The relationship between Motorcycle Rider Behaviour Questionnaire scores and crashes for riders in Australia

Accident Analysis and Prevention 102 (2017) 202–212

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The relationship between Motorcycle Rider Behaviour Questionnaire scores and crashes for riders in Australia A.N. Stephens a,∗ , J. Brown b , L. de Rome c , M.R.J. Baldock d , R. Fernandes e , M. Fitzharris a a

Monash University Accident Research Centre, Monash University, Clayton, Victoria, Australia Neuroscience Research Australia, University of New South Wales Australia Deakin University, Victoria, Australia d Centre for Automotive Safety Research, University of Adelaide, Australia e Transport for New South Wales, Australia b c

a r t i c l e

i n f o

Article history: Received 6 May 2016 Received in revised form 14 February 2017 Accepted 6 March 2017 Keywords: Motorcycle Crashes Motorcycle rider behaviour questionnaire

a b s t r a c t Motorcycle riders are over-represented in road fatalities in Australia. While riders represent 18% of the road users killed each year, motorcycle registrations constitute only 4.5% of the registered vehicle fleet. The Motorcycle Rider Behaviour Questionnaire (MRBQ) was developed with a view toward understanding behaviours likely to be associated with crash risk. These include behaviours that are either intentional (such as violations of road and speed regulations and stunts) or unintentional (such as errors relating to traffic or control of the motorcycle), as well as protective behaviours related to use of safety equipment. The dual aims of the current study were, first, to determine the appropriate structure of a modified version of the MRBQ for use in a representative sample of riders in Australia and, second, to understand which MRBQ factors are associated with crash involvement. A stratified sampling procedure was undertaken to ensure the socio-economic status of local government area, age and gender of the sample was representative of the broader population of riders in New South Wales, Australia. The sample consisted of 470 riders (males = 89%). Exploratory factor analysis revealed a 29-item, five factor structure was suitable on the Australian data encompassing traffic errors, speed violations, protective gear, control errors and stunts. Overall, riders reported relatively safe behaviours, with frequent use of protective gear and infrequent aberrant behaviours. However, even though infrequent, violations of speed and errors related to control of the motorcycle increased the odds of near-crash involvement, whilst stunt behaviours were associated with increased odds of crash involvement. Interventions and countermeasures need to target these specific behaviours. © 2017 Elsevier Ltd. All rights reserved.

1. Introduction Motorcyclists are over-represented in crash statistics. In Australia, riders account for 18% of road deaths annually while motorcycles account for 4.5% of the registered fleet (Australian Bureau of Statistics, 2015). Similar trends have been observed in the UK (DfT, 2005) and the USA (Savage, 2013). Not only are riders over-represented in fatal crash statistics, their crash risk per distance travelled is considerably higher than that for other modes of transport (Keall and Newstead, 2012). For this reason, and the fact that motorcycling is becoming more popular, as evidenced by registrations for motorcycles increasing more rapidly than any other

∗ Corresponding author. E-mail address: [email protected] (A.N. Stephens). http://dx.doi.org/10.1016/j.aap.2017.03.007 0001-4575/© 2017 Elsevier Ltd. All rights reserved.

type of road transport (Australian Bureau of Statistics, 2015), rider safety has risen sharply into focus. While there are a number of factors that can contribute to a rider’s crash risk, individual rider characteristics and rider behaviour have both been found to be key components (Lin and Kraus, 2009; Sexton et al., 2004). Lin and Kraus (2009) reviewed 220 publications reporting risk factors for casualty motorcycle crashes and found that inexperience, risk-taking behaviour and violations of speed and sobriety regulations all contributed to both the risk and potential severity of crash outcome. However, these types of behaviours can have different psychological underpinnings. For example, certain risk taking behaviours outlined in Lin and Kraus’ review, such as inappropriate headway or not allowing enough time to stop at amber lights, can result from inexperience or unintentional errors. Other risk taking behaviours, such as riding while under the influence of alcohol or failure to wear appropriate protective clothing, are mostly conscious decisions. Before appropriate

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interventions to reduce risk can be can be identified, the behaviours associated with crash involvement need to be understood. Elliott et al. (2007) proposed the Motorcycle Rider Behaviour Questionnaire (MRBQ) as a suitable measure for different types of behaviours which may contribute to crash risk. The original MRBQ has 43 items each representing different riding actions that cover five broader categories of behaviours: traffic errors; speed violations; performance of stunts; control errors and use of safety equipment. The latter measures the wearing of protective gear. Stunts are intentional sensation seeking behaviours, for example “Attempt to, or actually do, a wheelie”. Speed violations are intentional acts that are more instrumental than stunts (Elliott et al., 2007). An example of a speed violation item is: “Exceed the speed limit on a country road”. Traffic errors are unintentional mistakes made by the rider, e.g. “Not notice someone stepping out from behind a parked vehicle until it is nearly too late” and control errors can be either intentional or unintentional but are specific to motorcycle handling. For example “Skid on a wet road or manhole cover. While the MRBQ has been adopted by other researchers (e.g. Özkan et al., 2012; Sakashita et al., 2014), the five broad categories of behaviour have not always been used. In particular, Sakashita et al. (2014) used four general types of behaviour, combining control error and traffic error items into one factor. These differences in suitable factors may be a result of differing samples and sampling procedures. Specifically, Elliott et al. (2007) used a large sample (N = 8666) of registered motorcycle owners in the UK who responded to a postal questionnaire, while Sakashita et al. (2014) collected MRBQ data via a telephone or online survey from a sample of 1305 newly licensed riders based in Victoria, Australia. These riders had held a probationary/restricted motorcycle licence for 12-months or less. There have also been mixed findings as to which, if any, behaviour type best predicts rider crash involvement. Elliott et al. (2007) and Sexton et al. (2004) found that their 13-item factor for traffic errors best predicted self-reported crash involvement over the previous 12 months. However, when only ‘at fault’ crashes were considered, both traffic errors and speed violations were found to predict crashes, with traffic errors being the stronger of the two predictors (Elliott et al., 2007). In contrast, Özkan et al. (2012) reported that performance of stunts, not traffic errors (measured with only 10-items) reliably predicted self-reported at-fault crashes over the previous three year period and no MRBQ factor predicted not-atfault crashes. Sakashita et al. (2014) measured self-reported crashes and also obtained police recorded crash information for their participants. They found that both their factors for errors (17-items, combining traffic and control errors) and for speed violations (7 items) predicted self-reported crashes, however it was only the performance of stunts that contributed to police recorded crashes. While in all three studies age and experience of the rider were controlled for in the analyses, the crash data period differed. For example, Sakashita et al. (2014) and Elliott et al., asked for crashes across the previous 12 months, whereas (Özkan et al., 2012) collected crash data for the previous 36 months. These differences, coupled with the different factor configurations and different samples are likely to explain the inconsistent results. However, what remains consistent is that the latent constructs of control errors or lack of safety equipment do not appear to contribute to crashes, while traffic errors, violations or stunts may influence crash risk for some riders. Given the inconsistent findings regarding types of behaviours likely to contribute to crash risk, there is a need to develop further understanding in this regard. Therefore, the aim of the current study was twofold, first to identify the most appropriate factor structure of the MRBQ for a representative sample of riders from Australia, and, second, to examine the associations between result-

203

ing MRBQ factors with crash involvement and other dangerous riding behaviours.

2. Method 2.1. Participants and procedure Questionnaire data were collected from motorcyclists who attended government licencing and registration offices in NSW (motor registries). Ethics approval for the study was obtained from the University of NSW Human Research Ethics Committee. The sample was stratified so as to be representative by age, gender and socio-economic status of local government area. The stratification process involved multiple stages (see Fig. 1) and was based on the World Health Organisation (WHO) guidelines on probability sampling (WHO, 2012). Initially, relative socio-economic advantage/disadvantage was measured by Socio-Economic Indexes for Areas (SIEFA) for local government areas in NSW. These scores were then standardised and divided into quartiles representing disadvantaged and moderately disadvantaged socio-economic areas (lower two quartiles) and moderately advantaged and advantaged socio-economic areas (upper two quartiles). Four strata of local government areas were derived from this. The population of registered motorcycles was estimated using data from the NSW vehicle registration database. These were then classified across the four strata based on registration post-code. Motor registries were also classified into each of the four strata and ranked according to the average registration renewals. Motor vehicle registries to recruit from were then randomly selected from each strata in proportional numbers relative to the number of registered motorcycles. In total, 25 registries were chosen to recruit from, 12 from advantaged local government areas, seven from moderately advantaged local government areas and three each from disadvantaged and moderately disadvantaged local government areas. In NSW, Australia all licensed motor vehicle operators must attend a motor registry to renew their licence. All licensed motor vehicle operators therefore have an equal probability of being recruited from these study locations. A total of 13,879 potential participants were approached across the 25 selected motor vehicle registries. After initial screening to determine if the customer was aged over 18 and the owner of a registered motorcycle, 1120 were found suitable as potential participants (>90% exclusion). One quarter of these (n = 275) declined to participate leaving 845 customers who agreed to take part. Potential participants were offered the choice of completing the questionnaire on-site (469 agreed) or online at a later stage (376 agreed). A total of 506 questionnaires were completed; 403 onsite (a response rate of 86% for onsite completion) while 103 were completed online (a response rate of 27% for online completion). After data cleaning, discussed below, the final sample consisted of 470 motorcycle riders. Participants were mainly male (89%), which reflected the proportion of riders in the registration database. Participants ranged in age from 17 to 88 years (M = 43.72 ± 13.87).

2.2. Materials 2.2.1. Motorcycle rider behaviour questionnaire (MRBQ) The motorcycle rider behaviour questionnaire (MRBQ; Elliott et al., 2007; Sexton et al., 2004) contains 43 items that measure aberrant riding behaviours. Riders report the frequency of engagement in each behaviour on a 6-point scale (1 = never; 3 = occasionally; 6 = nearly all the time). This measure has good reliability with Cronbach alpha co-efficients for the five factors ranging from 0.70 to 0.84 (Elliott et al., 2007).

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Fig. 1. Recruitment procedure for obtaining a representative sample of riders from New South Wales.

A modified version of the original MRBQ tailored for riders in Australia was used in the current study. Item 30 “wear body armour (for elbows, shoulders and knees)” was split into two items, item 30: “wear body armour/impact protection for the elbows and shoulder” and an additional item, item 44: “wear body armour/impact protection for the knees”, because rates of body armour usage tend to differ for the upper and lower body (de Rome et al., 2011). Two further items were included, item 45: “wear body armour for the back” and item 46: “ride 3 or more seconds behind the vehicle in front”. To compensate for the addition of three items while keeping the questionnaire to 43-items, three items were excluded. These were item 43: “wear bright/florescent clothing”. This item was dropped by Elliott et al. (2007) for inconsistent loading and by Sakashita et al. (2014) for low loading. Item 26: “Unintentionally do a wheel spin” also showed low loading on to the stunts factor on Australian riders (Sakashita et al., 2014) and so was removed from the questionnaire. The pair to item 26, item 25 “Intentionally do a wheel spin”

was also removed from the questionnaire. The wording of these items is quite similar and therefore, they are likely to share a large amount of variance. Further, Özkan et al. (2012) reported similar factor loadings for item 26 and item 24 “pulled away too quickly and your front wheel lifted off the road”, indicating that these items might also be measuring similar constructs. Consistently, other stunts items such as “pulled away too quickly and your front wheel lifted off the road” and “attempted or did a wheelie” have stronger loadings on the stunts factor than the pair of items related to performing a wheel spin (Özkan et al., 2012; Sakashita et al., 2014). Therefore the removal of items 25 and 26 is justified. Factor scores were created from the average summed items within each factor. With the exception of the factor for safety equipment, higher factor scores indicate more frequent aberrant behaviour.

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2.2.2. Demographic information, exposure and crash history The questionnaire included information on rider demographics, motorcycle ownership and licencing. For example, participants provided age, gender, marital and employment status and responded to questions regarding motorcycle licencing history (type, validity, duration), motorcycle ownership (number and type of motorcycle, familiarity with main motorcycle) and use of protective clothing (helmet, visor, clothing, other equipment). Riding exposure (hours and kilometres per week, main riding purpose, days of the week, road and weather conditions) and experience (years, on-road and off-road, breaks in riding, group riding, involvement in extra training and/or ride days) were also obtained. Participants also provided information on motorcycle crashes including minor spills and near-crash history on public roads for the past 12 months. Near-crashes were defined as ‘only just avoiding a crash or a near-miss while riding’. Near-crash frequency was measured on a 5-point scale (0 = Never, 1 = 1 or 2, 2 = 3-5, 3 = 6-10 and 4 = more than 10 times). Participants were also asked about car and motorcycle crashes, traffic violations and any lost demerit points in the past three years. For this analysis they were classified on a 5-point scale (0 = none, 4 = more than three). These were later dichotomised (yes/no) for analysis. Traffic violation information was gained across five questions. Riders were asked whether they had received any traffic violations in the past three years; whether in the past three years their licence had been suspended or disqualified; whether the violations related to speed, alcohol or other and how many demerit points they had lost as a rider in the past three years. Yes or no answers were provided for each question and a distinction was made for offences whilst a rider of a motorcycle or driver of another vehicle.

2.3. Data handling and analysis There were a number of cases with missing data and these were handled in one of two ways. Cases with 10% or more missing responses (4 or more items) on the MRBQ were removed from the analysis, resulting in 29 lost cases. For cases missing less than 10% of the MRBQ items, missing values were replaced with a 5% trimmed mean. Mean imputation was performed on 71 data points, which is less than <0.5% of the data. Seven further cases were removed as large violations of multivariate normality, resulting in a final dataset of 470 cases. ® ® All analyses were conducted using IBM SPSS v.22. The factor structure of the MRBQ was determined using exploratory factor analysis with Principal Axis Factoring (PAF) and direct Oblimin method, which accounts for correlated factors. Associations between MRBQ factors and crash outcomes (crashes, nears crashes and speed-related traffic violations) were explored using binary logistic regression models. To do this, variables for each outcome measure were re-categorised into binary variables (yes/no). Forward likelihood ratio (LR) procedures were used for model building as these add one variable at a time based on significant contribution to the model. Variables for age, gender, kilometres and reason for riding were also included in the model as these have previously been identified as contributing factors for motorcycle crash involvement (Harrison and Christie, 2005). Given the low number of variables used in the model (age, gender, kilometres, years riding, reason for riding and the MRBQ factors) the p value for entry was set to 0.10, and p value for exclusion set at 0.05. Odds ratios were calculated for each independent variable at a 95% confidence interval (Hosmer et al., 2013). Collinearity was not observed to be a problem throughout model building.

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Table 1 Sample characteristics. % (n) Gender 89% (418) Males Females 11% (52) Agea 16–20 4% (19) 9% (43) 21–25 22% (105) 26–39 40–59 51% (239) 60 and older 12% (58) Motorcycle licence type Learner permit 9% (41) 6% (29) Probationary (stage 1) 3% (16) Probationary (stage 2) 81% (380) Full/unrestricted None 1% (4) Years riding 24% (115) 2 or less 29% (136) 3 to 10 11 or more 47% (219) Average hours riding per weeka 5 or less 52% (243) 25% (116) 6 to 10 13% (60) 11 or more Average weekly kilometresa 50 km or less 20% (93) 51 to 100 km 26% (123) 19% (88) 101 to 200 201 to 300 10% (47) 301 or more 25% (115) Main reason for riding Only to commute or for work 17% (80) Only for recreation 39% (185) Other – multipurpose riding 44% (205) Traffic violations (past 3 years) across all vehiclesa Yes 30% (143) 69% (323) No a Traffic violations (past 3 years) for motorcycles only Yes 11% (50) No 89% (416) Speed-related traffic violations (past 3 years) for motorcycles onlya Yes 8% (37) No 91% (429) a Near-crash (past 12 months) None 26% (123) 1 to 2 43% (200) 17% (81) 3 to 5 6 to 10 6% (29) 6% (29) 10 or more Crashes when riding (past 12 months)a None 87% (410) 9% (44) One 3% (13) Two or more a

Due to missing data percentage does not total 100.

3. Results 3.1. Sample characteristics The characteristics of the sample are displayed in Table 1. The majority of the participants were aged between 40 and 59 years (51%) with only a small percentage of riders aged between 16 and 20 years (4%). Likewise, most of the riders held a full, unrestricted motorcycle licence (81%) with 9% of the sample holding a learner permit and 12% holding a probationary licence, at either P1 (first year of licence: 6%) or P2 (second and third year: 3%). The majority of riders, on average, rode for up to five hours per week (52%). One quarter of the sample reported average weekly kilometres of more than 300 km, while 20% reported an average of 50 km or less per week. Almost half of the sample (44%) used the motorcycle for more than one purpose, while 17% only rode for their commute or for work and 39% only rode for recreation. A third (30%) had received a

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traffic violation across all vehicle types in the past three years, and 35% of these riders received a traffic violation due to offences committed whilst riding their motorcycle (i.e., 10.6% overall). Only 26% reported having no near-crash experiences in the past 12 months. Furthermore, 12% of the sample had been involved in a motorcycle crash when they were riding in the past year. 3.2. MRBQ item scores Table 2 shows the means for each of the MRBQ items and their factor allocations according to Elliott et al. (2007), Özkan et al. (2012) and Sakashita et al. (2014). The most frequent behaviours reported by riders were related to the use of protective gear. For example, out of an average score range of 1 = never to 6 = almost all the time, the three highest scoring items were: item 34 ‘Use daytime headlights on your bike’ (M = 5.36 ± 1.42); item 32 “Wear motorcycle gloves” (M = 5.24 ± 1.33); and, item 29 “Wear a protective jacket” (M = 5.19 ± 1.39). These findings suggest that the riders in the sample almost always use these types of safeguards. In contrast, the three lowest scoring items were: item 41 “Ride when you suspect you are over the legal limit for alcohol” (M = 1.16 ± 0.54); item 31 “Wear no protective clothing” (M = 1.18 ± 1.33); and, item 5 “Miss ‘Give Way’ signs and narrowly avoid colliding with traffic having the right of way” (M = 1.26 ± 0.49). 3.3. Factor analysis of the MRBQ All 43 items were subjected to Principal Axis Factoring (PAF). Prior to the factor analysis the item-interrelationships were examined and six items were found to have very few, if any, significant relationships with the other items (items 31,33,34,42,44,45). These were excluded from further analysis. The initial PAF produced eight factors with eigenvalues greater than 1, although Monte Carlo parallel analysis also conducted in SPSS (O’Connor, 2000) suggested a five-factor solution. The final five factor solution is displayed in Table 3. A number of items had low factor loadings and so were omitted from the model (items 20, 30, 40, 41, 46 < 0.30 and 11, 22 < 0.35) and item 19 had equal loadings on two factors so was also removed. The five factors, based on 29 MRBQ items, explained 54% of the variance. The Kaiser-Meyer-Olkin measure of sampling adequacy was good (0.86) and the Bartlett’s test of sphericity was significant (␹2 (406) = 5156, p < 0.001), indicating that there were significant correlations among the items and that the data were suitable for factor analysis. The original five factor names were retained for the factors due to their similarity with previously reported factors. Factor 1, explained 24% of the total variance and contained 10 items all from the original traffic errors factor (Elliott et al., 2007; Sexton et al., 2004). With the exception of item 9 that was excluded by Sakashita et al. (2014), there is agreement among the published research (Elliott et al., 2007; Özkan et al., 2012; Sexton et al., 2004) that these 10 items belong to an MRBQ factor relating to errors. Factor 2 explained a further 11% of the total variance of the scale and contained five items all from the original speed violations factor (Elliott et al., 2007). Unlike the original speed violation factor and subsequent versions from Özkan et al. (2012) and Sakashita et al. (2014) the five items that load onto the current factor all specifically relate to violations of speed. Factor 3, accounted for 8% of the variance and contained five items related to wearing protective gear. Therefore, in agreement with Sakashita et al. (2014) the label protective gear was assigned to this factor. Factor 4, explained 6% of the total variance and contained six items all originally from the control errors factor (Elliott et al., 2007). However, two of the items contained in this factor (items 12 & 13)

were cross loaded in the original structure, and then classified as violations in the analysis by Özkan et al. (2012). This factor is the most different to previous research. Factor 5, accounted for 5% of the variance and contained only three items originally classified as stunts. All factors had good reliability with ␣ co-efficients ranging between 0.76 and 0.84 (see Table 3) demonstrating acceptable internal consistency of the items within each factor. Table 3 also shows the composite scores for each factor, which represent the average of summed items. The use of protective gear was the most frequent type of behaviour reported (M = 4.53 ± 1.28). Traffic errors, control errors and stunts were relatively rare events with the average factor scores suggesting riders engaged in each type of behaviour never to hardly ever (means ranged from 1.50 to 1.90 out of a possible 6). The most frequent type of behaviour, albeit still relatively uncommon, was speed violations (M = 2.31 ± 0.92). Therefore, riders in the current sample, on average, tended to wear protective gear frequently and engaged in aberrant behaviours very infrequently. Table 4 shows the correlations between factors. With the exception of protective gear, all factors shared weak to moderate relationships, suggesting that they share some commonality but remain independent constructs. The strongest relationship was between traffic errors and control errors (r = 0.42), although this relationship was only of moderate strength. Protective gear was unrelated to speed violations, control errors or stunts, therefore suggesting that riding behaviours tend to be unrelated to the amount of protective gear a rider wears. 3.4. MRBQ scores across rider demographics and crash history Factor scores were compared across rider demographics (gender, age group), reason for riding (commute/work only; recreation only; other – mixed) as well as traffic violation and crash history while riding (violations in the past 3 years, crashes and near-crash in past 12 months; see Table 6). Although the five MRBQ had acceptable normality distributions, across all cases the independent groups being compared were extremely unbalanced. Therefore, these analyses were performed with non-parametric tests (MannWhitney U for two independent groups, Kruskal-Wallis for more than two independent groups). A significant, small effect of gender was found on self-reported speed violations and stunts, with male riders engaging in these behaviours more frequently than female riders. Scores for speed violations and stunts also differed across age groups. As can be seen in Table 5, riders aged 60 years or over reported less frequent speed violations and stunts than the younger riders. However, post hoc tests, with strict Bonferroni adjustments (set to p = 0.001) revealed only one significant weak relationship. Riders 60 years and older reported less frequent stunts than riders aged between 26 and 39 years (z = −3.39, p < 0.001, r = 0.27). When the reasons for riding were examined, frequency of speed violations, traffic errors, protective gear and stunts all differed across the reasons for riding, with mean values suggesting traffic errors were more frequent for those who rode for work or while commuting compared to recreation riding. Stunts, speed violations and protective gear were all more frequent for riders who only rode for recreation. However, post hoc tests showed only one significant relationship at alpha level of 0.001: riders who ride mainly for recreation more frequently wear protective gear than those who ride for their commute or for work (z = −3.75, p < 0.001, r = 0.17). To examine MRBQ scores across crash related conditions, binary variables were created for near-crash (yes or no) and crash (yes or no) over the past 12 months. Scores for traffic errors differed across riders who had and those who had not received a traffic violation, had experienced a near-crash or had been involved in a crash in the past 12 months. Riders who had not been involved in a crash

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Table 2 Item scores for the Australian sample and previously suggested factor structure for the MRBQ. No.

Item

M (SD)

Elliott et al. (2007) Sexton et al. (2004)

Özkan et al. (2012)

Sakashita et al. (2014)

1

Failed to notice that pedestrians are crossing when turning into a side street from a main road Not noticed someone stepping out from behind a parked vehicle until it is nearly too late Not noticed a pedestrian waiting at a crossing where the lights have just turned red Pulled out on to a main road in front of a vehicle you hadn’t noticed or whose speed you misjudged Miss “Give Way” signs and narrowly avoid colliding with traffic having the right of way Failed to notice or anticipate another vehicle pulling out in front of you and had difficulty stopping Queuing to turn left on a main road, you pay such close attention to the main traffic that you nearly hit the vehicle in front Distracted or pre-occupied, you belatedly realise that the vehicle in front has slowed and you have to brake hard to avoid collision Attempt to overtake someone that you had not noticed to be signalling a right turn When riding at the same speed as other traffic, you find it difficult to stop in time when a traffic light has turned against you Ride so close to the vehicle in front that it would be difficult to stop in an emergency Run wide when going around a corner Ride so fast into a corner that you feel you might lose control Exceed the speed limit on a country/rural road Disregard speed limit late at night or in the early hours of morning Exceed the speed limit on a motorway Exceed the speed limit on a residential road Race away from traffic lights with the intention of beating the driver/rider next to you Open up the throttle and just go for it on a country road Ride between two lanes of fast moving traffic Got involved in racing other riders or drivers Ride so fast into a corner that you scare yourself Attempted or done a wheelie Pulled away too quickly and your front wheel lifted off the road Intentionally do a wheel spin Unintentionally do a wheel spin Wear motorcycle riding boots Wear protective trousers − leather or non-leather Wear a protective jacket − leather or non-leather Wear body armour/impact protection for the elbows and shoulders Wear no protective clothing Wear motorcycle gloves Wear bright fluorescent strips/patches on your clothing Use daytime headlights on your bike Brake or throttle back when going around a bend Change gears when going around a corner Find that you have difficulty controlling the bike when riding at speed (steering wobble) Skid on a wet road or manhole cover, road marking, Have trouble with your visor or goggles fogging up Another driver deliberately annoys you or puts you at risk Ride when you suspect you might be over the legal limit for alcohol Wear a full leather suit Bright fluorescent clothing Wear body armour/impact protection for the knees Wear body armour back protector Ride 3 or more seconds behind the vehicle in front

1.45 (.69)

Error

Error

Error

1.60 (.69)

Error

Error

Error

1.42 (.61)

Error

Error

Error

1.53 (.66)

Error

Error

Error

1.26 (.49)

Error

Error

Error

1.73 (.69)

Error

Error

Error

1.54 (.80)

Error

Error

Error

1.73 (.71)

Error

Error

Error

1.36 (.62)

Error

Error

Errora

1.42 (.66)

Error

Error

Error

1.54 (.75)

Violation/Error

Violation

Error

1.89 (.84) 1.80 (.75)

Control/Error Control/Error

Violation Violation

Error Error

2.51 (1.27) 1.91 (1.00)

Violation Violation

Violation Violation

Violation Violation

2.40 (1.23) 2.56 (1.11) 2.19 (1.23)

Violation Violation Violation

Violation Violation Violation

Violation Violation Violation

2.13 (1.18) 1.46 (.87) 1.51 (.82) 1.74 (.78) 1.56 (.91) 1.63 (.88)

Violation Violation Stunts/Violation Control/Violation Stunts Stunts

Violation Violation Stunts Violation Stunts Stunts

Violation Violationa Violation Error Stunts Stunts

n/a n/a 4.20 (1.98) 4.12 (1.95) 5.19 (1.39) 3.90 (2.16)

Stunts Stunts Safety Safety Safety Safety

Stunts Stunts Safety Safety Safety Safety

Stunts Stunts Safety Safety Safety Safety

1.18 (1.33) 5.24 (1.33) 2.33 (1.85) 5.36 (1.42) 2.29 (1.07) 2.00 (1.01) 1.44 (.72)

Safety Safety Safety Safety Control Control Control

Safety Safety Safety Controla Control Control Control

Safetya Safetya Safety Safetya Error Error Error

1.98 (.96) 2.62 (1.21) 2.42 (1.12) 1.16 (.54)

Control Controla Stuntsa Violationa

Control Control Violation Safety*

Error Errora Violationa Violationa

1.90 (1.56) n/a 2.52 (1.92) 3.23 (2.16) 4.69 (1.40)

Stuntsb Violationsb n/a n/a n/a

Stunts Safety n/a n/a n/a

Stuntsa Safetya n/a n/a n/a

2 3 4 5 6 7

8

9 10

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

Error = traffic Errors; Violations = speed Violations; Safety = Safety equipment; Stunts = performance of Stunts, Control = Control Errors. a Item had loading <0.30 and was dropped from analysis. b dropped because loading did not make conceptual sense.

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Table 3 Factor structure and loadings of the MRBQ in a representative sample of riders from NSW. Item

Factor 1. Traffic Errors

5. Miss “Give Way” signs and narrowly avoid colliding with traffic having the right of way 1. Failed to notice that pedestrians are crossing when turning into a side street from a main road 2. Not noticed someone stepping out from behind a parked vehicle until it is nearly too late 4. Pulled out on to a main road in front of a vehicle you hadn’t noticed or whose speed you misjudged 7. Queuing to turn left on a main road, you pay such close attention to the main traffic that you nearly hit the vehicle in front 9. Attempt to overtake someone that you had not noticed to be signalling a right turn 3. Not noticed a pedestrian waiting at a crossing where the lights have just turned red 10. When riding at the same speed as other traffic, you find it difficult to stop in time when a traffic light has turned against you 8. Distracted or pre-occupied, you belatedly realise that the vehicle in front has slowed and you have to brake hard to avoid collision 6. Failed to notice or anticipate another vehicle pulling out in front of you and had difficulty stopping 17. Exceed the speed limit on a residential road 16. Exceed the speed limit on a motorway 14. Exceed the speed limit on a country/rural road 15. Disregard speed limit late at night or in the early hours of morning 18. Race away from traffic lights with the intention of beating the driver/rider next to you 29. Wear a protective jacket − leather or non-leather 28. Wear protective trousers − leather or non-leather 27. Wear motorcycle riding boots 32. Wear motorcycle gloves 30. Wear body armour/impact protection for the elbows and shoulders 12. Run wide when going around a corner 38. Skid on a wet road or manhole cover, road markings, etc. 37. Find that you have difficulty controlling the bike when riding at speed (steering wobble) 13. Ride so fast into a corner that you feel you might lose control 36. Change gears when going around a corner 35. Brake or throttle back when going around a bend 24. Pulled away too quickly and your front wheel lifted off the road 23. Attempted or done a wheelie 21. Got involved in racing other riders or drivers ␣= Sum SD Mean SD

2. Speed Violations

3. Protective gear

4. Control Errors

5. Stunts

0.66 0.65 0.65 0.63 0.57

0.55 0.55 0.51

0.50

0.50 0.82 0.75 0.73 0.58 0.52 0.81 0.67 0.55 0.61 0.54 −0.81 −0.65 −0.58 −0.52 −0.50 −0.48 0.80

0.84 15.03 4.28 1.50 0.43

0.84 11.56 4.58 2.31 0.92

0.76 22.65 6.40 4.53 1.28

0.78 11.40 3.72 1.90 0.62

0.77 0.46 0.80 4.70 2.21 1.57 0.74

Table 4 Correlations between MRBQ factors.

Traffic Errors Speed Violations Protective gear Control Errors Stunts ***

Traffic Errors

Speed Violations

Protective gear

Control Errors

– 0.32*** −0.23*** 0.42*** 0.27***

– −0.08 0.35*** 0.51***

– −0.05 −0.01

– 0.35***

p < 0.001.

or near-crash reported less frequent traffic errors than those who had. The frequency of speed violation behaviours was higher for riders who had received a traffic violation notice when compared to those who had not. Further, those who had been involved in a crash reported more frequent speed violations than those who had

not been involved in a crash. The frequency of stunt behaviours was also significantly higher for riders reporting a near-crash than those not reporting a near-crash, and also for riders who had been involved in a crash when compared to those who had not.

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Table 5 Mean (SD) of summed MRBQ factors scores across rider demographics and crash-related variables. Range of possible scores:

Traffic Errors

Speed Violations

Protective gear

Control Errors

Stunts

10 to 60

5 to 30

5 to 30

6 to 36

3 to 18

Gender

Males (n = 405) Females (n = 52) z=

15.04 (4.31) 15.23 (4.29) < 1, r = 0.01

11.78 (4.54) 9.98 (4.43) −3.04**, r = 0.14

22.75 (6.30) 21.52 (7.21) < 1, r = 0.05

11.45 (3.67) 11.04 (4.24) 1.07, r = 0.05

4.83 (2.26) 3.81 (1.39) −3.25***, r = 0.15

Age group

16 to 20 (n = 19) 21 to 25 (n = 43) 26 to 39 (n = 105) 40 to 59 (n = 239) 60 + (n = 58) ␹2 (4) =

14.79 (4.60) 16.27 (4.82) 15.49 (4.54) 14.61 (4.04) 14.72 (3.80) 6.40

13.72 (5.28) 12.09 (4.42) 12.11 (5.15) 11.33 (4.27) 9.99 (4.20) 13.09*

21.31 (6.56) 21.17 (5.99) 22.19 (6.99) 22.98 (6.15) 23.70 (6.48) 7.14

12.63 (3.30) 11.96 (3.13) 11.32 (3.80) 11.42 (3.92) 10.43 (3.21) 9.91

4.89 (2.87) 5.00 (2.26) 5.08 (2.51)*** 4.64 (2.08) 3.84 (1.58)*** 14.24**

Reason for riding

Commute/Work only (n = 80) Recreation only (n = 185) Mixed/other (n = 207) ␹2 (2) =

16.07 (4.77) 14.56 (4.00) 15.05 (4.27) 6.03*

10.84 (4.70) 11.27 (4.40) 12.11 (4.65) 6.50*

20.18 (6.96)*** 23.47 (6.40)*** 22.88 (5.94) 15.25***

10.81 (3.59) 11.72 (3.94) 11.35 (3.55) 3.59

4.31 (2.13) 4.94 (2.44) 4.64 (2.00) 7.41*

Traffic Violations riding only Past 3 years

Yes (n = 50) No (n = 416) z=

15.25 (4.51) 14.98 (4.25) −0.28, r = 0.01

14.38 (4.81) 11.22 (4.44) −4.36***, r = 0.20

22.70 (6.14) 22.68 (6.43) −0.07, r = 0.003

11.84 (3.39) 11.36 (3.77) −1.21, r = 0.06

5.24 (2.86) 4.64 (2. 12) −1.34, r = 0.06

Near-crash Past 12 months

Yes (n = 339) No (n = 123) z=

15.41 (4.14) 13.99 (4.52) −3.99***, r = 0.19

12.09 (4.60) 10.29 (4.31) −1.94, r =0.09

22.72 (6.24) 22.47 (6.81) −4.16***,r=0.20

11.59 (3.34) 10.91 (4.63) −0.05, r = 0.001

4.79 (2.19) 4.52 (2.30) −2.81**,r = 0.13

Crashes Past 12 months

Yes (n = 57) No (n = 410) z

16.28 (4.69) 14.85 (4.20) −2.60**, r = 0.12

13.18 (5.02) 11.36 (4.48) −3.14**, r = 0.15

22.34 (5.88) 22.69 (6.47) −2.15, r = 0.10

12.44 (3.07) 11.27 (3.79) −0.79, r = 0.04

5.47 (2.42) 4.60 (2.17) −2.84**, r = 0.13

** p ≤ 0.01; ***p ≤ 0.001; Bonferroni adjustments were made for multiple comparisons. Table 6 Factors associated with crash involvement (past 12-months). Not crashed%

Crashed%

OR

95% CI

p-value

Age 16–20 21 − 25 26 − 39 40 − 59 60 +

63.2 83.3 88.6 90.8 84.2

36.8 16.7 11.4 9.2 15.8

Referent (1.0) 0.25 0.16 0.13 0.34

0.68–0.92 0.05–0.53 0.46–0.41 0.99–1.15

<0.05 <0.01 <0.001 0.08

Stunts (MRBQ) Lower quartile Second quartile Third quartile Upper quartile

96.2 90.3 81.1 82.1

7.4 9.7 18.9 17.9

Referent (1.0) 1.58 3.44 3.42

0.58–4.34 1.61–7.34 1.58–7.39

0.37 0.001 <00.01

Hosmer and Lemeshow test, p > 0.05; Area under the curve = 0.69.

3.5. MRBQ scores, crash risk, near-crashes and traffic violations Logistic regression analyses were conducted to investigate the association between three outcomes of interest, these being: 1) crashes in the past 12-months; 2) near-crash in the past 12months; 3) speed-related traffic violations in the past 3-years, and MRBQ factor scores. For each dependent variable (crash, nearcrash, speed-related traffic violations), statistical model building commenced with an initial model that included age (across the five groups), gender and average weekly kilometres (low ≤ 100 km; medium ≤ 300 km; high > 300 km) and years riding (less than 2; 3–10; 11 or more). Given that riding style might differ according to the purpose for the ride, the purpose for riding (commuting or work only; recreational only; multipurpose or other) was also included in the model. Scores for each MRBQ factor were separated into quartiles (lower, second, third, upper) to obtain a better understanding of the relationships between frequency of MRBQ behaviours and each of the three outcomes examined. For example, it is conceptually easier to understand the odds of crash involvement compared between low and high frequency aberrant riding behaviours, than what the odds per one unit increase in MRBQ factor frequency represent.

3.5.1. Crash involvement in past 12-months Table 6 shows the results of the regression analysis for crash involvement. The final model was significant (2 (7) = 26.43, p < 0.001) and showed rider age and performance of stunts to be significantly associated with crash involvement. To test whether gender and kilometres confounded the relationships between stunts and crash involvement, a further model was run where age, gender, kilometres and stunts were forced into the model. The differences in co-efficient values were negligible and therefore showed gender and average weekly kilometres did not confound the relationship between stunts and crash involvement, when age was controlled for. As can be seen in Table 6, after controlling for the effect of age, the odds of crash involvement were threefold for riders who reported frequent stunt behaviours (scoring the third and fourth quartiles), when compared to those who reported the least frequent stunt behaviours (ORthirdquartile = 3.44, CI: 1.61,7.34/ORupperquartile = 3.42, CI: 1.58,7.39). 3.5.2. Near-crash involvement in past 12-months Following the same model building process described above, riding purpose, increased frequency of speed violations and more frequent control errors were found to be significantly associated

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Table 7 Factors associated with near-crash involvement (past 12-months). No near- crash%

Near-crash%

OR

95% CI

p-value

Main reason for riding Commute or work Recreation Other/mixed

19.0 35.5 21.5

81.0 64.5 78.5

Referent (1.0) 0.36 0.75

0.19–0.70 0.38–1.49

<0.001 0.42

Speed Violations (MRBQ) Lower quartile Second quartile Third quartile Upper quartile

39.7 29.8 15.5 19.8

60.3 70.2 84.5 80.2

Referent (1.0) 1.48 3.44 2.04

0.85–2.59 1.82–6.48 1.02–4.07

0.17 <0.001 <0.05

Control Errors (MRBQ) Lower quartile Second quartile Third quartile Upper quartile

36.9 24.0 17.9 21.6

63.1 76.0 82.1 78.4

Referent (1.0) 2.21 1.27 2.05

1.15–4.18 0.71–2.26 1.09–3.87

<0.05 0.43 <0.05

Hosmer-Lemeshow test p > 0.05; Area under the curve = 0.70.

with experiencing a near-crash (2 (8) = 44.01, p < 0.001). Once again, a further model was conducted with age, gender and weekly kilometres in to the model to test whether these were confounding factors. The resulting estimates showed negligible change indicating that the relationships between speed violations and control errors with near-crash involvement were not confounded by age, gender or average weekly kilometres travelled. Table 7 shows that relative to those who reported the least frequent speed violations, the odds of crash involvement for riders who reported the most frequent speed violation behaviours were between two to three times greater (third quartile; OR = 3.44; CI: 1.82,6.48; upper quartile; OR = 2.04; CI: 1.02,4.07). Riders who reported the most frequent control errors had twice the odds of crash involvement compared to those who reported the least control errors (OR = 2.05; CI: 1.09,3.87). 3.5.3. Traffic violations in past 3-years Given that MRBQ scores showed associations with the occurrence of both crashes and near crashes, it was considered to be of value to examine the association between MRBQ scores and traffic violations. In particular, traffic violations committed while riding and relating to excess speed were considered. As can be seen in Table 8, Speed Violations were significantly associated with selfreported speed-related offences (2 (3) = 25.43, p < 0.001) and this relationship was found not to be confounded by age, gender or distance ridden. As can be seen in Table 8, the odds of receiving a traffic violation for riders who reported the most frequent violations (upper quartile) was significantly higher than for riders reporting the least frequent violations (OR:30.79, CI: 4.00, 237.27). However, it should be cautioned that, as is to be expected, a very small proportion of the sample reported low frequency Speed Violations and having received a speed-related violation (0.8%) which in combination with the relatively small sample size influences the reported confidence intervals. Notwithstanding this, a significant relationship between MRBQ Speed Violations and receiving a traffic infringement notice is evident. 4. Discussion The aims of the current study were to understand the most appropriate structure of the Motorcycle Rider Behaviour Questionnaire (MRBQ) for use with riders in Australia and to determine which factor/s are associated with crash involvement, near crash involvement and receiving a speed-related traffic infringement notice. Exploratory factor analysis revealed a 29-item, five factor solution with the resulting factors being conceptually similar to previously suggested factors of traffic errors, speed violations, protective gear, control errors and stunts. However, in our study

fewer items were included in each factor. For example, traffic errors contained eight items, speed violations and protective equipment contained five items each, control errors contained six items and stunts had three items. Of the five factors, only stunts was significantly associated with crash involvement. Riders who reported frequent engagement in stunt behaviours had three times the odds of crash involvement compared to riders who infrequently performed stunts. While the previously reported item-to-factor structure of the MRBQ was not replicated in this sample, the analysis showed some consistencies across the broad factors used. In the current study, with the exception of only three items (item 9, 21, 32) all MRBQ items that were retained in the final 29-item version were also those retained, in the same factors, across the three alternative structures (Elliott et al., 2007; Sakashita et al., 2014; Sexton et al., 2004). Therefore, there are a number of robust items in the MRBQ. Likewise, there are a number of items that are dropped by researchers or produce inconsistent loadings. For example, item 41 “Ride when you suspect you might be over the legal limit for alcohol” was dropped by all researchers (Elliott et al., 2007; Özkan et al., 2012; Sakashita et al., 2014; Sexton et al., 2004). Stephens and Fitzharris (2016) also found low scores on this item when assessing the validity of the Driver Behaviour Questionnaire (DBQ) in a representative sample of motorists; leading them to recommend the removal of this item from the DBQ for Australian samples. They argued that strong enforcement and benefits of targeted drink driving campaigns have been effective in curbing this behaviour. Data from the current study suggests this holds true for motorcyclists as well. The mean responses for each of the 29 items retained with the Australian sample allowed better understanding of which frequent behaviours were reported by riders. When individual items were considered, the highest scores were for protective gear items. Riders reported nearly always using daytime headlights on their motorcycle (bike) (item 34), which is unsurprising as many motorcycles have this feature fitted as standard equipment. Similarly, riders reported nearly always wearing protective gear such as motorcycle gloves (item 32) and a protective jacket (item 29). However, riders reported hardly ever wearing a full leather suit, suggesting that a protective jacket and trousers were preferred over a one-piece suit. This may be because one-piece suits are generally designed and worn for racing and are less appropriate or convenient for road riding. In contrast, behaviours almost never undertaken by riders included riding when under the influence of alcohol (item 41) and not wearing any protective clothing (item 31). The overall means of the five factors show that the group on a whole adopted relatively safe riding practices. For example, they frequently wore protective

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Table 8 Factors associated with receiving a speed-related traffic violation (past 3-years).

Speed Violations (MRBQ) Lower quartile Second quartile Third quartile Upper quartile

No traffic violation%

Traffic violation%

OR

95% CI

p-value

99.2 91.8 92.3 81.2

0.8 8.2 7.7 18.8

Referent (1.0) 11.55 10.95 30.79

1.46–91.63 1.38–86.85 4.00–237.27

0.02 0.02 0.001

Hosmer-Lemeshow test p > 0.05, Area under the curve = 0.71.

gear (4.53 out of 6.00) and reported infrequent traffic errors while riding (1.50 out of 6.00) and hardly ever performed stunts while riding (1.57 out of 6.00). The most frequently reported aberrant behaviour was speed violations (2.31 out of 6.00); however this noncompliance with speed regulations was reported as hardly ever occurring. This pattern of mean responses from the sample was similar to that reported by Elliott et al. (2007) and Sakashita et al. (2014), providing support for the shortened version of the MRBQ. The relationships that emerged between rider characteristics and the MRBQ make it possible to profile riders likely to engage in aberrant behaviours. Male riders reported more frequent speed violations and stunts when compared to female riders. This has also been found in a sample of novice riders from Australia (Sakashita et al., 2014); however other researchers have not examined gender effects on MRBQ behaviours (Elliott et al., 2007; Özkan et al., 2012). This may be because males constitute the majority of the riding population and therefore representative samples will invariably be male dominated (Harrison and Christie, 2005; Road Traffic Authority, 2012). Our results also demonstrate that riders aged 60 years and older reported less stunt behaviour than riders aged 26–39. Therefore, male riders and those aged 26–39 may perform stunts more often than female or older riders. Stunts was the only MRBQ factor associated with crash involvement. Riders who reported the most frequent engagement in motorcycle stunts, which included racing with other riders or drivers and riding on one wheel had three times the odds of crash involvement when compared to riders who reported very infrequent to no stunts. Therefore, although stunts overall are reported as infrequent behaviours they may be indicative of riders who exhibit the types of behaviours that put them at greater risk of crash involvement. These results align with previous research showing relationships between frequency of stunts and involvement in crashes reported by the Police recorded crashes (Sakashita et al., 2014) or self-reported at-fault crashes (Özkan et al., 2012). Furthermore, in the current study, more frequent speed violations and control errors were significantly associated with having been involved in a near-crash in the past year. Riders reporting the most frequent speed violations had twice the odds of involvement in a near-crash, when compared to the low violation riders and this was the same for control errors. The odds of near-crash involvement were two-fold for riders with high frequency control errors in comparison to riders with low frequency control errors. Hence, near-crashes were associated with a higher speed violation and control error MRBQ score. These findings are interesting because speed violations are defined as intentional behaviours whereas errors are unintended behaviours that may also be influenced by the presence and behaviour of other roadusers. Speed violations was significantly associated with having received a traffic offence notice related to speeding while riding in the past three years. As self-reported incidence of speeding behaviours increased so did the odds of having speed-related traffic offences. While this result is to be somewhat expected, it aligns with previous research highlighting relationships between aberrant riding behaviours and traffic infringement notices (e.g. Elliott et al., 2007; Özkan et al., 2012).

Taken more broadly, our results show clear associations between each of the aberrant type behaviours and increased crash risk. Further, our findings make clear that riders whose characteristic riding style includes the performance of stunts have increased odds of crash involvement. In addition, the results highlight an association between speed violations and control errors with nearcrashes. While speed violation behaviours and control errors were not directly associated with crash involvement, given their association with near-crashes there is likely to be an indirect association between these behaviours and crash risk. This suggestion warrants further exploration. 4.1. Limitations The findings are subject to the perceived weakness of self-report methodology, related to social desirable responding and common method bias. However, as participants were able to complete the questionnaire at home if desired, the influence of social desirability is likely to be minimal (Lajunen and Summala, 2003). Further, as participants were volunteers, it is possible that some degree of volunteer bias may be present where responses from volunteers may differ from non-volunteers. However, the sample was representative of the population of registered motorcycle owners and the age and gender distributions are similar to the distributions on the NSW motorcycle registration database (Road Traffic Authority, 2012), suggesting the sample is still matched to the desired population. 4.2. Practical implications In their review of risk factors for crash and serious injury, Lin and Kraus (2009) found the key contributors to fatal crashes include alcohol, inexperience, conspicuity of the motorcycle, riding speed and risk-taking behaviours. Data from the riders in NSW suggest that riders very infrequently ride while under the influence of alcohol or without daytime lights, bearing in mind that on most motorcycles in Australia headlights are hard-wired to operate when the engine is running. It should be noted that the MRBQ question related to alcohol specifically refers to being “under the influence” of alcohol, which is likely to be interpreted as being legitimately intoxicated. This may differ slightly from responding about whether the rider had consumed alcohol or not. Non-compliance with posted speed limits and risk-taking in the form of stunts occurred more often for the riders in our sample from NSW compared to the previous studies, and while relatively infrequent these have been shown to be related to near-crashes and crashes respectively. While speed violations were not significantly associated with crash involvement, the MRBQ speed violation score was associated with near-crashes and, as expected, speeding offences. These findings highlight the value of the MRBQ in identifying riders who have an underlying propensity to engage in risky behaviour. Control errors were also related to increased risk of near-crash. Common themes in these two factors are speed. For example, riding so fast around a corner or having difficulty in controlling the motorcycle when driving at speed are two of the control error

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items. Therefore, interventions need to target the reduction of these deliberate behaviours or behaviours that lead to lack of motorcycle control and one starting point for this is to address rider speed. Chosen speed can be related to attitudes regarding speeding and therefore speed reduction is embedded within improving these attitudes. For example, Elliott and Thomson (2010) and Özkan et al. (2012) have investigated behaviour intentions from a psychological perspective with results suggesting social norms and perceived acceptability of the behaviour are key in determining behaviour and can potentially be used to reduce aberrant riding behaviours. Likewise, Stephens et al. (under review) investigated self-reported speed behaviour in a large representative sample of drivers in Australia (N = 5, 179) and found that drivers who frequently exceed the speed limit are more likely to underestimate the risk associated with increased vehicle speeds, less likely to perceive they would get caught and more likely to have friends and family who also drive over the speed limit. Further, the same attitudes have been found when investigating drivers who report driving when over the legal BAC limit. In comparison to drivers who do not drink and drive, those do who are more likely to drive when they believe they will not get caught, perceive less crash risk associated with drink driving and have a larger number of friends and family who drink drive and do so more often (Stephens et al., 2017). Indeed, increased enforcement coupled with media campaigns promoting drinking and driving behaviour as being socially unacceptable have been successful in reducing the incidence of this behaviour on Australian roads (Fitzharris et al., 2015). The same should be considered when identifying appropriate countermeasures for other risky-riding behaviours. Following from above, and, as the current study demonstrated a robust relationship between MRBQ Stunts and self-reported crashes, measures to address these behaviours – and the underlying propensity to engage in these – are required. Following the success seen in other domains, behavioural countermeasures that lead to an increase in perceived enforcement may be of benefit. Similarly, programs that highlight the elevated crash risk for motorcyclists associated with engaging in these types of behaviours may also be of benefit. Although we may note that the motorcyclists in our sample engaged in these behaviours infrequently, it is nonetheless the case that these crashes are likely to be preventable and so addressing this is a worthy goal. Taken together, the results highlight that crash risk for riders in Australia can, at least in part, be attributed to intentional behaviours. While the MRBQ has been used elsewhere with similar findings, and indeed in Australia with novice riders, these findings make an important contribution to current knowledge regarding the over-representation of riders in our crash statistics. Özkan et al. (2006) suggest that violation behaviours are bound within the culture of driving environment. Therefore, it is important to understand self-reported frequencies of each type of aberrant behaviour in a sample of Australian riders. Further, extrapolating findings from drivers in Australia suggests that reductions of these intentional behaviours, such as stunts, speed violations and speeds which adversely impact vehicle handling may be achieved by increasing perceived enforcement and focussed educational programs aimed at reducing the social acceptability of these behaviours.

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