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The crash involvement of older drivers is associated with their hazard
perception latencies
Article in Journal of the International Neuropsychological Society · September 2010
DOI: 10.1017/S135561771000055X · Source: PubMed
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This is the accepted version of this journal article:
Horswill, Mark, Anstey, Kaarin J., Hatherly, Christopher, & Wood,
Joanne M. (2010) The crash involvement of older drivers is associated
with their hazard perception latencies. Journal of the International
Neuropsychological Society, 16(5), pp. 1‐6.
© Copyright 2010 The International Neuropsychological Society
Horswill – Older drivers’ hazard perception and crash involvement 1
Running head: OLDER DRIVERS’ HAZARD PERCEPTION AND CRASH INVOLVEMENT
The crash involvement of older drivers is associated with their hazard perception latencies
Mark S. Horswill1*; Kaarin J. Anstey2; Christopher G. Hatherly2; Joanne M. Wood3
1School of Psychology, University of Queensland, Brisbane, Queensland, Australia
2Ageing Research Unit, Centre for Mental Health Research, Australian National University,
Canberra, ACT, Australia
3School of Optometry, Queensland University of Technology, Brisbane, Queensland, Australia
Word count abstract: 195
Word count main text: 3670
Corresponding author:
Dr Mark S. Horswill
School of Psychology
University of Queensland
St Lucia, Brisbane
QLD 4072
Australia
Telephone +61 (7) 334 69520
Fax +61 (7) 336 54466
E-mail: [email protected]
Horswill – Older drivers’ hazard perception and crash involvement 2
Abstract
Hazard perception in driving is the one of the few driving-specific skills associated with crash
involvement. However, this relationship has only been examined in studies where the majority of
individuals were younger than 65. We present the first data revealing an association between
hazard perception and self-reported crash involvement in drivers aged 65 and over. In a sample
of 271 drivers, we found that individuals whose mean response time to traffic hazards was
slower than 6.68 seconds (the ROC-curve derived pass mark for the test) were 2.32 times (95%
CI 1.46, 3.22) more likely to have been involved in a self-reported crash within the previous five
years than those with faster response times. This likelihood ratio became 2.37 (95% CI 1.49,
3.28) when driving exposure was controlled for. As a comparison, individuals who failed a test
of useful field of view were 2.70 (95% CI 1.44, 4.44) times more likely to crash than those who
passed. The hazard perception test and the useful field of view measure accounted for separate
variance in crash involvement. These findings indicate that hazard perception testing and training
could be potentially useful for road safety interventions for this age group.
Mesh terms: automobile driver examinations, aged, aging, automobile driving standards, traffic
accidents, motor vehicles.
Horswill – Older drivers’ hazard perception and crash involvement 3
Introduction
In the context of road safety research, it can be argued that the most compelling statistic
of whether any behavioral measure is worthy of investigation is whether it is associated with
crash risk, given that the reduction of crashes is the key goal of the field. The most direct
measure of crash risk is an individual’s crash involvement. The problem is that crash
involvement is fraught with methodological and psychometric problems when used as an
indicator of a driver’s risk of crashing (where “risk of crashing” is viewed as a trait that we want
to predict using behavioural and other measures).
One manifestation of this problem is that crash involvement is notoriously inconsistent
over time. Work reviewed by Elander et al. (1993) indicates that a correlation of around 0.3
between crash rates over two consecutive time periods is typical. This correlation could be
viewed as a test-retest measure of the psychometric reliability of crash involvement (when used
as a measure of a driver’s risk of crashing), which would be considered poor. This lack of
reliability is likely to be due to a number of factors. First, crashes are rare events. For example,
Evans (1991) estimated that the average driver has one crash every 10 years. That is, one needs
to recruit hundreds of drivers to gather even a modest sample of recently crash-involved
individuals. Second, all methods of recording crashes are problematic (Elander, et al., 1993). For
example, for self-reported measures, it has been demonstrated that drivers forget (or increasingly
fail to report) crash involvement at a rate of about 30% per year (Maycock, 1991). On the other
hand, police crash records only tend to sample more serious incidents. For example, Anstey,
Wood, Caldwell, Kerr, and Lord (2009) found that while 22.3% of a sample of older drivers
reported a crash within the previous five years, only 3.2% had police crash records. Third,
crashes are typically caused by multiple factors, including chance. That is, involvement in a
Horswill – Older drivers’ hazard perception and crash involvement 4
crash does not necessarily mean that an individual is a poor driver or even that they are at
particularly high risk of crashing again: the crash may not have been the driver’s fault.
In light of these factors, it is perhaps surprising that any statistically reliable association
between crash involvement and any single behavioral measure has been found. Despite all this, a
number of studies have found performance in hazard perception tests to be associated with crash
involvement.
Hazard perception in the context of driving can be defined as the ability to anticipate
potentially dangerous situations on the road ahead. It is typically measured using video-based
tests and has been found to correlate with previous crash involvement in a number of studies
involving cross-age samples (Darby et al., 2009; McKenna & Horswill, 1999; Quimby et al.,
1986). It has also been found to predict certain crash types prospectively in novice drivers (Wells
et al., 2008).
Hazard perception tests have been reported to distinguish between novice and
experienced drivers, consistent with the substantial differences in crash rates between these
groups (Horswill et al., 2008; Smith et al., 2009). Quimby and Watts (1981) tested a cross-age
sample and found that hazard perception was fastest for mid-age drivers (35-54 years) and
slowest for both young drivers (<25 years) and older drivers (>65 years). Horswill et al. (2009)
found that healthy old-old (75-84) drivers were significantly slower at hazard perception than
healthy young-old (65-74) and mid-age (35-55 years) drivers where the latter groups did not
differ (groups were matched for education level, gender, and vocabulary).
We present the first data in which the relationship between hazard perception and selfreported crash rates has been examined in a sample of drivers aged 65 and over. It is important to
consider older drivers separately because the mechanisms underlying hazard perception have
Horswill – Older drivers’ hazard perception and crash involvement 5
been argued to be different to that of younger populations (Horswill et al., in press). For younger
drivers, inexperience is likely to be the key factor mediating hazard perception ability, consistent
with the novice/experienced driver differences previously noted. In contrast, older drivers are not
usually hampered by lack of experience: many have been driving for over half a century. Instead,
Horswill et al. (2008) proposed that, for older drivers, hazard perception ability was likely to be
mediated by age-related cognitive, sensory, and motor deficits. In a sample of healthy drivers
aged 65 and older, they found that hazard perception ability was associated with individual
differences in useful field of view, contrast sensitivity, and simple reaction time.
It is not a foregone conclusion that hazard perception will be associated with crash rates
in older drivers. For example, older drivers are known to moderate their driving to compensate
for perceived deficits by avoiding driving at night, during peak-hour traffic, and during bad
weather, as well as limiting the distance driven (Keeffe et al., 2002). It is conceivable that these
strategies could compensate for increases in crash risk resulting from poor hazard perception.
If an association is found then it would provide (1) a strong indicator of validity for the
type of hazard perception test used, in the sense that it would be shown to be associated with a
real world safety outcome, and (2) an imperative to justify the investigation of hazard perception
as an approach to improving road safety. As a comparison, we also included an established
measure found to be associated with crash involvement in older adults across a number of
previous studies, namely a version of the useful field of view (De Raedt & PonjaertKristoffersen, 2000; Goode et al., 1998; Owsley et al., 1998; Owsley et al., 1991; Sims,
McGwin, Allman, Ball, & Owsley, 2000).
Method
Horswill – Older drivers’ hazard perception and crash involvement 6
Participants
A sample of 2707 individuals aged 65 years and over were selected at random from the
local electoral roll and invited to take part in the study if eligible (participants were required to
be active drivers). Three hundred and eight (11.38%) drivers volunteered to take part and 271
drivers provided complete data on hazard perception, self-reported crash involvement, driving
frequency, and kilometers driven per week. Of those who did not provide complete data (1) 10
individuals failed to complete at least 50% of the items in the hazard perception test (due to
motion sickness), (2) 15 individuals failed to adhere to the hazard perception test instructions, (3)
four individuals did not attempt the hazard perception test at all, (4) 17 left the kilometers driven
item blank, (5) 13 left the driving frequency item blank, and (6) 10 left the crash involvement
item blank (note that many individuals fell into multiple categories). The final sample for
analysis was comprised of 271 drivers, aged between 65 and 96 years (M = 74.84, SD = 6.88;
34.3% female), who reported driving an average of 188 km per week (SD 143) and had been
driving for 52.83 years (SD 8.42, range 12 to 75). 23.6% of the sample indicated that they had
been involved in at least one crash over the previous five years (this is consistent with the figure
of 22.3% found by Anstey et al. (2009) in their previous self-reported crash study). 68.3% of the
sample reported that they drove every day. Participants gave informed consent and the study had
ethical approval from the Australian National University.
Materials and Procedure
Participants completed a shortened version of a video-based measure of hazard
perception (the ACT hazard perception test). The full length test has previously been validated
(Wetton, et al., in press) via its ability to (1) distinguish between novice and experienced drivers,
(2) correlate with other measures of hazard perception, (3) correlate with age in a sample of older
Horswill – Older drivers’ hazard perception and crash involvement 7
drivers, and (4) correlate with measures that have been found to be associated with crash risk in
older drivers, namely Useful Field of View and contrast sensitivity (Owsley et al., 1991).
The ACT hazard perception test involved participants viewing video footage of real
traffic situations filmed from the driver’s perspective. In the present study, the footage was
displayed on a 32” LCD touchscreen. Participants viewed unstaged potential traffic conflicts (a
traffic conflict was defined as an incident in which the camera car might have to slow or steer to
avoid a collision with another road user). Participants were required to touch any road user
(stationary or moving vehicles, cyclists, or pedestrians) that could be involved in a traffic conflict
with the camera car. They were asked to respond as early and as quickly as possible. In one
example scene (Figure 1), the camera car is travelling along a freeway and an on-ramp joining
the freeway becomes visible through trees. A truck is travelling along this on-ramp and it is
possible to predict that the truck will join the freeway and come into conflict with the camera car.
Drivers with good hazard perception ability would be expected to anticipate this conflict from
early cues (e.g. the trajectory of the truck) but drivers with poor hazard perception ability would
be expected to respond only when the truck pulls into the path of the camera car. Note that this
test was specifically designed as a response time measure and not as a hit rate measure. Clips
were chosen to fulfill this remit (for example, the clips were selected so that most drivers would
be likely to respond eventually). This was to avoid the ambiguity associated with missing
responses, which could be due to drivers (1) not seeing the hazard or (2) seeing the hazard but
not considering the event worth responding to. With the current approach, we could be confident
that there was a general consensus among participants that each event was indeed hazardous.
[INSERT FIGURE 1 HERE]
Horswill – Older drivers’ hazard perception and crash involvement 8
Due to time constraints in the testing session, a shortened version of the ACT hazard
perception test was created using 22 items (out of the 68 items in the original test). Items were
selected to maximize the magnitude of novice/experienced differences, quality of image, and
quality of traffic conflict (e.g. whether the traffic conflict could be regarded as ambiguous),
while minimizing miss rates and replication of scene content. The test was scored by calculating
the mean response time to the 22 incidents: item raw scores were converted into z scores,
averaged (items where participants did not respond were excluded), and then converted into an
overall response time using the mean and SD of responses from all participants across all scenes
(this conversion back to a response time was done to aid interpretation of outcomes).
Participants also completed a measure of Useful Field of View (UFOV). This was
assessed using a measure based on subtest two of the PC version of the UFOV® test (Edwards, et
al., 2005). This subtest involves rapid presentation of dual targets: a white stylized outline figure
of either a car or a truck in the centre of the screen, and a car figure located at a 10cm radius (on
screen) from the point of fixation at one of the eight cardinal or intercardinal locations (i.e., N,
NE, E, SE, S, SW, W). Note that this test differs slightly from the standard UFOV® test (Ball &
Owsley, 1993) as, in the PC version of the test, targets were presented at a single distance from
fixation, which tends to lead to faster threshold estimates than with the standard test (see
Edwards, et al., 2005). In the present study, the screen size was larger (32”) than that used in the
original PC version of the test (17”) but the image was adjusted so the stimuli were the same size
on the screen as in the original tests. Following stimulus presentation and a random noise mask,
participants were required to make a discrimination response to the central target (“was it a car or
a truck?”), and a localization response to the peripheral target (“at which of the 8 peripheral
locations did it occur?”). A double staircase procedure adjusted the presentation duration (in
Horswill – Older drivers’ hazard perception and crash involvement 9
intervals of 16.66ms, starting at 250ms) until six reversals (i.e., correct to incorrect response or
vice versa) had been recorded, and threshold speed was calculated as the average of the
presentation durations at the last four reversals. This subtest and version of the UFOV® has been
shown to be highly correlated with previous versions (Edwards, et al., 2006), and to have similar
high reliability and validity (Edwards, et al., 2005).
Participants indicated how often they drove per week (five point scale, labeled “once a
week”, “1-2 times per week”, “2-3 times per week”, “3-6 times per week”, and “every day”), the
number of kilometers driven per week, and whether they had been involved in a traffic accident
as a driver within the previous five years. Note that Anstey et al. (2009) described evidence
indicating that retrospective self-reported crashes over five years may be a better measure of
crash risk than state crash records for older drivers in an Australian sample. Participants also
completed a battery of cognitive and vision tests that were not analyzed in the present article.
Results
Alpha was set at 5%. The internal consistency of the shortened hazard perception test was
estimated by inserting means at the item level for any missing responses (a conservative strategy)
and was found to be acceptable (Cronbach’s alpha = .87). A logistic regression was carried out
with self-reported crash involvement as the dependent variable and mean hazard perception
response latency as the independent variable and a significant association was found, Odds Ratio
= 1.40, 95% CI 1.04, 1.89, p = .028. The mean hazard perception response time was 5.49
seconds (SD 0.91) for the 207 crash-free drivers and 5.79 seconds (SD 1.04) for the 64 crashinvolved drivers. A second logistic regression also included driving frequency and kilometers per
week in order to control for exposure. To reduce skew, driving frequency was converted into a
Horswill – Older drivers’ hazard perception and crash involvement 10
dichotomous variable (drive every day versus drive six times per week or less) and a logarithmic
transformation was applied to kilometers driven per week. When these two variables were
included in the logistic regression, the effect of hazard perception response times on crash
involvement remained significant, Odds Ratio = 1.42, 95% CI 1.04, 1.93, p = .026. The hazard
perception/crash involvement effect also remained significant when age and sex (potential
mediating variables) were included as additional covariates, Odds Ratio = 1.50, 95% CI 1.08,
2.10, p = .016.
As described above, the hazard perception test was designed apriori as a response time
measure rather than a hit rate measure. Nonetheless we conducted another logistic regression
using the proportion of clips that participants responded to as the independent variable, to see
whether this had any association with crash involvement. No significant association was found, p
= .449.
The Useful Field of View (UFOV) measure was transformed (square root) to minimize
skew and was also entered into a logistic regression with crash involvement as the dependent
variable (note that there were 12 individuals in the present sample who did not complete the
UFOV). UFOV was found to be significantly associated with crash involvement, Odds Ratio =
1.09, 95% CI 1.02, 1.16, p = .009, where the crash-free drivers obtained a mean threshold of
118ms (SD 96) and the crash-involved drivers obtained 155ms (SD 110). As with hazard
perception, we conducted two more logistic regressions controlling for driving frequency and
kilometers per week in order to control for exposure, and then additionally controlling for age
and sex. The effect of UFOV on crash involvement remained significant in both cases (Odds
Ratio = 1.10, 95% CI 1.02, 1.17, p = .008; Odds Ratio = 1.11, 95% CI 1.03, 1.20, p = .006,
respectively).
Horswill – Older drivers’ hazard perception and crash involvement 11
The correlation between UFOV and the hazard perception test score was significant, r =
.29, n = 259, p < .001. In order to determine whether the hazard perception and the UFOV tests
could account for unique variance in accident involvement independent of one another, we
conducted a further logistic regression, with hazard perception response time, UFOV threshold,
driving frequency, kilometers per week, age, and sex as independent variables and crash
involvement as the dependent variable. The effects of both hazard perception, Odds Ratio = 1.43,
95% CI 1.02, 2.001, p = .040, and UFOV, Odds Ratio = 1.10, 95% CI 1.02, 1.19, p = .013, on
crash involvement remained significant.
Both the hazard perception and UFOV scores were converted into dichotomous pass/fail
variable to aid in interpretation of the effect sizes. A ROC curve analysis was used to define the
pass mark, where crash involvement was the state variable. The pass mark for the hazard
perception test was chosen to be 6.682 seconds (12.5% of the sample responded slower than this
cut off and hence failed the test), which was the point on the ROC curve at which the sum of
sensitivity and specificity was highest, and was selected to maximize discrimination between the
crash-involved and crash-free groups (De Monte et al., 2007). Using the same technique, the pass
mark for the UFOV measure was chosen to be 48.33 ms (67.9% of the sample had a threshold
higher than this value and hence failed the test). Note that the UFOV pass mark would not be
considered to be a clinically practical cut off when using the UFOV to determine fitness-to-drive
(the pass mark selected represents very good performance): it was calculated purely to allow us
to calculate a crash-involvement effect size that was comparable with the effect size obtained
from the hazard perception test.
Horswill – Older drivers’ hazard perception and crash involvement 12
Hazard perception test pass/fail was entered as a dichotomous variable into a logistic
regression to predict crash involvement. For ease of interpretation we converted the odds ratios
produced by the logistic regression analysis into likelihood ratios using the formula provided by
Zhang (1998). Hazard perception test outcome was associated with crash involvement with a
likelihood ratio of 2.32 (95% CI 1.46, 3.22) This indicated that individuals who failed the ACT
hazard perception test were 2.32 times more likely to self-report a crash during the previous five
years compared with those who passed (see Table 1 for the frequency table). The likelihood ratio
controlling for driving frequency and kilometers per week was 2.37 (95% CI 1.49, 3.28).
We completed the same procedure for the UFOV measure (see Table 2 for the frequency
table). Individuals who failed the UFOV test were 2.70 times (95% CI 1.44, 4.44) more likely to
have reported a crash than those who passed. The likelihood ratio became 2.77 (95% CI 1.47,
4.55), when driving frequency and kilometers per week were controlled for. When hazard
perception, UFOV, driving frequency, kilometers per week, age, and sex were entered together
as independent variables, the likelihood ratio became 2.52 (95% CI 1.56, 3.48) for hazard
perception and 2.95 (95% CI 1.53, 4.86) for UFOV.
[INSERT TABLES 1 AND 2 HERE]
Discussion
We found a significant association between self-reported crash history and hazard
perception ability in a sample of older drivers; the first time such a relationship has been
reported. This effect is not mediated by age, sex, or driving exposure. The magnitude of the
effect found compares favorably with a previously-established measure known to be associated
with crash risk, namely useful field of view. Also, hazard perception and useful field of view
accounted for variance in crash involvement independent of one another. One potential avenue
Horswill – Older drivers’ hazard perception and crash involvement 13
for further research would be to gather information about the details of participants’ crashes (for
example, whether they were at-fault or whether hazard perception was likely to have been a
factor in the crash, etc), in order to reduce noise in the data that might be suppressing the crash
relationships.
To give an idea of the implications of the 0.3s response time difference between the
crash-involved and crash-free drivers, this would translate into 5 meters of additional travel when
driving at 60 kph, which could plausibly translate into the difference between having and not
having a crash. This suggests that hazard perception ability could be a factor in explaining the
crash risk of older adults. Of course, as with any correlational study, the possibility remains that
this difference might not reflect a causal relationship or that the causality might be in the
opposite direction to that proposed, where the experience of crashing somehow results in a
decline in hazard perception. However the latter does not seem particularly plausible
(remembering that we controlled for driving exposure): one would presume that it would be
more likely that the experience of crashing would lead to drivers being more vigilant and
responsive, which would counteract the relationship found. In contrast, there are theoretical
reasons for expecting poor hazard perception would lead to greater crash risk: if a driver is slow
to anticipate dangerous events on the road ahead then they would be expected to be less likely to
avoid them, potentially resulting in a collision with an object or another vehicle.
The findings have implications for driving research, driver assessment, and driver
training and establishes the hazard perception test as a valid measure of driving performance, in
that it is associated with on-road safety outcomes. Video-based hazard perception tests have a
number of advantages over real-world measures of driving, including (1) the ability to present
rare (we estimate that each of the 22 events shown took 1-2 hours of driving in normal traffic to
Horswill – Older drivers’ hazard perception and crash involvement 14
obtain) and potentially dangerous events in a short time frame with no risk to the participant or
examiner, (2) a high level of experimental control (all participants experience the same stimuli),
and (3) a relatively low cost (the test can be run on a standard computer with a touch-screen
attached). It is possible that the hazard perception test, combined with other measures, could be
useful as an assessment of fitness-to-drive for older adults.
In terms of safety interventions, Horswill et al. (in press) found that the hazard perception
scores of a sample of older drivers could be improved by a short video-based training
intervention. While it is not yet possible to say whether this type of training would generalize to
changes in actual crash risk, the present findings give grounds for optimism that changing
performance in a hazard perception test may yield beneficial real world outcomes.
Horswill – Older drivers’ hazard perception and crash involvement 15
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Acknowledgements
This research was funded by the Australian Research Council and NRMA-ACT Road
Safety Trust (Linkage Grant LP0668078). We acknowledge the help of Ada Tam and Amy
Dawel in collecting, entering, and cleaning the data and Mark Wetton for his contribution in
creating the ACT hazard perception test. We confirm that the information in this manuscript and
the manuscript itself has never been published either electronically or in print and that none of
the authors has any financial or other conflict of interest.
Table 1
Frequencies for crash-involvement by hazard perception test (HPT) outcome
Crash-involved Crash-free Totals
Passed HPT 48 189 237
Failed HPT 16 18 34
Totals 64 207 271
Table 2
Frequencies for crash-involvement by Useful Field Of View (UFOV) outcome
Crash-involved Crash-free Totals
Passed UFOV 8 67 75
Failed UFOV 53 131 184
Totals 61 198 259
Figure 1
An example scene from the ACT hazard perception test (note that the original stimuli were
presented in colour and were of higher resolution)
Figure Legends
Figure 1
An example scene from the ACT hazard perception test (note that the original stimuli were
presented in color and were of higher resolution)
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