validationdata. Before virtual reality can be properly developed as tool tounderstand and prevent pedestrian injuries, empirical evidence isneeded to suggest pedestrian behavior in a virtual world matchesbehavior in the real world. In other domains, there is strong evi-dence that virtual reality is a valid strategy for training physiciansin surgery (e.g., Aucar et al., 2005; Gomoll et al., 2007; van Dongenet al., 2007) and to train individuals with TBI in memory-relatedoffice skills (Matheis et al., 2007). Existing validation data for virtualpedestrian environments is limited to early evidence of age-related

d Prevention 40 (2008) 1394–1400 1395

differences in behavior in immersive and interactive virtual envi-ronments (Plumert et al., 2004; Simpson et al., 2003).

1.2. The present study

The present study was developed to demonstrate the validity ofvirtual reality as a tool to understand and prevent children’s pedes-trian injuries. We recruited a sample of 102 children and 74 adultswho completed simulated road-crossings in an immersive, inter-active virtual environment as well as in the real environment thatwas simulated virtually. We hypothesized that behavior in the vir-tual and real situations would be comparable for both children andadults, demonstrating construct validity of the virtual environment.We also expected to demonstrate construct validity of the virtualworld by showing developmental differences (that is, adults wouldengage more safely in the virtual world than children); conver-gent validity by showing differences in pedestrian behavior basedon children’s parent-reported temperament; internal reliability ofpedestrian behaviors in the virtual world; and face validity, as mea-sured by participants’ self-reported perception of realism withinthe virtual world. We believe demonstrated validity of VR wouldoffer scientific rationale to use it as a pedestrian safety researchand training tool.

2. Methods

2.1. Participants

One hundred two children ages 7–9 were recruited from com-munity advertisements and a laboratory database of familiesinterested in child safety research (29 age seven (28%), 39 age eight(38%), 34 age nine (33%); average age = 8.59 years, S.D. = 0.88). Thesample included 54 girls (53%) and 48 boys (47%), and was mod-erately diverse racially (72% Caucasian, 27% African-American, 2%other ethnicities). The sample included individuals from a relativelywide range of socioeconomic status (median household income inthe $60,000–$80,000 range, with 16% of the sample having house-hold income of less than $40,000 and 32% of the sample havinghousehold income greater than $100,000).

Seventy-four adult participants were recruited from undergrad-uate psychology classes. Average age of the adult participants was21.76 years (S.D. = 7.09; range = 17–52; median = 19). The adult sam-ple was overrepresented by women (70%) and was moderately

diverse racially (66% Caucasian, 23% African-American, 7% otherethnicities, 4% chose not to report).

The research protocol was reviewed and approved by theuniversity ethics board. Adults and children’s parents providedsigned informed consent to participate in the study; children pro-vided assent, as developmentally appropriate. Children’s familiesreceived modest financial compensation for their time, and adultsreceived credit as one option to complete course requirements.

2.2. Protocol

Children completed 7 street crossings in each of two simu-lated streetside pedestrian situations: the “shout” technique andthe “two-step” technique, described below. They also completed8 street crossings in the virtual reality environment. Adults com-pleted the same simulated streetside crossings as children, andalso crossed the real road 7 times. Order of pedestrian tasks wasrandomly determined across participants.

All crossings, including those in the virtual world, occurred inthe same simulated environment, which was located at a moder-ately busy 2-lane bidirectional suburban road. The 25-ft crosswalk

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was located mid-block, with the nearest traffic light about a half-block away. The crossing was adjacent to an elementary school.

During experimental sessions, the average traffic volume was11.92 vehicles/min for adults and 10.65 vehicles/min for children.Those levels are associated with relatively high risk for pedestrianinjury (Roberts et al., 1995). The standard deviation of traffic volumewas 2.14 vehicles/min for adults and 2.77 vehicles/min for children,suggesting relative consistency across participants. Traffic in thevirtual world was set to travel at a constant speed of 30 miles/h,and to appear at an average density of 525 ft between vehicles(range = 300 ft); these figures were based on observations of actualtraffic at the site during after-school hours. The types of vehicles inthe virtual world were also programmed to match observations atthat site (e.g., 41% sedans, 20% SUVs, 16% pick-up trucks, etc).

All pedestrian behaviors were videotaped for later coding. Theprotocol for all four pedestrian tasks is detailed below.

In the “shout technique”, participants stood immediately adja-cent to the actual road with traffic. Yellow “caution” tape cordonedparticipants from traffic for safety. Participants were directed towatch for traffic as if they were actually going to cross the street,and to shout “Now!” when they deemed it safe to cross (Demetreet al., 1992). The shout technique offers the advantage of percep-tual accuracy – participants judge traffic from the location wherethey would if they were actually crossing the street – but suffersfrom the lack of motor initiation. Previous research suggests per-formance in the shout technique corresponds to other pedestriansafety measures (Demetre et al., 1992).

In the “two-step technique”, participants stand a short distanceaway from the curb of the actual road with traffic (Demetre et al.,1992). Again, the road was cordoned from participants using yellow“caution” tape. In this case, participants took two steps toward the

curb to indicate when they deemed it safe to cross. The two-steptechnique offers the advantage of initiated motor movement, butsuffers from subtle perceptual distortion since participants judgetraffic from a location a few steps away from the “normal” loca-tion to begin crossing a street. Nonetheless, previous work suggestspedestrian behavior in the two-step technique corresponds to thaton other pedestrian safety measures (Demetre et al., 1992).

Adults crossed the real street at a marked crosswalk. They wereinstructed to judge traffic and cross when they felt it safe. On thevery rare instances when a vehicle stopped to permit the partici-pant to cross (a common practice in some locales and required bylaw in many jurisdictions, but unusual practice for drivers in theregion where this study was conducted), the trial was replaced.

Crossing in the virtual reality environment paralleled thestreet-side simulations and the real-road task. Following twoexperimenter-led demonstration trials (including one in whichthe experimenter purposely was “hit” by a vehicle, to reduce par-ticipant curiosity to discover what happens when he/she is in acollision), participants stood on a simulated curb constructed fromplywood, and viewed the virtual environment on three monitorsarranged in a semi-circle in front of them (see Figs. 1 and 2).

Fig. 1. Photograph of the virtua

d Prevention 40 (2008) 1394–1400

Fig. 2. Photograph of a child participating in the virtual environment scenario.

Bidirectional traffic was displayed on the monitors to representthe density and types of vehicles recorded on the real road dur-ing after-school hours. Ambient and traffic noise was deliveredthrough speakers. When participants deemed it safe to cross thevirtual road, they stepped down off the curb, triggering a pres-sure plate connected to the computer. The virtual world changedfrom an immersive first-person perspective to a third-person per-spective, so that participants could then view themselves via anavatar (gender-matched) crossing the virtual street. Avatars’ walk-ing speed in the virtual environment was matched to participants’walking speed, as measured prior to the primary protocol in a dif-ferent location.

Following the avatar crossing, a cartoon character appeared toinstruct the participant on the safety of the crossing (one of twobrief positive responses was randomly selected for safe crossings;

cautionary responses were delivered by the cartoon character in thecase of “close calls” or collisions). In the case of a collision, the screenfroze briefly before the cartoon character appeared; in the case ofa successful crossing (including close calls), the avatar reached theopposite side of the street before the character delivered feedback.

Immediately following completion of the virtual environmentcrossings, adults (in written form) and children (orally) completedbrief reports of perceived realism of the crossing and simulatorsickness experienced while immersed in the virtual environment(details below).

While children completed the pedestrian measures, their par-ents completed a demographic report and a temperament measure(details below). Adults completed a brief demographic report.

2.3. Measures of pedestrian safety

Three measures, adapted from previous research (Barton andSchwebel, 2007; Demetre et al., 1992; Lee et al., 1984), were com-puted to assess safety of the pedestrian crossings in all settings: (a)average gap size available (average temporal gap between the timethe participant safely crossed the street and the arrival of the next

l environment scenario.

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demonstrated adequate internal reliability for all three scales (.71,.89, and .75, respectively).

3. Results

Table 1 shows descriptive data addressing face validity of thevirtual environment. First, we considered how real children andadults viewed the environment to be. As shown, all adult ratingswere near 4, or “quite realistic”. The overall rating of 4.22 suggests arating between “quite” and “completely realistic”. Children’s ratingswere slightly lower, but had a larger standard deviation, suggestingmany children rated the simulator similarly to adults but a smallpercentage felt the virtual environment was somewhat less real.

Table 1 also lists ratings of simulator sickness. All ratings werequite low, suggesting no or only very mild symptoms of motionsickness among both children and adults who participated.

Table 2 offers descriptive data for both the child and adult sam-ples on all pedestrian measures of interest. Not surprisingly, thedata demonstrate adults showed more advanced, and arguably

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vehicle into the crosswalk; for simulated crossings in the two-stepand shout techniques, the time was computed between entry intothe street and the arrival of the next vehicle); (b) average wait timeover cars passed (average time waiting to cross the street divided bynumber of cars that pass during that waiting); and (c) average startdelay (time after a car passes and before participant initiates cross-ing, which in previous research has been found to be magnified inyoung children (e.g., Thomson et al., 2005) and may represent cog-nitive processing time). In addition, two measures were computedfrom the virtual reality task only: (a) errors (instances when theparticipant would have been hit by a vehicle had he/she been onthe actual road); and (b) close calls (instances when the temporalgap between the participant and an oncoming vehicle was less than1 s).

Similar to previous research (Barton and Schwebel, 2007;Plumert and Schwebel, 1997), outliers were removed prior to aggre-gation of behaviors within streetside and real-road tasks becausethey appeared to represent traffic anomalies rather than typicalbehavior. Outliers were defined as individual trial scores greaterthan 3 standard deviations above or below the mean, and werepresent only for continuous variables (i.e., gap size available, waittime by cars passed, and start delay). To remove outliers, weexamined the mean and standard deviation of all individual trialsand removed outliers before trials were aggregated into aver-ages across a task. Within each task and behavior (e.g., waitingtimes for two-step technique), there were 7 trials and 104 child/74adult participants, for a total of 728 data points for children and518 for adults. On average, 11.17 trials of the data points (1.53%;range = 0–16) were dropped for children and 7.50 trials (1.45%;range = 0–18) for adults. Outliers were not removed from the vir-tual environment data because traffic anomalies did not appear inthat environment.

Following removal of outliers, behaviors were averaged acrosstasks. That is, all start delays within the shout technique wereaveraged into an average start delay score for each child’s shouttechnique. Therefore, each child had 3 scores (gap available, waittime, start delay) by 3 tasks (shout, two-step, virtual environment)and 2 scores (errors, close calls) for the virtual environment taskonly. Each adult had 3 scores by 4 tasks (shout, two-step, real road,virtual environment), plus the 2 scores for the virtual environmenttask only.

2.4. Interrater reliability

Interrater reliability in coding pedestrian behaviors was assuredby having 2 researchers independently code 22% (adult) and 19%(children) of the samples. Reliability for all measures was very high(all rs ≥ .95). Differences were resolved by using data from the pri-mary coder, who coded all data.

2.5. Simulator realism

Immediately after pedestrian crossings were completed in thevirtual environment, adults completed a brief questionnaire on theperceived realism of the virtual environment. Children completeda shorter but similar questionnaire orally with a researcher. In bothcases, questions about the perceived realism of the environmentwere answered on a 5-point Likert scale, from 1 (not realistic at all)to 5 (completely realistic).

2.6. Simulator sickness

Also immediately following the virtual environment cross-ings, adults completed the Simulator Sickness Questionnaire (SSQ;Kennedy et al., 1993), which lists 16 physical symptoms (e.g.,

d Prevention 40 (2008) 1394–1400 1397

fatigue, headache, sweating) that individuals sometimes experi-ence while participating in simulators or virtual environments.Items are answered on a 5-point Likert scale from 0 (no symp-toms) to 4 (severe symptoms), and the instrument yields goodinternal consistency in a 3-factor structure (oculomotor, disorien-tation, and nausea; Kennedy et al., 1993). Children completed anabbreviated oral version of the SSQ with an experimenter; to easeadministration of the moderately complex instrument, children’sanswers were recorded dichotomously (symptom not present (1)or symptom present (2)).

2.7. Temperament measures

While children completed the pedestrian tasks, parents ofchildren completed the Temperament in Middle Childhood Ques-tionnaire (TMCQ; Simonds and Rothbart, 2004), a 157-iteminstrument designed to assess children’s temperament. The TMCQproduces 17 subscales; of particular interest in this study werethree: assertiveness (behaviors representing an individual whois socially assertive and dominant, and who prefers to lead andtake charge in groups), impulsivity (behaviors representing speedof response initiation, including in situations when behavior isinappropriate or not permitted), and inhibitory control (behaviorsrepresenting the ability to restrain or inhibit impulses to act, includ-ing in novel, uncertain, or prohibited situations). Cronbach’s alpha

safer, behavior on nearly all measures of interest. To further inves-tigate developmental issues, we divided the child sample into three

Table 1Face validity, ratings of virtual world realism and simulator sickness by children(N = 102) and adults (N = 74)

Variable Adult ChildMean (S.D.) Mean (S.D.)

Simulator realism, overall 4.22 (0.63) 3.25 (1.44)Realism of characters 3.88 (0.79) Not assessedRealism of vehicles 3.96 (0.75) 3.33 (1.52)Realism of scenery 3.84 (0.88) Not assessedRealism of sound 4.10 (0.93) 3.86 (1.64)Realism of timing/movement 4.42 (0.67) 3.41 (1.51)

Simulator sickness, total 1.02 (1.92) 1.08 (0.11)Nausea 0.08 (0.13) 1.06 (0.10)Oculomotor 0.13 (0.25) 1.13 (0.19)Disorientation 0.06 (0.20) 1.05 (0.15)

Note: Realism ratings made on 5-point scale from “not at all realistic” [1] to “com-pletely realistic” [5]. Adult sickness ratings made on a 4-point scale from “nosymptoms” [0] to “severe symptoms” [4]. Child sickness ratings made on a dichoto-mous scale of “yes, symptom present” [2] or “no, symptom not present” [1].

sis and Prevention 40 (2008) 1394–1400

Table 4Internal reliability: correlations among virtual reality measures, children (N = 102;above diagonal) and adult (N = 74; below diagonal) samples

Variable 1 2 3 4

1. Hits/close calls – −.32** −.21* .032. Gap size available −.32** – .56** .27**

3. Wait time by traffic density −.04 .58** – .33**

4. Start delay −.06 .17 −.01 –

Note: Child data appears above the diagonal and adult data below the diagonal. Agepartialed for correlations with children.

* p < .05.** p < .01.

Table 5Construct validity: correlations among virtual reality and streetside measures, chil-dren’s start delay (N = 102; above diagonal) and adult gap size available (N = 74;below diagonal)

Variable 1 2 3

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Table 2Construct validity, differences between child (N = 102) and adult (N = 74) behavior inthe virtual world

Variable Adult ChildMean (S.D.) Mean (S.D.) t

Hits 0.03 (0.16) 0.19 (0.50) −2.63**

Close calls 0.11 (0.31) 0.25 (0.54) −2.10*

Gap size available 9.28 (1.24) 9.62 (1.20) −1.81Wait time by traffic density 1.04 (0.92) 1.80 (1.55) −3.75**

Start delay 0.84 (0.37) 1.35 (0.52) −7.11**

* p < .05.** p < .01.

groups, by age (7 vs. 8 vs. 9), and entered those three groups intoone-way ANOVA equations, along with the adult sample. Table 3illustrates these results. As shown, the 7-year-old children had sig-nificant more hits than the adults. Both the 7- and 9-year-old groupswaited longer to cross than the adults. The start delay variableshowed the most differences, with all developmental groups vary-ing from all others except the 7- and 8-year olds. Together, thesefindings indicate validity of the virtual environment by reflectingthe developmental process of pedestrian safety, with younger chil-dren making more errors and requiring more time to process streetsafety than older children and adults.

Table 4 presents data concerning internal reliability of the vir-tual environment. Note that data concerning children appear abovethe diagonal in the table, and data concerning adults below thediagonal. Note also that hits were rare in both samples, so hits andclose calls were summed into a single outcome measure for this andsubsequent analyses. As shown, participants who displayed riskypedestrian behavior on one measure also displayed risky behaviorson other measures. The primary exception is the correlations with

start delay in the adult sample; this result is partly because adultshave mastered pedestrian decision-making and therefore have uni-versally short start delays, which caused the measure to have poorvariance.

Table 5 presents data concerning construct validity of the virtualenvironment, as measured by correlations between behavior in thevirtual world and behavior in the real world. Again, data concern-ing children appear above the diagonal and data concerning adultsbelow the diagonal. The criterion measures chosen for this test werethose that are the most salient measures of risk-taking, by develop-mental level; and that have good variance (for example, we did nottest construct validity using measures of hits or close calls becausethose were relatively rare events, even among the children). Foradults, we used the gap size available after the adult successfullycrossed the street. As shown, this measure correlated moderatelywell across pedestrian tasks; behavior in the real world was sim-ilar to that in the virtual world. Just one correlation, between thetwo-step technique and the virtual road, did not quite reach tra-ditional levels of statistical significance (r (72) = .22, p = .07). Forchildren, we used the delay in starting as the criterion measure;this variable also correlated well across tasks, with statistical signif-

Table 3Construct validity, differences between children age 7 (n = 29), age 8 (n = 39), age 9 (n = 34

Variable Age 7 Age 8Mean (S.D.) Mean (S.D.)

Hits 0.31 (0.71)a 0.15 (0.43)Close calls 0.31 (0.54) 0.23 (0.63)Gap size available 9.68 (1.34) 9.65 (1.29)Wait time/traffic density 2.18 (1.72)b 1.56 (1.37)Start delay 1.57 (0.67)def 1.26 (0.45)dg

Subscripts (a–h) refer to statistically significant (p < .05) differences using Tukey HSD pos** p < .01.

1. Virtual road – .52** .42**

2. Two-step technique .22 – .67**

3. Shout technique .34** .43** –4. Real road-crossing .25* .49** .31**

Note: Child data appears above the diagonal and adult data below the diagonal. Agepartialed for correlations with children.

* p < .05.** p < .01.

Table 6Convergent validity: correlations between child temperament and virtual realitymeasures (N = 102)

Pedestrian behavior Inhibitory control Impulsivity Assertiveness

Hits/close calls −.24* .26** .19*

Gap size available .27** −.26** −.25*

Wait time by traffic density .03 −.15 −.23*

Start delay .09 −.10 −.24*

Note: Age partialed.* p < .05.

** p < .01.

icance throughout, and with stronger effect sizes than in the adultsample.

Our final test of the validity of the virtual environment wasan examination of convergent validity with parent-reported mea-sures of child temperament (see Table 6). As shown, impulsivityand inhibitory control, the two temperament traits most closelyrelated to child injury risk in previous work (see Schwebel andBarton, 2006, for review), were correlated to both hits/close callsand gap size available. Not surprisingly, impulsive and uninhibitedchildren had more hits/close calls and smaller gap sizes available.Assertiveness, a temperament trait not studied carefully in previ-ous work on child injury, partly because it is typically not measureduntil early adolescence, also emerged as a strong predictor of pedes-trian behavior. More assertive children displayed riskier pedestrianbehaviors.

), and adults (N = 74) behavior in the virtual world

Age 9 AdultMean (S.D.) Mean (S.D.) F

0.12 (0.33) 0.03 (0.16)a 3.72**

0.24 (0.43) 0.11 (0.31) 1.659.54 (0.98) 9.28 (1.24) 1.151.75 (1.59)c 1.04 (0.92)bc 5.98**

1.25 (0.40)eh 0.84 (0.37)fgh 21.08**

t-hoc tests.

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envision broad installation of pedestrian VR systems in schools,

D.C. Schwebel et al. / Accident Analy

4. Discussion

Results suggest that the virtual environment was valid. Con-struct validity is suggested by the correlated behavior in the virtualand real worlds among both the child and adult samples. The resultsalso offer evidence of internal reliability and convergent and facevalidity. Together, the findings suggest immersive, interactive vir-tual reality environments might be an appropriate methodologyfor both etiological research on the causes of pediatric pedestrianinjuries and for intervention research designed to study virtualreality as a tool to train children in pedestrian safety.

4.1. Generalizability

One prominent question the results raise is whether the findingsin one virtual environment will generalize to other virtual envi-ronments. This is inherently an empirical question that we cannotanswer definitively, but at least three aspects of this question maybe considered: immersion, interactiveness, and realism.

Virtual environments offer a wide range of immersion, from fullimmersion in “cave” or goggles environments to complete non-immersion from a 2-dimensional (e.g., overhead) perspective ona single computer monitor. Our environment was immersive, butoffered some external cues; this level of immersion was chosenbecause it offers a level of immersion that permits valid transfer-ence to the real world, but includes enough external cues to reducerisk of motion sickness that is troublesome in full-immersionsystems. Our hunch is that similar levels of validity would bediscovered in more immersive systems, but not less immersiveones.

A second aspect of generalizability is the interactive aspect ofvirtual environments. Face validity is greater when participantsinteract with the virtual environment—the participant’s movementtransfers to movement in the virtual world. Less interactive sys-tems rely on mouse-clicks, joystick movement, or button-pushes,and probably sacrifice validity by doing so. Our system is unique inthat the interactive nature of the virtual environment was linkedto a training tool—participants experience first-person immersionwhile they judge safety of the street, and then step down off thecurb in an interactive manner. Upon reaching the lower curb, oursystem transfers the perspective from first to third person, offeringthe opportunity for learning.

Would the validity we discovered transfer to systems thatoffer more or less interaction? At one extreme are head-mounted

systems, where the participant is placed in an open room and per-mitted to walk anywhere within the virtual environment. On thesurface, such a system is highly valid—real-world walking trans-fers to movement within the virtual environment. The limitationis technological: if simulation is accurate, we would expect a highlevel of validity. If the simulation inaccurately represents move-ment, validity might be sacrificed. At the other extreme are systemswithout interactive features. We would anticipate loss of validity insuch systems.

A third and final aspect of generalizability is that of simulation.Quality of computer graphics ranges widely in virtual environ-ments. Our simulation was rated as highly realistic by lay adults,and that perceived realism likely translates to validity of a system.Less realistic simulations are likely to suffer from reduced validity.

4.2. Limitations, future directions, and application of VR

Like all research, this study suffered from limitations. The sam-ple sizes were modest; the pediatric sample was limited to childrenages 7–9; and the VR system simulated only one pedestrian envi-ronment. Future work should consider generalizability to other

d Prevention 40 (2008) 1394–1400 1399

populations and to other dangerous street environments such asurban intersections, rural highways, and so on.

Despite these limitations, we believe that the findings suggestvirtual reality can be used as a valid tool to understand and preventchildren’s pedestrian injuries. Our system proved valid in multiplemeasures of internal reliability and face, construct, and convergentvalidity. Future research might proceed with the expectation thatvirtual environments offer a reasonable proxy of real-world envi-ronments, and therefore children’s pedestrian behavior in a virtualworld will validly approximate their behavior in real-world pedes-trian settings.

This discovery opens tremendous opportunity for application ofVR in etiological and intervention research. From the perspectiveof etiological research, the various aspects of cognitive, perceptual,and motor development that are required for safe street-crossingcan be examined in a safe virtual setting. The influences of distrac-tion (e.g., talking on the phone, having emergency vehicles passby), social cues (e.g., peers, parents), and other intrapsychic, inter-personal, and environmental variables on safe pedestrian behaviorcan be examined. Developmental issues in pedestrian safety can beparsed apart; specifically, what aspects of cognitive, motor, and per-ceptual ability are required to negotiate street environments safely?Do most children possess those abilities at age 6 or are those abili-ties that do not typically develop until age 9 or 10? Similarly, whydo particular children (e.g., boys, children with ADHD) have ele-vated risk of pedestrian injury and how might that knowledge helpus develop intervention programs for at-risk populations?

Validation of VR also has implications for intervention research.Existing behavioral strategies to train children in safe pedestrianbehavior achieve only moderate success (Duperrex et al., 2002).The most successful interventions are streetside training strategies,conducted by teachers or parents in a one-on-one model at actualpedestrian environments (e.g., Rothengatter, 1984). Such strategiesare very time- and labor-intensive, but offer children repeatedpractice at the cognitive, perceptual, and motor tasks required forsafe pedestrian behavior. Virtual reality offers the same opportu-nity without placing children at risk in real traffic. In fact, virtualenvironments offer the opportunity for unlimited unsupervisedpractice, increasing difficulty level tailored to individual children’sabilities, computerized feedback on the safety of crossing, and theopportunity to create distraction, simulate local environments,and manipulate traffic patterns. As VR becomes technologicallysimpler and economically more feasible to develop, we might

libraries, religious institutions, and other locations where childrenspend time.

Acknowledgements

This project was supported by the UAB Injury Control ResearchCenter at the University of Alabama at Birmingham through a grantfrom the National Center for Injury Prevention and Control, Cen-ters for Disease Control and Prevention, Award R49/CE000191 anda cooperative agreement with the Federal Highway Administration,Project No. ICRC (1)/PL 106-346. Thanks to the following individualsfor help with data collection and coding: Songul Abay, Liz Bowl-ing, Katie Byington, Lauren Craig, Aaron Davis, Danielle Dulion,Stephanie Feil, Joanna Gaines, Meredith Hope, Libby Kongable,Jerome Morgan, Alana Pearson, Nina Reynolds, Rudy Roussel, JennaSmith, Despina Stavrinos, Jennifer Simpson, Janene Wasson. Thanksto the following for help with software development and sup-port: Corey Bruse, Aeron Gault, Shayne Gelo, Sasha Gorbach, MattSchikore, Rick Vanderleest. Thanks to Jack Carter, Jon Remley, andthe Masons at Shades Valley Lodge No. 829 F. & A.M. for use of theirbuilding to collect data.

sis an

study. British Medical Journal 310, 91–94.

1400 D.C. Schwebel et al. / Accident Analy

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