characteristics of older drivers who adopt self-regulatory driving behaviours

Transportation Research Part F 9 (2006) 363–373

www.elsevier.com/locate/trf

Characteristics of older drivers who adopt self-regulatorydriving behaviours

Judith L. Charlton *, Jennifer Oxley, Brian Fildes, Penny Oxley,Stuart Newstead, Sjaanie Koppel, Mary O’Hare

Accident Research Centre, Building 70, Monash University, Victoria 3800, Australia

Abstract

This paper describes a survey of self-regulatory driving practices of 656 drivers aged 55 years and older. Types and prev-alence of self-regulatory behaviours were examined and key characteristics of self-regulators were identified. Overall, themajority of drivers reported being very confident in potentially difficult driving situations and relatively few avoided thesesituations. The most commonly avoided situations were driving at night (25%), on wet nights (26%) and in busy traffic(22%). There was a strong association between drivers’ avoidance of and confidence in specific driving situations (e.g. nightdriving) and ratings of relevant functional abilities (e.g. vision for night driving). Logistic regression modelling revealedthat those most likely to adopt avoidance behaviour were female, 75 years and older, not the principal driver in the house-hold, had been involved in a crash in the last 2 years, reported vision problems and had lower confidence ratings. Impli-cations for promotion of safe driving practices are discussed.� 2006 Elsevier Ltd. All rights reserved.

Keywords: Older drivers; Road safety; Fitness to drive; Functional impairment; Medical conditions

1. Introduction

It is generally assumed that older drivers ‘self-regulate’ their driving behaviour to minimise their risk ofcrashing. Self-regulation implies that drivers make adjustments in their driving behaviour that adequatelymatch changing cognitive, sensory and motor capacities. Examples of such behaviours include reduction indriving distance and avoidance of busy traffic and night driving. While there is evidence that older drivers gen-erally travel shorter distances on average than younger drivers (LTSA, 2000), evidence for widespread adop-tion of other types of self-regulatory practices and the factors that predict self-regulatory behaviours is lessdefinitive.

1369-8478/$ - see front matter � 2006 Elsevier Ltd. All rights reserved.

doi:10.1016/j.trf.2006.06.006

* Corresponding author. Tel.: +61 3 9905 1903; fax: +61 3 9905 4363.E-mail addresses: [email protected] (J.L. Charlton), [email protected] (J. Oxley), Brian.

[email protected] (B. Fildes), [email protected] (P. Oxley), [email protected](S. Newstead), [email protected] (S. Koppel), Mary.O’[email protected] (M. O’Hare).

mailto:[email protected]


mailto:Brian. [email protected]

mailto:Brian. [email protected]




mailto:Mary.O'[email protected]

364 J.L. Charlton et al. / Transportation Research Part F 9 (2006) 363–373

Researchers have proposed that older drivers are able to regulate their driving adequately and compensate forage-related declines by reducing their annual driving and regulating when and where they drive (Eberhard, 1996).While it is likely that many older drivers adjust their driving adequately to accommodate these changes, it is alsopossible that some fail to self-regulate appropriately and as a consequence, may be at a higher risk of crashinvolvement. A question of interest in this study is what are the characteristics of those who self-regulate and spe-cifically, do drivers with specific impairments change their driving to avoid potentially risky driving situations.

Several authors have reported on the self-regulatory practices of older drivers in Europe and North Amer-ica (e.g. Ball et al., 1998; Hakamies-Blomqvist & Wahlstrom, 1998; Holland & Rabbitt, 1992; Lyman, McG-win, & Sims, 2001; Rimmo & Hakamies-Blomqvist, 2002). Comparatively little, however, is known aboutdriving patterns of older Australian drivers and the effectiveness of these practices in reducing crash risk (Fil-des, 1997). Older driver licensing rates and reliance on driving for mobility and transportation in the Austra-lian population differs considerably from other OECD countries (OECD Expert Group, 2001) and it isreasonable to expect that the self-regulatory driving behaviours of Australian older drivers may differ fromtheir counterparts in other parts of the world.

The processes involved in self-regulation are complex and the factors that influence the adoption of self-reg-ulatory behaviours are likely to be multi-faceted. For the most part, the literature refers to these behaviouralchanges as compensatory, implying that older drivers change their behaviour in response to a loss of function.Others may choose to avoid difficult driving conditions as a common sense strategy to enhance their safety andminimise their risk of crashes, while for others, the decision to regulate when, where and how they drive mayreflect lifestyle choices.

It is suggested that older individuals adopt considerable caution when driving (Eberhard, 1996). Severalstudies have shown that most older drivers recognise that good vision is one of the most important elementsfor safe driving and often cite poor vision as a major factor in determining avoidance of driving at night or inpoor weather (Kostyniuk & Shope, 1998; Marottoli et al., 1993; Persson, 1993). Ball et al. (1998) have reportedstrong associations between objective measures of cognitive status as well as vision abilities and avoidance ofpotentially difficult driving situations. This suggests that at least some older adults are able to compensate wellfor limitations in their abilities.

In contrast, Rothman, Klein, and Weinstein (1996) argued that people of all ages are poor at recognisingthe relationship between their own actions and potential risks. This may lead to optimism about one’s invul-nerability, underestimation of risk and overestimation of one’s driving ability (Matthews, 1986). In support ofthis notion, several studies have demonstrated that some older drivers do not adequately compensate for age-related changes in vision and cognitive abilities when driving (e.g. Holland & Rabbitt, 1992; Stutts, 1998).Arguably, one would expect that those drivers who are unaware that they have reduced functional abilitieswill be less likely to avoid difficult driving situations than those who have insight into their capacities. Hollandand Rabbitt (1992) demonstrated that once drivers were made aware of their declining abilities, many adjustedtheir driving appropriately. Hence, drivers’ insight into their impairments appears to be a critical factor indetermining self-regulatory behaviour. This finding emphasises that while it may be useful to examine theextent to which objective measures of drivers’ functional capacities are able to explain their self-regulatorypractices, it is also informative to understand the link between self-regulation and drivers’ perceptions (self-assessment) of functional abilities.

The study reported here examined the prevalence and types of self-regulatory driving practices of a sampleof Australian drivers. Of interest was the relative influence of key variables on adoption of self-regulatorystrategies and in particular, whether drivers’ perceived health status and functional abilities were associatedwith self-regulation.

2. Method

This study surveyed 656 drivers, recruited through seniors’ newspapers, auto club magazines, seniors’ clubs,retirement villages and local government aged care services. Eligible participants were drivers aged 55 yearsand older living in the State of Victoria, Australia.

A questionnaire was designed to gather information on the driving patterns of older drivers, transportationneeds and decisions about driving cessation. The questionnaire was administered by a telephone interview.

J.L. Charlton et al. / Transportation Research Part F 9 (2006) 363–373 365

Items included questions about demographic variables (age, gender, place of residence etc.), current drivingpatterns, recent crash and infringement history, recent changes in driving (distance, speed and quality), andconfidence in and avoidance of potentially difficult driving situations. To assess health status and functionalabilities, drivers were asked to complete a checklist indicating the presence of specific medical conditions andto rate (excellent, good, fair and poor) their overall health and functional abilities for safe driving, includingvision for daytime driving, vision for night driving, speed of decision-making, upper and lower body strengthand head/neck movement. Five experienced telephone interviewers conducted the interviews.

This paper focuses on a sub-set of issues addressed in the questionnaire including driving patterns, confi-dence and avoidance of driving situations, medical conditions, functional abilities and history of crashes andinfringements.

Descriptive statistics were used to summarise the demographic characteristics, driving patterns and fre-quency of different types of self-regulatory practices amongst the sample. Chi-square analyses were conductedto explore the relationship between self-regulatory behaviours (e.g. avoidance of specific driving situations)and variables of interest including age group, gender, and drivers’ ratings of confidence and functional abil-ities. In addition, multivariate logistic regression modelling was used to describe the characteristics of thosewho adopted self-regulatory practices. The dependent measure for the regression analyses was defined in termsof avoidance of any driving situations (that is, self-regulators were considered to be those who avoided any ofthe specified driving situations and non-self-regulators those who did not avoid any situations). Althoughother self-regulatory practices were examined in this study, it was not practical or statistically desirable toexplore all of these dependent variables using regression modelling. The avoidance variable provided someuseful insights into characteristics of drivers who engaged in avoidance of selected driving situations identifiedin the literature as potentially difficult for older drivers. An important assumption for the interpretation of thisanalysis is that avoidance of specific driving situations that are deemed difficult or risky, reflects a self-regu-latory behaviour that enhances driver safety.

3. Results

A total of 656 respondents described themselves as ‘current drivers’ and all held a valid driver’s licence.Twenty-six percent of participants were aged 55–64 years, 38% were aged 65–74 years and 36% were aged75 years and older. Sixty-five percent of drivers were males. The majority of drivers (61%) indicated that theywere not working while approximately one-third worked part time (including voluntary and paid work) and10% worked full time. Approximately two-thirds (66%) of participants were married (including de facto orcommon law relationships), 20% widowed, 10% separated or divorced and 4% never married.

3.1. Medical conditions and self-rated functional abilities for safe driving

The three most commonly reported conditions were vision problems (77%), predominantly long- and short-sightedness (33% and 27%), arthritis (41%) and heart problems (23%). Drivers’ self-ratings of their overallhealth and functional abilities for safe driving are reported in Table 1. Participants were more likely to ratetheir overall health for safe driving as good (53%) rather than excellent (45%). Approximately half of the driv-ers rated their vision for safe driving during the daytime as excellent (49%) while about half rated their vision

Table 1Self-ratings of functional abilities for safe driving

Self-rating Overallhealth %

Visionday %

Visionnight %

Decision-makingspeed %

Upper bodystrength %

Lower bodystrength %

Head/neckmobility %

Excellent 45 49 24 38 39 38 30Good 53 49 52 58 57 58 59Fair 1 2 19 4 4 4 11Poor <1 <1 5 <1 0 0 <1

Total 100 100 100 100 100 100 100


good (49%). Similar ratings were observed for decision-making, strength (upper and lower body) and headand neck mobility for safe driving, with the majority rating their abilities in these areas as either good (57–59%) or excellent (30–39%). Fewer participants (24%) rated their vision for night driving as excellent com-pared with all other driving-related abilities (30–49%).

3.2. Driving patterns

Overall, 86% of drivers reported that they were the principal driver in the household. Males were morelikely than females to be the principal driver (90% vs. 78%) and drivers 75 years and older (90%) were morelikely than those aged 55–64 years (85%) and 65–74 years (83%) to be the principal driver.

Table 2 summarises the driving patterns of participants and reported changes in driving over the past fiveyears. Approximately two-thirds of participants drove more than 100 km weekly (67%). The majority of driv-ers (86%) indicated that they drove less than (41%) or the same amount (45%), compared with five years ago,while comparatively few (14%) indicated that they now drove more. No gender differences were apparent,p = 0.285, however, significant differences were observed across age groups (v2(4) = 30.3, p < 0.001). Moredrivers aged 75 years and older indicated that they drove less than they did five years ago compared withthe two younger age groups (49% vs. 38% and 37%, respectively). Seventy-eight percent of drivers indicatedthat they were driving about as much as they would like to. Interestingly, place of residence (urban, countrytown, rural) was not significantly related to driving distance and since this variable was unrelated to mostother driving-related variables of interest, it is not discussed further. Reasons for driving less were primarilyrelated to changes in employment status (34%) or changes in lifestyle such as moving house (38%). Relativelyfew drivers (17%) identified health or age-related factors and 6% identified a lack of confidence in specific driv-ing situations as reasons for driving less, suggestive of appropriate self-regulation.

The majority of drivers reported driving about the same speed (58%) or slower (40%) now compared withfive years ago. Of those who indicated that they drove slower, most attributed this to compliance with speedrestrictions and enforcement (38%). Female drivers were more likely than males to indicate that their drivingspeed had not changed over the last five years, while male drivers were more likely than female drivers to indi-

Table 2Driving patterns of participants

Driving characteristic Overall Gender Age group (yrs)

% (N) Female Male 55–64 65–74 75+

Weekly distance driven (km)

650 13 (82) 19 9 5 12 1851–100 20 (128) 27 16 14 16 28101–200 21 (138) 25 19 21 23 19>200 46 (306) 29 56 60 49 35Total 100 (654)

Change in driving frequency

More 14 (92) 15 14 25 13 7Same 45 (292) 42 41 37 50 44Less 41 (271) 43 45 38 37 49Total 100 (655)

Change in driving speed

Faster 2 (14) 4 1 4 2 1Same 58 (381) 62 56 57 56 61Slower 40 (261) 34 43 40 42 38Total 100 (656)

Change in driving quality

Better 11 (73) 12 11 19 10 6Same 80 (524) 81 79 70 83 84Not as good 9 (58) 7 10 11 7 9Total 100 (655)

Table 3Confidence ratings for driving situations and odds ratios for ‘very confident’ ratings by gender and age

Driving situation Confidence level (%) Odds Ratios

Very Mod Not at all Gender female:male Age group (yrs) 75+:<75

Rain 61 38 1 2.3* 1.6*

Merging 68 30 2 3.0* 1.6*

Busy traffic 71 28 2 1.9* 1.5*

Night 55 36 9 2.9* 2.3*

Night/wet 44 44 12 3.3* 1.8*

Changing lanes 74 25 <1 2.0* 1.3Intersection: no light 70 28 2 2.1* 1.1Right turn: no light 75 23 2 2.6* 1.5*

Right turn: light/no arrow 77 23 <1 1.7* 1.7*

Right turn: light/arrow 93 7 0 1.3 1.5Roundabouts 82 17 1 1.8* 1.4*

* Differences are significant at p 6 0.05.


cate that they drove slower now compared to five years ago (v2(2) = 6.9, p < 0.032). No significant differencesin changes in driving speed were found across the three age groups, p = 0.4.

Most drivers (80%) thought that their quality of driving was about the same as it was five years ago, how-ever, a small proportion (9%) believed their driving was ‘not as good’ as it was previously. Significant age dif-ferences were found across categories of driving quality, v2(4) = 19.8, p = 0.001. The youngest participants(19%) were more likely to rate their driving quality as better than five years ago than the two older age groups(10% and 6%). No significant gender differences were found, p = 0.285.

Drivers were asked to rate their level of confidence and to indicate whether they intentionally avoided spe-cific driving situations in the previous six months (Table 3). In general, participants were very confident in themajority of situations. As expected, high confidence ratings were particularly evident for right hand turns1

with signals (i.e. fully controlled turning phase) (93%). In contrast, only 70% of drivers were very confidentat uncontrolled intersections. Fewer drivers (55%) were very confident when driving at night and only 44%of drivers indicated that they were very confident when driving at night in the wet.

Odds ratios were calculated for the relative proportion of ‘very confident’ responses for gender and agegroup (comparing responses of those aged 75 years and older with responses of the two younger groups com-bined) (see Table 3). The analyses revealed that males were more likely than females to be very confident in alldriving situations, except for making right-hand turns at fully controlled traffic signals (those with right-handturn arrows). Drivers aged 75 years and older were generally less likely than the younger group to be veryconfident in the majority of driving situations, except at intersections with no traffic control, making right-hand turns at fully controlled traffic signals and when changing lanes.

Table 4 summarises drivers’ avoidance patterns for specific driving situations. Overall, approximately onequarter of the sample or less (6–26%) indicated that they intentionally avoided specific driving situations. Themost commonly avoided driving situations were driving at night (25%), on wet nights (26%), and in busy traf-fic (22%). Only 10% of drivers indicated that they avoided some types of intersections and of those, the major-ity avoided intersections without traffic lights (77%) and intersections without fully controlled right turnarrows (30%). The most frequently reported reasons for avoiding intersections were concerns for safety andcrash avoidance and problems with vision. The main reasons for avoiding specific driving situations included:

Rain: safety factors (66%).Merging: personal preference/comfort factors (28%).Busy roads: personal preference/comfort factors (40%).Night/wet night: vision problems (53%/54%, respectively).

1 Right turns in Australia are equivalent to left turns in Europe and North America.

Table 4Summary of drivers avoidance of driving situations and odds ratios for avoidance by gender and age

Driving situation Overall % Gender Age group (yrs)

F M OR F:M 55–64 65–74 75+ OR 75+:< 75

Rain 14 19 11 1.8** 15 11 17 1.4Merging 6 11 4 3.2** 2 5 9 2.4**

Busy traffic 22 22 22 1.0 16 26 23 1.1*

Night 25 36 18 2.6** 15 18 38 2.5**

Night/wet 26 40 19 2.9** 19 19 40 2.4**

Lane change 15 14 15 0.9 12 16 16 1.1Intersections 10 15 7 2.2** 8 9 12 1.4

* Differences are significant at p < 0.05.** Differences are significant at p < 0.01.


Avoidance patterns were also compared across gender and age group. Summary data and odds ratios forthese analyses are shown in Table 4. Compared with males, female drivers were more likely to avoid drivingin the rain, merging, night driving and driving at night when wet. No gender differences were observed for avoid-ance of busy traffic and changing lanes. Drivers aged 75 years and older were significantly more likely to avoidmerging into traffic, driving at night and at night when wet (p’s < 0.001). Older drivers were also more likely toavoid busy traffic compared with the younger group; however, these age group differences were not as prominentas for other traffic conditions (p < 0.05). Inspection of age differences in avoidance of busy traffic in Table 4shows that differences were greatest between the youngest group and the two oldest groups (16% vs. 26% and23%, for the 55–64 year olds, 65–74 year olds and those aged 75 year and older, respectively). No differencesbetween age groups were observed for avoidance of driving in the rain, intersections, and changing lanes.

Those driving situations with the highest reported levels of avoidance were analysed in more detail to exam-ine relationships with key driver variables. Variables of interest were confidence ratings, ratings of overallhealth for safe driving and functional abilities in vision and decision-making for safe driving. Chi-square anal-yses were used to examine whether drivers with lower ratings of confidence in specific situations and lowerratings of functional abilities were more likely to avoid driving situations than drivers with higher ratings.Table 5 summarises the data from these analyses.

Drivers’ confidence in night driving was found to be significantly related to avoidance of driving at night(v2(1) = 189.1, p < .0001). Similarly, confidence in and avoidance of driving at night when wet (v2(1) = 148.71,p < 0.0001), and busy traffic (v2(1) = 74.74, p < 0.0001) were found to be strongly associated. As shown inTable 5, those drivers who were less confident in each of the driving situations were more likely to avoid thesespecific situations (44–50%) than those who were very confident (2–13%).

Significant relationships were found between ratings of overall health for safe driving and driving at night(v2(1) = 14.7, p < 0.0001) and driving at night when wet (v2(1) = 14.7, p < 0.0001). Generally, drivers withlower health ratings were more likely to avoid these situations (30–33%) compared with those with higherhealth ratings (17–19%). The relationship between overall health and avoidance of busy traffic followed thesame trend as other avoidance behaviours, although this was only marginally significant, p = 0.048.

Significant relationships were found between self-rated vision for night driving and driving at night(v2(1) = 42.1, p < 0.0001) and driving at night when wet (v2(1) = 48.3, p < 0.0001). Generally, drivers withlower ratings for vision for safe night driving were more likely to avoid these situations (30–33%) comparedwith those with higher ratings (5%). The relationship between vision for night driving and avoidance of busytraffic reached borderline significance (v2(1) = 4.1, p < 0.048) with around one quarter (24%) of all drivers withlower ratings reporting that they generally tried to avoid busy traffic compared with 16% of those with excel-lent night vision.

Interestingly, self-rated vision for daytime driving was also significantly related to avoidance of driving atnight (v2(1) = 21.1, p < .0001) and driving at night when wet (v2(1) = 23.4, p < 0.0001). Those with lower day-time vision ratings were more likely to avoid driving at night (32%) and on wet nights (35%) than those withlower ratings (17–18%). No relationship was found for daytime driving ability and avoidance of busy traffic(v2(1) = 0.3, p = 0.320).

Table 5Percentage of drivers who avoid driving situations by ratings of confidence and functional abilities

Self-rating Percentage who avoid driving N

At night Wet night Busy traffic

Yes No Yes No Yes No

Confidence

Very 4 96 2 98 13 87 462Mod/not at all 50 50 45 55 44 56 193

Overall health

Excellent 17 83 19 81 20 80 319Good/fair/poor 30 70 33 67 24 76 337

Vision for day driving


Vision for night driving


Decision-making speed


Total 25 75 26 74 22 78


Self-rated speed of decision making for safe driving was significantly related to avoidance of night driving(v2(1) = 12.5, p < 0.0001) and driving at night when wet (v2(1) = 12.4, p < 0.0001). Approximately one-third ofdrivers with lower ratings avoided driving at night (29%) and on wet nights (31%) while only 17–18% of thosewho reported excellent decision-making abilities avoided these situations. The same trend was observed fordriving in busy traffic, however the effect just failed to reach significance (v2(1) = 2.5, p = 0.067), with aroundone-quarter of drivers with lower decision-making abilities avoiding busy traffic (24%) compared with 19% ofthose who reported excellent decision-making abilities.

Analyses were also conducted to examine the association between self-reported medical conditions andavoidance behaviours. The question of interest was whether drivers with specific medical conditions mightchange their driving to avoid potentially risky driving situations. Analyses were restricted to the three mostcommonly reported medical conditions: vision problems, heart problems and arthritis. Vision problems werefound to be significantly related to avoidance of busy traffic (v2(1) = 6.92, p < 0.01). Twenty-five percent ofpeople who reported having a vision condition also indicated that they intentionally avoided driving in busytraffic compared with only 14% of participants without vision problems. Surprisingly, however, no significantrelationships were found between vision problems and avoidance of driving at night and on wet nights(p’s = 0.62 and 0.22, respectively). Similarly, there was no association between self-reported heart problemsand avoidance of driving at night, on wet nights or in busy traffic (p’s were 0.76, 0.58 and 0.80, respectively).Drivers with arthritis were significantly more likely to avoid driving at night when wet (v2(1) = 4.85, p < 0.05).Around one-third of drivers with arthritis (31%) avoided driving on wet nights compared with 23% of thosewithout arthritis. No significant relationships were found between arthritis and driving at night or in busy traf-fic (p’s = 0.25 and 0.30, respectively).

3.3. Crash and infringement history

Fifteen percent of drivers reported that they had been involved in a crash in the past two years. Most ofthese occurred during the day (83%). Seventy-two percent of all crashes occurred on the road, 7% occurredon private property and 21% in car parks. Fifteen percent of drivers had incurred an infringement, other thana parking fine, in the past two years.


3.4. Characteristics of self-regulators

Logistic regression was used to model the self-regulatory behaviour of older drivers. The purpose of themodelling was to identify key characteristics that were indicative of self-regulatory behaviour in the studygroup of older drivers. For these analyses, drivers were classified using a dichotomous outcome: those whoexhibited self-regulatory behaviour and those who did not, where self-regulators were defined as those whoavoided one or more difficult driving situations. The initial selection of predictor variables was made onthe basis of a priori knowledge from previous studies and included various demographic variables as wellas medical conditions, functional abilities for safe driving and crash history. The statistical significance ofpotential predictor variables was first confirmed using chi-square analyses.

The logistic regression analyses measured the proportionate change in the odds ratio when moving from thereference category to the category of interest (e.g. females, compared with the reference group of males). Theseare reported as relative odds ratios. Summary figures for the logistic regression analysis are shown in Table 6.The analyses identified characteristics that may be considered typical of a self-regulating driver. Odds ratios(and 95% confidence intervals) are reported for each variable, adjusted for all other variables in the model.

Females were more likely than males to be self-regulators (OR: 1.8). Age appeared to be monotonicallyassociated with self-regulatory behaviour, that is, the likelihood of being a self-regulator increased withincreasing age. The odds of a 55–64 year old driver being a self-regulator was 68% that of a 75+ year old dri-ver, while the odds of a 65–74 year old driver being a self-regulator was 79% that of a 75+ year old driver. Theodds of self-regulatory avoidance behaviours amongst those who were the principal driver was only half (51%)that of drivers who were not the main driver in the household. A self-reported vision problem was a significantpredictor of avoidance of specific driving situations. Those with vision problems were approximately 1.5 timesas likely to be self-regulators as those without vision problems. Interestingly, crash involvement was also asignificant predictor of avoidance. Those drivers who reported being involved in a crash in the last two yearswere approximately 1.5 times more likely to avoid difficult driving situations than those who had not beeninvolved in a crash.

Additional exploratory analyses were conducted to consider confidence rating as a potential predictor var-iable given the strong association between driving confidence and avoidance of specific driving situations iden-tified in bivariate analyses reported above. For this model, confidence ratings were categorised dichotomously(very confident vs. moderately or not at all confident in any of the driving situations). This model showed thatconfidence was highly predictive of avoidance of any of the designated driving situations. Those who were veryconfident in all of the eight driving situations were 4 times as likely to be non-self-regulators (OR: 4.1, 95% CI:2.9 and 5.7). Not surprisingly, the inclusion of the ‘confidence’ variable in the regression model tended toreduce the relative importance of all the other predictor variables.

3.5. Driving cessation

Drivers were asked a series of questions addressing the issue of driving cessation. Seventy-seven percent ofrespondents indicated that they had thought about giving up driving one day. The oldest drivers were more

Table 6Summary of multivariate model statistics for prediction of ‘avoidance of any driving situations’

Variable Reference Sig. Relative odds ratio 95% CI for odds ratio

Lower Upper

Gender Male 0.001 1.8 1.26 2.5755–64 yrs 75 yrs+ 0.076 0.68 0.45 1.0465–74 yrs 75 yrs+ 0.215 0.79 0.54 1.15Principal driver No 0.011 0.51 0.30 0.86Vision condition No 0.028 1.53 1.05 2.25Crash-involved No 0.069 1.54 0.97 2.46Constant 0.053 1.90


likely to indicate that they had thought about this issue (83%) than the two younger groups (76% and 73%),and females (82%) were more likely than males (75%) to do likewise. Interestingly, while the majority of driv-ers reported that they had thought about the possibility of not driving one day, only 21% of drivers indicatedthat they had actually planned for this by moving closer to family or services or by exploring alternative trans-port options.

4. Discussion

The broad aim of the study was to describe the prevalence and types of self-regulatory practices adopted byolder drivers and to identify characteristics of those who self-regulate and those who do not. Telephone inter-views were conducted with 656 drivers aged 55 years and older from urban and rural areas in the State ofVictoria, Australia.

Over two-thirds of all drivers reported driving more than 100 km weekly and the majority were satisfiedwith their amount of driving. Although the general perception of drivers was that their driving qualityremained unchanged, about 40% of drivers said that they were driving less and slower now than they were5 years ago. Reasons for reductions in the amount of driving were primarily related to general lifestylechanges, such as moving house and employment changes. Fewer than 20% of drivers who reduced theiramount of driving attributed this to health or general ageing issues. Most drivers reported that they were sat-isfied with their current amount of driving. Most also said that they had thought about giving up drivingsometime in the future although fewer than one quarter had actually made plans for this. This suggests a needfor development of appropriate educational resources for assisting seniors in making plans for retiring fromdriving.

Typically, drivers reported being very confident in the majority of driving situations. Two notable excep-tions were for night driving and driving at night when wet, where ‘very confident’ ratings were made by onlyaround half of the drivers. Given the high levels of confidence across most driving situations, it was not sur-prising that a relatively small proportion of drivers reported avoiding difficult driving situations. Highestavoidance levels were seen for busy traffic, night driving and driving at night when wet. About one-quarterof the participants reported avoiding these situations. Not all drivers with lower confidence ratings wereadopting appropriate self-regulatory practices. Indeed, only one-half of those participants who reported beingmoderately or not-at-all confident, who arguably should avoid these driving situations, reported self-regulat-ing. It is also worth noting that, while few in number, some participants with high confidence ratings were self-regulating.

Avoidance of night driving and driving at night when wet were significantly related to overall health andself-ratings for functional abilities in vision (for both day and night driving) and decision making. Consistentwith this, more than half of the drivers who avoided night driving explicitly attributed this to vision-relatedissues, especially adjusting to glare from lights. Avoidance of busy traffic was associated with vision problems.On the face of it, this would seem to be a safe strategy since busy traffic implies complexity in the array ofvisual information in the road environment. For the same reasons, one might expect that those who rated theirdecision-making as less than excellent might also opt to avoid busy traffic which is cognitively demanding.While a trend was evident, the results showed only a weak association between avoidance of busy trafficand decision-making. The most common reason that drivers gave for avoiding busy traffic was not relatedto health or functional ability but rather to personal preference, with many reporting that busy traffic wasnot enjoyable and made them feel uncomfortable.

Avoidance rates for difficult driving situations in this study were considerably lower than those reportedpreviously. For example, Ball et al. (1998) reported avoidance rates for rush hour and night driving as highas 70–80% in drivers aged 55 years and older, while Hakamies-Blomqvist and Wahlstrom (1998) reportedavoidance rates of 35–45% for the same driving situations in drivers aged 70 years. Differences in avoidancerates between these two studies and the current study may reflect differences in the sample demographics, ques-tionnaire response modes or in fundamental mobility and driving characteristics such as availability of othermodes of transport, reliance on the motor car, and licensing reassessment procedures for older drivers. Withregard to the latter point, some have argued that regular licence retesting promotes self-checking and self-regulation. However, if this were true, then one would expect higher self-regulatory behaviours among the


Finnish drivers in Hakamies-Blomqvist and Wahlstrom’s study, who were subject to strict medical screeningat age 70 years, compared with drivers in the current study and those surveyed by Ball et al. who had no suchrelicensing requirements. Interestingly, while this expectation was upheld for the comparison with Victoriandrivers, the reverse was in fact true for the comparison with drivers in Alabama (Ball et al., 1998). We arecurrently conducting further studies to evaluate the relationship between licence reassessment and self-regulation.

As implied in the findings discussed above, it is unlikely that any single factor can adequately explainself-regulatory behaviours. Hence, we used regression modelling to enable us to identify some of the key char-acteristics of self-regulators amongst older drivers. Not surprisingly, those who avoided specific driving situ-ations were more likely to be 75 years and older. Gender was also a significant predictor of self-regulatorypractices with females more likely than males to avoid potentially risky driving situations. Interestingly, thosewho were not the principal driver in the household were more likely to be self-regulators than those who werenot the principal driver. This is an intuitive result since the presence of another driver may provide morechoice about when and where trips are taken. Of interest was the relationship between self-regulation andself-reported medical conditions and functional abilities. Previous studies have identified vision impairmentand cognitive impairment as a significant predictor of both amount of driving and avoidance of driving in cer-tain situations (Ball et al., 1998; Lyman et al., 2001; Stutts, 1998). In this study, only self-reported vision prob-lems were found to be associated with avoidance of potentially difficult driving situations. A unique finding ofthis study was that driving confidence was strongly predictive of avoidance behaviour. Those who rated them-selves as moderately or not at all confident in at least one of the eight driving situations were more likely to beself-regulators. Interestingly, crashes were more prevalent amongst those who self-regulated by avoiding dif-ficult driving situations. It is important to note that the survey did not provide information about the relativetiming of crashes and adoption of self-regulatory behaviour. It is plausible, however, that drivers avoidedpotentially risky driving situations following their involvement in a crash in an attempt to minimise their riskof future crashes. Future research is needed to better identify the relationship between self-regulatory drivingpractices and crash risk.

4.1. Limitations of the study

Notwithstanding the use of multiple recruitment methods and sources, it is possible that the sample maynot be entirely representative of Australian drivers aged 55 years and older. For example, it could be arguedthat the sample of volunteers may be healthier, more interested in driving, more safety conscious and have agreater need to drive than the general population from which they are drawn.

Due to constraints on interview time, it was not possible to explore changes in self-regulatory practice usinga longitudinal study design. Hence, it was difficult to determine whether those who avoided certain drivingsituations had done so only recently or whether they had avoided these situations all their driving life. In addi-tion, functional abilities related to driving were assessed by self-report. Ideally, these should be measured usingstandardised tests of cognition and other abilities related to driving. However, one could also argue that ifdrivers perceive their abilities in these areas to be less than optimal for safe driving, then this indeed maybe sufficient justification for self-regulation. While this might be the case for the majority of drivers, those withpoor cognitive capacity and poor insight are likely to provide inaccurate information about their health statusor driving patterns.

5. Conclusions

The results of this study confirmed for a sample of Australian drivers many of the findings from previousresearch with drivers in other countries (e.g. Ball et al., 1998; Hakamies-Blomqvist & Wahlstrom, 1998;Kostyniuk & Shope, 1998; Lyman et al., 2001; Marottoli et al., 1993; Persson, 1993). In general, this studyfound evidence for a reduction in driving distances and an increase in avoidance of specific driving situationsas a function of age, although the prevalence of avoidance behaviours was considerably lower than previouslyreported. A major contribution of this study has been to explore characteristics, other than age, that areassociated with self-regulatory driving practices. These findings have practical implications for road safety


countermeasures aimed at improving the safety of older drivers. In particular, the study highlights the need forstrategies to promote through educational materials and programs, the adoption of self-regulatory practicesconsistent with declining functional ability.

Acknowledgements

This study was funded by Austroads and the Baseline Sponsors of Monash University Accident ResearchCentre. We thank the following people who assisted throughout the project: Nicola Pronk, Lara Cameron,Keith Hsuan, Lauren Johnson for research and computing assistance; and interviewers Noelene and DeanneDeveson, Mirriam Shrimski, and Samia Toukhsati. We gratefully acknowledge the advice and support of theProject Advisory Committee and we thank the Victorian drivers who generously volunteered to participate inthis research.

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characteristics of older drivers who adopt self-regulatory driving behaviours

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