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Accident Analysis and Prevention 39 (2007) 886–893

Where did that car come from?: Crossing the road whenthe traffic comes from an unfamiliar direction

Lucy Johnston a,∗, Victoria Peace b

a Department of Psychology, University of Canterbury, Private Bag 4800, Christchurch, New Zealandb University of Bath, Bath, United Kingdom

Received 15 August 2006; received in revised form 10 December 2006; accepted 18 December 2006

bstract

Using a virtual road crossing environment, the reported research investigated the road crossing behavior of 12 male pedestrians in familiar andnfamiliar environments. Environment familiarity was manipulated using traffic direction. Seven of the participants were from a country whereraffic flows from right to left and five were from countries were traffic flows from left to right. Each participant was asked to cross the road when

raffic was coming from both the familiar and the unfamiliar direction for them. Results showed that pedestrians had lower safety ration, or a lower

argin of error, in crossing the road when traffic was flowing in an unfamiliar direction, suggesting that pedestrians might be at greater risk ofccident in such environments. Implications of these findings are discussed.

2007 Elsevier Ltd. All rights reserved.

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eywords: Pedestrian road crossing; Virtual reality

Pedestrian safety is an important social and economic issue.fter drivers and their passengers, pedestrians are the largestroup of road users to be killed or injured in road traffic acci-ents. Approximately 12% of all fatalities, and 8% of injuries,n New Zealand traffic accidents are pedestrians (LTSA, 2002).n urban roads this figure extends to 28% of fatalities. Further-ore, pedestrians are more likely to be at fault in accidents than

re other road users (LTSA, 2001). Investigation into the safetyudgments and behaviors of pedestrians may offer insight intoow to increase the effectiveness of safety campaigns and toeduce pedestrian accidents.

Travelers and migrants are especially vulnerable as pedes-rians, particularly those in countries where the traffic flow isn the opposite direction to that of their home country. The USepartment of State (2003) reported that more than 200 US

itizens die overseas in traffic accidents every year, with theajority of these fatalities being non-motor vehicle occupants

uch as pedestrians. Specific warnings are issued with regard

o countries in which the traffic travels on the left-hand sidef the road (the opposite side to the US), as US citizens arenvolved in more accidents in these regions. With international

∗ Corresponding author. Fax: +64 3 364 2181.E-mail address: lucy.johnston@canterbury.ac.nz (L. Johnston).

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001-4575/$ – see front matter © 2007 Elsevier Ltd. All rights reserved.oi:10.1016/j.aap.2006.12.010

ravel increasing, understanding and preventing such accidentsas become increasingly important.

Despite this, we are unaware of any investigation of roadrossing behavior that has considered the impact of changes innvironment, such as changes in traffic direction. In the presentesearch we conducted an exploratory investigation into whetherchange in the familiarity of the environment results in increased

oad crossing errors in adult pedestrians. Environment familiar-ty was manipulated through the direction of traffic flow. In theamiliar condition, traffic flowed in the direction with which thearticipant is familiar and in the unfamiliar condition, in thepposite direction.

The ability to cross a road safely is a perceptual-motor skillDemetre et al., 1992, 1993; Lee et al., 1984) in which the pedes-rian must assess whether a given gap in a flow of traffic affordsafe crossing. To do so involves perception of the size of theraffic gap in terms of time to act. Judgment about a given gap islways relative to the perceiver (e.g., a given gap may afford saferossing for a fast but not for a slow walking pedestrian) and tourrent constraints (e.g., a pedestrian requires a larger gap whene or she is carrying a heavy load). The activity of crossing a road

nvolves correctly perceiving the time-to-arrival (TTA) of anpproaching vehicle and judging whether that TTA affords saferossing. In order to be able to make comparisons between indi-iduals with different walking speeds and between crossings by

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L. Johnston, V. Peace / Accident Ana

he same individual under different conditions, researchers haveuggested the use of a unit-free ratio, of the time gap betweenehicles and a walker’s time-to-cross as a measure of safetyClancy et al., 2006; Owen et al., 2003; Simpson, 2002). Thisafety index is independent of an individual’s walking speed andence allows for meaningful comparisons between experimen-al conditions and between pedestrians. This index is used in theresent research.

Traffic provides the same optical information to the pedes-rian whether is it traveling from the left or from the right, andence poses the same challenges for the pedestrian in decidinghen it is safe to cross the road. Crossing the road in a familiar

nvironment becomes a habitual, or mindless, behavior and ahange in traffic direction may disrupt this habitual behavior.e are interested in investigating how this disruption influences

he road crossing behavior of the pedestrian. Even with traf-c flowing in an unusual direction, however, the road crossingituation retains many of the dynamic constraints of a famil-ar road crossing. Accordingly, the ability to transfer previouslyearned information (from the familiar environment) should beigh (Flach et al., 1990; Holding, 1991). Differences betweenhe familiar and unfamiliar environments are, then, predicted toe greatest when comparing the first block of trials from eachraffic direction.

Although the impact of changes to the environment has noteen considered in the road crossing domain, Murray (2003)as recently investigated the impact of changes to the self. Henvestigated the impact of a temporary impairment to mobilityaused by wearing a leg brace, on pedestrian’s road cross-ng behavior. Initially there was a decrease in pedestrians’afety ratio, but this was followed by rapid adaptation to theirew (lack of) mobility and improvement in crossing ability.t is likely that a similar pattern would emerge in the presenttudy.

A concern for researchers in this domain is the exposure ofarticipants in the research to physical risk. Researchers haveecently suggested the use of a virtual reality (VR) system forhe investigation of road crossing behavior and for use in safetynterventions (Clancy et al., 2006; Murray, 2003; Owen et al.,003; Simpson, 2002; Simpson et al., 2003). Experimenters canontrol the nature of the virtual environment, but the partic-pant has complete control over his or her actions, as in theeal world (Blascovich et al., 2002). Mistakes can be madeithout physical danger. The visual and auditory experiencef being “hit” by a virtual car is the same as being hit by aeal car but without physical risk. Participants have been showno display natural responses (e.g., increased walking speed;inching) when “hit” by vehicles in the virtual environment,

ndicating immersion in the environment (Murray, 2003). Fur-hermore, the nature of road crossing errors made by childrenn the virtual environment was similar to those made in theeal environment (Connelly et al., 1996, 1998; Simpson et al.,003).

The present research was conducted in an immersive vir-ual environment. The virtual environment was manipulated torovide participants with both a familiar and an unfamiliar envi-onment, as specified by direction of traffic movement.

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and Prevention 39 (2007) 886–893 887

. Method

.1. Participants

Twelve male students volunteered to participate in return for$5 voucher redeemable at campus stores. Seven (aged 18–29ears) were from New Zealand, where traffic travels on the left-and side of the road, and had never visited a country in whichhe traffic travels on the right-hand side of the road. The otherve participants (aged 21–29 years) had very recently arrived inew Zealand (mean time in New Zealand was 2.8 weeks, with a

ange from 1 to 8 weeks) from countries where the traffic travelsn the right hand side of the road (four participants were fromermany and one from the USA). These participants had notreviously visited or lived in a country in which the traffic travelsn the left-hand side of the road. For one group of participantshe familiar environment was when traffic was traveling on theeft-hand side of the road and the unfamiliar environment whenraffic was traveling on the right-hand side of the road and forhe other group of participants, the reverse. Given that they hadeen in New Zealand for some time prior to participation inhe reported research, those participants from countries whereraffic travels on the right-hand side of the road will likely haveained some experience of traffic traveling on the left-hand sidef the road. The possible impact of this experience is discussedurther below.

.2. Virtual environment

The virtual environment was generated by a 800 MHz Pen-ium III PC with 128 MB of RAM and a 32 MB Riva TNT2 3Draphics accelerator card and viewed through a Virtual Researchystems V8 Head Mounted Display that contains two full-color.3 cm × 640 × 480 pixel active matrix liquid crystal displaysith a display rate of 60 frame/s, presenting a 48◦ horizontal and0◦ diagonal field-of-view to each eye. The system included a 6-egree-of-freedom head tracker (Ascension Technology Flockf Birds with extended range transmitter) with an orientation andosition sample rate of approximately eight-times-per-second.he virtual environment consisted of a straight, flat section of

oad, a traffic island, a tree, sky, roadside grass, and vehicles.he road width was 6 m and was marked with continuous whitedge lines and dashed central white stripes. All vehicles wereodelled on a van, 1.74 m in width and 4.38 m in length, and

ach was randomly assigned one of four colours—orange, red,ellow or white. The participant did not have a visible pres-nce in the virtual environment (i.e. participants could not seehemselves).

.3. Design

The experiment consisted of 36 experimental trials and eightamiliarization trials. The first two familiarization trials occurred

n normal environment with the HMD helmet resting on the par-icipant’s head. The participant’s walking speed in the normalnvironment was recorded from these trials. The next six trialsccurred within the virtual environment, but with no traffic on

8 lysis and Prevention 39 (2007) 886–893

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mtrThey were told to treat the virtual road as if it were an actual road.Participants were told that they could move around by walkingand could look around by turning their head. Each trial was ter-

1 In a uniform distance trial, the separation distance between any two vehicleswhen the first vehicle was in line with the participant was constant. The separa-

88 L. Johnston, V. Peace / Accident Ana

he virtual road. The participant was asked to walk across the vir-ual road at a normal speed in three trials, then at a rushed speedn three trials. The shortest time to cross was used to calibrateubsequent experimental trials, and therefore to standardize theehicles times-to-arrival across participants. Recent studies inur laboratories have scaled the time-to-contact of an oncom-ng vehicle to each participant’s walking speed (Owen et al.,003; Simpson, 2002) so that the opportunities for road cross-ng are set to the same levels for each participant. The time toross to a safe position in the training trials is used to individu-te inter-vehicle gaps in the experimental trials, while ensuringhat the crossing opportunities provided to each participant werehe same in terms of the ratio of time available to cross theoad given their individual walking speeds. Use of fixed inter-ehicle intervals would likely have provided some participantsith more viable road crossing opportunities than other partici-ants. The time to cross in training is calculated from when thearticipant is 0.5 m in front of the starting position at the sidef the road until they have crossed the road to centre. The ini-ial 0.5 m movement was included to avoid false trial initiationsue to body sway and/or false starts by participants. Times-to-rrival of the vans are then calculated according to the followingormula:

ime to arrival = shortest time to cross in training

× (1 + (TTCFactor × (VanNo − 1))).

Time to arrival is the time available to cross from when therevious van just passed the participant until the next one willrrive at the intended crossing path. VanNo is the van numbernot including the first van). The TTCFactor effectively sets thencrease in time to arrival for each subsequent van and the num-er of vans selects the range of values. This equation meanshat the shortest van time to arrival will be equal to the short-st time to cross in training (for van number 1). In the presenttudy the TTCFactor was set to 0.15, based on past researchMurray, 2003; Owen et al., 2003). This TTCFactor ensuredhat the intervals between the vans were of sufficient magnitudeo be noticeable and to offer different crossing opportunitieso participants while at the same time being sufficiently smallo allow the point at which a gap changes from affording saferossing to not affording safe crossing to be identified. If, forxample, the shortest time for a participant to cross the road inhe training trials was 3.0 s, then that participant would get vanimes to arrival of 3.0, 3.45, 3.90, 4.35 and so on.

In each of the 36 experimental trials, the participant was pre-ented with a two-lane road on which cars were moving in oneane. Fig. 1 shows one frame of a typical view seen by the par-icipant looking to their right towards the approaching traffic.he task of the participant was simply to cross the virtual road –

iterally walk as if they were crossing an actual road – when theyelieved it was safe to do so. An instruction message appeared

n the visor before each trial, and when each trial was complete,o direct the participant to the correct position. For increasedealism, and provision of negative feedback, virtual collisionsere accompanied by a crash sound and near misses by a loudorn honk.

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ig. 1. View seen by the participant from the starting position at the side of theoad. The corner of the traffic island can be seen at bottom left.

Each experimental trial consisted of a series of 11 vehiclesoving along a virtual road. The first vehicle in each trial passed

y the participant within the first 1.5 s, ensuring that the par-icipant would collide with a vehicle if they crossed the roadmmediately after the trial started without first looking for traf-c. There were two different types of trial: uniform speed andniform distance trials. In a uniform speed trial all vehicles in theraffic flow had the same speed, and in a uniform distance trialll vehicles were the same distance apart.1 There were three lev-ls of both uniform speed and uniform distance. In the uniformpeed trials the vehicles traveled at either 40, 50 or 60 km/h.2 Inhe uniform distance trials the inter-vehicle distance was either5, 75 or 85 m. This resulted in six unique trials. A block of tri-ls consisted of one presentation of each of these six trials, withhe order of presentation of trials randomized for each partici-ant within each block. For each road direction there were threelocks of trials. Hence, for each participant, there were 18 trialsn the familiar and 18 in the unfamiliar condition. Half of thearticipants first completed the familiar and half the unfamiliarlock of trials.

.4. Procedure

Participants were tested individually by a female experi-enter and completed eight familiarization and 36 experimental

rials. Participants were told that the task involved crossing theoad to the traffic island, when they thought it was safe to do so.

ion distance between vehicles with different velocities changes over time. Themportant separation however, as far as the participant wishing to cross the roads concerned, is the separation between the vehicle that has just passed them andhe next approaching vehicle.

2 Note that the NZ speed limit for urban roads is 50 km/h.

L. Johnston, V. Peace / Accident Analysis and Prevention 39 (2007) 886–893 889

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ohsfratio was higher for the familiar than for the unfamiliar direction(Ms = 1.61 versus 1.48). There was no difference in the safetyratio for those participants who first saw traffic from the familiar

ig. 2. A bird’s eye view of the central portion of the road crossing environment.vehicle is approaching the participant who is at the starting position by the

ide of the road.

inated when the participant had crossed the road to the centref the traffic island. At this point participants were given verbalnstructions to turn around and returned to the starting position,ndicated by a tree in the virtual environment. There were noehicles on the road during the return crossing. Fig. 2 shows aird’s eye view of the road crossing situation. The instructions totart the trial and to turn around were recorded and automaticallylayed through the HMD headphones.

.5. Dependent measures

The following measures were calculated for each trial:

. Safety ratio: The ratio of the available crossing time fromwhen the participant moves 0.5 m from the starting pointdivided by the time taken to cross to the far edge of the van.The safety ratio captures the sensitivity of the pedestrian towhether a given gap affords safe or unsafe crossing relative tothe effectiveness of taking advantage of each opportunity tocross or choosing not to do so. A safety ratio above 1 indicatesa safe road crossing, a ratio below 1 an unsafe crossing.

. Incidence of unsafe crossings: The number of crossingswhere a participant was either hit, or within 0.5 s of beinghit (a ‘near miss’).

. Incidence of cautious crossings: The number of crossingswhere participants waited until all the vehicles have passedbefore crossing the road.

. Percentage of gap used: The percentage of the available gapused by the participant.

. Walking speed: The speed with which the participant crossedfrom 0.5 m to the far edge of the lane.

. Results

Preliminary analyses revealed no effect of country of origin,r more specifically which traffic direction was familiar to partic-pants, and hence is not included in the reported analyses. Thisnding suggests that the initial experience of the participantsew to New Zealand with traffic driving on the left hand side

f the road prior to their participation in the experiment wasot sufficient to differentiate their performance from the Newealand participants who had had no experience with traffic on

he right hand side of the road.

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Fig. 3. Safety ratio as a function of order and direction.

Consistent with previous research (Connelly et al., 1996,998; Simpson et al., 2003), there were differences on the depen-ent measures as a function of trial type. Participants had a higherroportion of unsafe road crossings in the uniform distance thanhe uniform speed trials (Ms = 0.45 versus 0.05). Participantslso had a higher safety ratio (Ms = 1.91 versus 1.29) and usedgreater proportion of the available gap (Ms = 79.6% versus

7.2%) in the uniform speed than the uniform distance trials.s there were no interactions between trial type and any of

he other variables, trial type was not included in the reportednalyses.3

For each of the dependent measures, two analyses were con-ucted. First, a 2 (direction: familiar/unfamiliar) × 2 (order:sual first/usual second) × 3 (block: 1/2/3) ANOVA withepeated measures on the first and third factors was conducted.n addition, an analysis was conducted to compare road crossingerformance after an unsafe and a safe crossing. That is, safetyatio, % gap used and walking speed were analyzed as a func-ion of whether the previous road crossing had been a safe or annsafe crossing.

.1. Safety ratio

For each participant the mean safety ratio within each blockas calculated. The 2 (direction: familiar/unfamiliar) × 2 (order:sual first/usual second) × 3 (block: 1/2/3) ANOVA yielded aain effect of block, F(2, 18) = 10.89, p < .001. The safety ratioas significantly lower in the first than in the second and thirdlocks which did not differ from one another (Tukey HSD,< .05; Ms = 1.51 versus 1.63 and 1.67).

There was also a significant interaction between direction andrder, F(1, 9) = 6.52, p < .05. This effect is shown in Fig. 3. Postoc comparisons showed that direction of traffic only effects theafety ratio for those participants who first saw traffic comingrom the unfamiliar direction. For these participants the safety

irection (Ms = 1.63 and 1.70).

3 Full details of these analyses can be obtained from the corresponding author.

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Mean safety ratios were calculated for trials that followed safend unsafe road crossings. These were analyzed by a 2 (direc-ion: familiar/unfamiliar) × 2 (previous crossing: safe/unsafe)epeated measures ANOVA. This revealed a main effect of previ-us crossing, F(1, 10) = 7.81, p < .05. The safety ratio was higherfter safe than unsafe crossings (Ms = 1.64 versus 1.47).

.2. Incidence of unsafe road crossings

.2.1. Collisions and tight fitsThere were 50 collisions and 60 tight-fits, giving a total of 110

25.5%) of unsafe crossings. All participants had some unsaferossings among their 36 experimental trials, with a mean of.17 unsafe crossings per participant and a minimum of 3 andaximum of 17.The proportion of unsafe crossings was calculated for each

articipant as a function of block, and subjected to a 2 (direction:amiliar/unfamiliar) × 2 (order: usual first/usual second) × 3block: 1/2/3) ANOVA. This revealed no significant effects.

For each participant the proportion of their unsafe road cross-ngs that followed immediately after another unsafe crossingnd after a safe crossing were calculated. A 2 (direction: famil-ar/unfamiliar) × 2 (previous crossing: safe/unsafe) ANOVAevealed a main effect of previous crossing, F(1, 10) = 10.37,< .01, that showed a higher proportion of unsafe crossings to

ollow after a previous unsafe crossing (Ms = 0.31 versus 0.19).his effect was, however, qualified by a significant directiony previous crossing interaction. In the familiar traffic directionhere was no difference in the proportion of unsafe crossingshat followed after safe and unsafe previous crossings (Ms = 0.2nd 0.27). In the unfamiliar traffic direction, however, there wassignificantly greater proportion of unsafe crossings followingther unsafe crossings than following safe crossings (Ms = 0.36ersus 0.18).

.2.2. Cautious crossings4

On 29 trials (6.71%) the participant did not cross the roadntil all the cars in the traffic flow had passed. Five of the 12articipants had a least 1 cautious crossing amongst their exper-mental trials, with the mean number amongst those five being.8 (16.1%) trials, with a minimum of 1 and a maximum of 18rials. Twenty-four of the cautious crossings were in the unfamil-ar traffic direction and just five in the familiar traffic directionondition. Sixteen of the cautious crossings were on uniformistance trials and 13 on uniform speed trials.

.3. Percentage of gap used

For each participant the mean percentage gap used withinach block was calculated. This was then subjected to a

4 Waiting until all the cars had passed before crossing the road produced a saferossing in this experimental paradigm. Generalizing this strategy to the real-orld however, would be at best undesirable, and at worst unsafe. Trying to crossbusy road at a peak time by waiting for there to be no traffic in sight would be

mpractical, and may lead to frustration and the selection of inappropriate gaps.cautious crossing strategy is not viable as the sole road crossing strategy; it is

mportant for pedestrians to learn strategies that can be employed when traffics present.

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and Prevention 39 (2007) 886–893

(direction: familiar/unfamiliar) × 2 (order: usual first/usualecond) × 3 (block: 1/2/3) ANOVA. This yielded marginally sig-ificant effects of direction, F(1, 9) = 3.86, p < .09 (Ms = 70.2%ersus 66.6% for the unfamiliar and familiar directions, respec-ively), and block, F(2, 18) = 3.01, p < .07 (Ms = 66.4%, 68.4%nd 70.5%, respectively).

A 2 (direction: familiar/unfamiliar) × 2 (previous crossing:afe/unsafe) ANOVA revealed only a main effect of previousrossing, F(1, 10) = 5.47, p < .05. Participants used a greater per-entage of the available gap when crossing after a previouslyafe than after a previously unsafe crossing (Ms = 71.38% versus5.97%).

.4. Walking speed

For each participant the mean walking speed within eachlock was calculated by dividing the width of the road byhe time it took for participants to cross. A higher score,herefore, indicates a faster walking speed. A 2 (direction:amiliar/unfamiliar) × 2 (order: usual first/usual second) × 3block: 1/2/3) ANOVA yielded a significant effect of block, F(2,0) = 3.37, p < .05. Walking speed was slower in the first blockhan in the second and third blocks of trials (Ms = 2.10 versus.22 and 2.25 m/s).

A 2 (direction: familiar/unfamiliar) × 2 (previous crossing:afe/unsafe) ANOVA revealed no significant effects. Partici-ants walking speed did not differ as a function of the outcomef the previous road crossing trial.

. Discussion

The reported research sought to investigate male pedestrianoad crossing ability in a familiar and an unfamiliar environment.nvironment familiarity was manipulated by varying trafficirection, so that the traffic came from either a familiar or annfamiliar direction for the participants. The primary focus ofhe research was on the impact of traffic direction on road cross-ng. We will also briefly consider our data in terms of roadrossing behavior in general, and in terms of learning effects.

Our results did indeed point to some differences in roadrossing behavior in familiar and unfamiliar environments. Theafety ratio was lower in unfamiliar trials, when those trials werencountered first. That is, if the first traffic direction that partici-ants encountered in the experiment was the unfamiliar directionhen the safety ratio was lower than in subsequent trials whenhe traffic came from a familiar direction. This effect cannote explained simply as a practice effect, with improved perfor-ance (higher safety ratio) for the second traffic direction, since

here was no corresponding increase in safety ratio in the secondraffic direction for those participants who first encountered traf-c traveling in the familiar direction. It would appear then that

he deficit in performance in an unfamiliar environment relativeo a familiar one is most apparent when that unfamiliar envi-

onment is encountered “cold”, that is without being precededy the familiar environment. Of course this is typically the sit-ation encountered by tourists and migrants; they do not havehe opportunity to practice on familiar roads immediately before

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L. Johnston, V. Peace / Accident Ana

hey encounter the unfamiliar environment. Although there waso difference in the proportion of unsafe road crossings in theamiliar and unfamiliar environments in our study, lower safetyatios are more likely to lead to unsafe road crossings as themargin of error” in the crossing is smaller. Also, in the unfamil-ar direction, the percentage of unsafe crossings that followed

previous unsafe crossing was greater than that following aafe crossing. No such difference was seen in the familiar traf-c direction, however, suggesting that participants may showetter adaptation to making errors in familiar than unfamiliarnvironments.

Our dependent measures do offer some insights into theature of the adjustments made by participants in the unfamiliarnvironment. Participants made more cautious crossings in thenfamiliar than in the familiar traffic direction and they usedgreater percentage of the available gap in the traffic in the

nfamiliar direction. Cautious crossings involve waiting for allhe traffic to pass before attempting to cross the road. Althoughhis could be considered to be a particularly safe road cross-ng strategy – wait until no traffic is visible before crossing – its not one that can easily be transferred to real-world settings.n many roads waiting for such a road crossing opportunityould involve a very long wait. This in turn may then leadedestrians to become frustrated and attempt to cross in an inap-ropriate gap. Waiting for traffic to disappear before crossinghe road is also not a very useful learning strategy as it doesot provide any opportunity for feedback, from tight-fits or theike. Hence, when waiting for all the traffic to pass by is not

viable crossing option, pedestrians who tend to adopt suchn approach may be poorly equipped to make good alternativerossing decisions.

Using a higher percentage of an available gap in traffic maye a good crossing strategy, for example pedestrians use a higherercentage of the available gap in uniform speed trials in whichhey also have a higher safety ratio and fewer unsafe crossingsn comparison to uniform distance trials. It would suggest thatarticipants are not rushing to cross safely, but rather are using allhe time available, selecting gaps which afford safe crossing withreasonable margin of error. It would appear then, that facedith an unfamiliar road crossing environment, our participants

dapted their road crossing behavior somewhat. However, whileome of these adaptations (e.g., using a higher proportion of thevailable gap) were adaptive, others (e.g., increased number ofautious crossings) were non-adaptive.

This is the first study to explore road crossing behavior in annfamiliar environment. The data are consistent with the acci-ent statistics that show an increased rate of accidents amongstedestrians visiting countries where traffic travels in an unfamil-ar direction. Future research should also attend to the decision

aking processes involved in road crossing. Anecdotal reportsf road crossing in a foreign country describe “looking the wrongay for travel” as a major error. Our research did not investigatehether this did indeed occur and, if so, whether it was linked to

nsafe road crossings. It should be noted that the overall rate ofollisions in our study was somewhat higher than that expectedrom accident statistics (LTSA, 2002), suggesting that partici-ants might have been less cautious overall in their road crossing

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and Prevention 39 (2007) 886–893 891

n the virtual environment than in real road crossing situationsSimpson et al., 2003). It is difficult to ascertain the incidencef tight-fits in everyday road crossing situations, since these areot recorded in official accident statistics as collisions are, bute might speculate that the incidence of tight-fits is also ele-ated in our research relative to actual road crossing situations.lthough we must be cautious, then, in generalizing the rate ofnsafe road crossings from the reported research to everydayettings, the pattern of those unsafe crossings is less likely toe affected by being in a virtual environment. If, in the future,irtual environments might be utilized for road safety trainingt might be advantageous that overall unsafe crossing rates arelevated in this environment as it provides more opportunity foreedback to be provided during training.

In addition to familiar and unfamiliar environments, we alsoncluded both uniform distance and uniform speed trials in ouresearch. As in previous studies (Connelly et al., 1996, 1998;impson et al., 2003), we showed participants to have a greaterroportion of unsafe crossings in uniform distance than in uni-orm speed trials. Further, participants had a higher safety ration the uniform speed than in the uniform distance trials. Partici-ants used a higher percentage of the available gap in the uniformpeed than the uniform distance trials. Overall, our results againuggest that people generally use distance as a guide to saferossing gaps, and do not take full account of vehicle speed.his finding provides some direction for those designing inter-entions to improve road safety and road crossing decisions.raining programmes need to educate individuals to pay atten-

ion to speed and time-to-collision information in road crossingnd not to rely simply on inter-vehicle separation as a guide toafe road crossing.

Our data can also speak to participant’s learning, both as aunction of practice but also as a function of feedback fromnsafe crossings (provided aurally). The inclusion of threelocks of trials for each traffic direction in the present studyllowed us to consider whether learning occurred as a functionf practice across the relatively short time of the experiment.he safety ratio, the percentage gap used and walking speedependent measures all showed effects of trial block. On eachf these measures, participants showed better road crossing inhe second and third blocks of trials than in the first block. Partic-pants had a higher safety ratio, used a higher percentage of thevailable gap and walked faster in the second and third blocks ofrials. These effects are consistent with the participants learnings a function of practice at road crossing. It is noteworthy thathe enhanced performance occurs after just one block of trialsnd then asymptotes. It would appear, then, that the effects ofractice are very rapid within this environment. It is possiblehat increased familiarity with being in the virtual environmenttself may have contributed to these effects of block. Eight prac-ice trials (equivalent to two-thirds of an experimental block ofrials) were given to participants to familiarize them with theirtual environment and with wearing a head mounted display.

ll participants reported feeling comfortable at the end of theractice sessions. Further, the effects of experience or famil-arity evidenced by main effects of trial block on a number ofur dependent measures can explain the differences between

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92 L. Johnston, V. Peace / Accident Ana

amiliar and unfamiliar environments seen on our dependenteasures.That the effects of block were independent of traffic direction

s also encouraging, suggesting that pedestrians need relativelyittle practice in the unfamiliar environment in order to improveheir road crossing skills. Travelers and migrants are likely atreatest risk of accident soon after arrival in a country where traf-c flows in an unfamiliar direction. Of course, this is also a timehen travelers are most tired and most distracted by their unfa-iliar surroundings. Although relatively few trials were needed

n the experimental set-up for adaptation to the unfamiliar traf-c direction to occur, it must be acknowledged that this set-upas somewhat different from everyday road crossing situations.irst, the pedestrians focus was solely on the road crossing task,

here were no distractions, for example from other pedestrians,alking on a cell-phone (Murray, 2006). Second, the road cross-ng occurred in a concentrated block of trials making it easieror adaptation than if trials are more dispersed across time, as inveryday settings. Accordingly, it is likely that real-life adapta-ion may proceed more slowly. It is noteworthy that the non-Newealand participants in the reported research, who had been in

he country for an average of nearly 3 weeks prior to participa-ion in the study and hence had likely had some experience withnfamiliar traffic flow, performed similarly to the New-Zealandased participants who had no experience of unfamiliar flow.hird, our experiment used uniform speed and uniform distance

rials to isolate the effects of speed and distance on pedestrian’soad crossing judgments. In everyday settings, however, trafficoes not flow in streams of uniformly distanced vehicles or atniform speed. Accordingly, the road crossing judgments wille more complex in real-life settings than in our experimentalaradigm.

We also analyzed our data as a function of the previous roadrossing—whether it had resulted in a safe or an unsafe crossing.omewhat surprisingly, these data suggest that participants didot adapt to unsafe crossings. The safety ratio was higher afterafe than after unsafe crossings, the proportion of unsafe roadrossings was higher after unsafe than safe crossings and theercentage of the available gap used by participants was higherfter safe than unsafe crossings. Having an unsafe road cross-ng did not, then, appear to lead to any corrective behavior byarticipants to reduce the likelihood of subsequent unsafe cross-ngs. In fact, at least in the unfamiliar traffic direction, havingn unsafe crossing actually increased the likelihood of havingnother such unsafe crossing. We hypothesize that participantsould attempt to avoid unsafe crossings in our research set-uput that if an unsafe crossing does occur that it is somewhat dis-urbing or distracting to participants. Accordingly, participantshould be motivated to avoid such events. The distraction of annsafe crossing may, however, prevent participants from takingppropriate adaptive action in the subsequent road crossing. It is

lso possible, however, that participants were not disturbed byhe unsafe crossings but rather found these risky outcomes excit-ng and were not motivated to avoid collisions.5 Future research

5 We are grateful to an anonymous reviewer for this suggestion.

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eeds to explore these possibilities in order to better under-tand both pedestrian reactions to unsafe road crossings, andhe utility of a virtual environment for investigating road cross-ng behavior. If findings suggest that pedestrians are motivatedo avoid unsafe road crossings but do not respond adaptivelyo the feedback received in our experimental set-up, we woulduggest that safe road crossing programs need to involve morehan simple practice, even with feedback, at road crossing. Spe-ific instruction about how to make decisions regarding roadrossing, what information to attend to, needs to be provided toedestrians.

A final caveat to our research that is important to acknowledges that only male pedestrians participated in our study. Accord-ngly, caution should be exercised in generalizing the findingso female pedestrians. It is possible the females react differ-ntly to virtual environment technology than do males (Cairdnd Hancock, 1994; Manser and Hancock, 1996). Males, bothhildren and adults, also show a tendency toward greater injury-isk taking behavior (Jelalian et al., 1997; Morrongiello andennie, 1998) than females. These differences in risk-takingehavior have been attributed to differences in cognitive-basedactors such as risk appraisals and attributions (Morrongiellond Rennie, 1998), factors that could easily generalize to roadrossing situations (Simpson et al., 2003).

In sum, the present research provided an initial exploratorynvestigation of adult road crossing behavior as a function ofnvironment familiarity. The results point to interesting differ-nces as a function of the changing environment that necessitateurther investigation. In addition, this research could be extendedo consider other environmental changes, both permanent andemporary, within the road crossing situation such as changeso traffic flows (e.g., introduction of a traffic island) and roadorks.

cknowledgements

The authors would like to thank Pat Bodger for suggestinghis study be conducted, Gordon Simpson and Stephen Murrayor their technical assistance, and Michael Richardson for hiselpful comments. The research was supported by grant D3336rom the University of Canterbury.

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