Factors Associated with Motorcycle Crashes in New South Wales, Australia, 2004 to 2008

Factors Associated with Motorcycle Crashes in New South Wales, Australia, 2004 to 2008 Liz de Rome and Teresa Senserrick In New South Wales, Australia, registered vehicle counts have relatively low error and, although imperfect, are the best measure of exposure currently available. The age and gender of the registering owner is collected as part of the vehicle registration process in New South Wales, thus allowing for population-based studies. The crash database, which is compiled from police crash reports, provides substantial detail on factors associated with reported crashes and controllers involved; this detail allows exploration by a range of crash factors including crash severity, type of vehicles involved, and contributing factors such as road environment and road user behavior.

The data can also be linked to vehicle registration and license databases, to allow exploration by license status. Together these data sets offered a valuable opportunity to explore PTW crash factors in depth on the basis of data routinely collected by state authorities.

The objective of this research was to identify key risk factors for PTW crashes and the severity of crashes in New South Wales during the 5-year period 2004 to 2008, including rates and proportions by demographics, crash factors, and behavioral factors.

This research aimed to identify factors associated with powered twowheeler (PTW) crashes in New South Wales, Australia. An exploratory analysis was conducted on data from state crash, license, and vehicle registration databases for 2004 to 2008. Over the study period, PTW registrations and crashes increased (39% and 17%, respectively), but crash rates and fatality crash rates per 10,000 registered vehicles decreased (from 215.9 to 180.9 and from 5.7 to 3.7, respectively). Forty-one percent of PTW crashes were single-vehicle crashes; 49% occurred on curves, with road surface hazards contributing to 23%. Single-vehicle crashes accounted for 43% of all PTW fatalities. Other vehicle drivers were deemed at fault in 62% of multivehicle crashes, including 71% at intersections. T-junctions were the site of 30% of all multivehicle crashes. Riders were most likely to be at fault in rear-end (62%) and head-on (82%) crashes. The majority of head-on crashes were not overtaking (69%), and of these 83% occurred on curves. Super sport models had the highest crash rate per 10,000 registered motorcycles (284.6). Young riders were overrepresented in crashes (9% of registrations, 28% of crashes), and unlicensed riders, in fatal crashes (7% of crashes, 26% of fatal crashes).

Unlicensed riders represented 41% of casualties not wearing helmets and 26% of all riders with an illegal concentration of alcohol. Although PTW crash rates showed an encouraging decline, countermeasures were found to be needed to protect the increasing numbers of riders. The analysis recommended head-on, rear-end, and intersection crashes as specific crash risk patterns to be targeted in education and training for riders and drivers; road treatments in high-risk locations; and interventions to address high-risk unlicensed riding.

METHODS Data Source and Variables Data for 2004 to 2008 were obtained from New South Wales state crash and registration databases. Police-reported crashes include those on public roads involving at least one moving vehicle and in which any person is killed or injured or a vehicle is towed away (5).

The key vehicle is generally defined as the one considered to have played the major role in the crash (6). This variable was utilized as a best-available proxy for at-fault status, with the acknowledgment that this use is not the one intended by the Roads and Traffic Authority and that in a small (unknown) number of scenarios the controller of the vehicle may not have been at fault.

Variables include demographic details (age, gender, license status of rider), crash severity (injury, fatality), number of vehicles (single or multiple), vehicle type, at-fault status, crash type (e.g., overtaking, head-on), road type (intersection, speed zone), hazards (e.g., potholes), alignment (curved or straight), and behavioral factors (helmet, speed, alcohol, rider or driver error). Age was coded into previously identified motorcycle crash risk groups: 17 to 25 (young riders), 26 to 39, and older than 40 years (7).

New South Wales has a four-stage graduated licensing system for riders (8). Applicants (16 years and 9+ months) complete a 2-day learn-to-ride course to obtain a learner license. They progress to a first intermediate license by completing a 1-day riding course and operational skills test (minimum age 17). After 12 months they progress to

Motorcycle and scooter riders represent an increasing proportion of road traffic casualties around the world. This increase is due to a resurgence of riding of powered two-wheelers (PTWs) in high-income countries and the increased motorization in low- to middle-income countries (1, 2) As vulnerable road users, riders of PTWs have high rates of serious injuries and fatalities (3).

Strategies to reduce the crash and injury risk of PTWs depend on the accurate identification of causes and risk patterns, including demographic and behavioral factors and exposure. Commonly used measures of exposure for PTWs include the number of licensed riders, vehicle kilometers or miles traveled, and registered vehicles (4).

George Institute for Global Health, University of Sydney, P.O. Box M201, Missenden Road, Camperdown, New South Wales 2050, Australia. Corresponding author: L. de Rome, lderome@georgeinstitute.org.au.

Transportation Research Record: Journal of the Transportation Research Board, No. 2265, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 54–61.

DOI: 10.3141/2265-06

54

de Rome and Senserrick

TABLE 1

55

Crash Rate by Classification of Registered PTWs, New South Wales, 2008

Classification Super sport Scooter Sport, unclad Standard Sport, touring Dual purpose Cruiser, custom Touring Off-road Other or unclassified Total

Crashes (N)

All Crashes (%)

Registered (n)

Registered (%)

Crash Rate (per 10,000 PTWs)

503 205 337 219 221 47 257 115 189 602 2,695

18.7 7.6 12.5 8.1 8.2 1.7 9.5 4.3 7.0 21.0 100

17,675 7,813 13,601 8,865 10,995 3,227 19,522 11,214 23,586 30,085 146,583

12.1 5.3 9.3 6.0 7.5 2.2 13.3 7.7 16.1 20.5 100

284.6 262.4 247.8 247.0 201.0 145.6 131.6 102.6 80.1 200.1 183.9

a second intermediate license and after a further 24 months to full licensure. Learner and intermediate riders are subject to restrictions including motorcycle size and maximum speed. Helmets are mandatory for all Australian riders.

Common speed zones in New South Wales range from 30 to 110 km/h (18.6 to 68.4 mph) in 10-km (6.2-mi) increments. These were grouped into four categories: up to 50 km/h (residential areas), 60 km/h (main collector roads), 70 to 90 km/h (divided roads, arterials), and 100+ km/h (highways, motorways). PTWs in the registration database were classified into nine vehicle types (Table 1) based on make, model, and engine displacement in cubic centimeters.

PTWs showed decreases for all crashes and injury and fatal crashes (Figure 1).

Age of Riders The average age of PTW riders in 2008 was 43 years. The number of registered owners 40 years of age and older has increased by more than 28,000 since 2004, whereas the number age 26 to 39 increased by less than 8,000 and those age 17 to 25 by only 2,189. By 2008, riders 40 years of age and older represented 57% of all registrations (Figure 2).

Analyses

Crash Rates by Age Group Crash data were obtained in MS Access 2007 and registration data in MS Excel 2007. Files were linked with MS Access 2007 and a descriptive analysis was conducted with MS Excel 2007. A range of analyses was selected to feature differing crash associations with demographics, crash factors, and behavioral factors. First, the overall pattern of crashes from 2004 to 2008 was explored by number and percentage change over time and rates per 10,000 registered PTWs, including by age group, speed zone, and alcohol use [illegal blood alcohol content (BAC) ≥ 0.05 g/100 mL]. Second, crashes and crash rates by PTW type were calculated for 2008. Finally, a range of factors by number, proportion, and rate for 2004 to 2008 collectively was determined: single-vehicle crashes (SVCs) and multivehicle crashes (MVCs), including by key vehicle, road alignment, and crash type; rider or driver error; vehicle type; age group; and road type by license status and age group, including a focus on young riders and unlicensed riders.

RESULTS Crash Patterns by Year, 2004 to 2008

Crash and Injury Outcomes From 2004 to 2008 there were 12,257 crashes involving PTWs in New South Wales, including 305 fatal crashes. Over that period PTW registrations increased by 36% and the number of PTW crashes increased by 17% (Table 2), but the crash rate per 10,000 registered

Despite overall decreases, the crash rate for young riders remained much higher than that for older groups. Riders age 17 to 25 were involved in 28% of reported crashes over the 5 years. In 2008 they had 606 crashes per 10,000 registered vehicles compared with 211 crashes for riders age 26 to 39 and 115 crashes for those 40 years of age and older. Figure 3 illustrates crash rates by age group.

Speed Zone Most PTW crashes (69%) occurred on roads zoned for 60 km/h (37.3 mph) or less. Only 12% of the crashes occurred in zones for 100 km/h (62.1 mph) or more. A lower proportion (43%) of fatal PTW crashes occurred in areas zoned for 60 km/h or less, and 25%

Number of Crashes Involving PTWs and Percentage over 5 Years, New South Wales, 2004–2008 TABLE 2

Severity of Crash Fatality Injury No casualty (tow away) Total

2004

2005

2006

2007

2008

Change (%)

60 2,002 211

63 2,019 216

66 2,258 214

62 2,196 239

54 2,372 225

−10 18 7

2,273

2,298

2,538

2,497

2,651

17

56

Transportation Research Record 2265

Injury crashes 211.0

185.8

5.6

202.7

178.1

5.6

All crashes

Fatal crashes

occurred in areas zoned for 100 km/h or more. The small numbers involved (∼60 per year) meant that no trends could be identified.

206.8 187.0

180.9

164.5

161.8

184.0

5.4

4.6

3.7

Alcohol From 2004 to 2008, 3.2% of PTW crashes involved a rider with an illegal BAC compared with 2.4% of all vehicle crashes. Crashes in which the rider had an illegal BAC accounted for 16.3% of all fatal PTW crashes compared with 13.0% of all vehicle controllers in fatal crashes.

When alcohol was involved in an PTW crash, it was more likely to be the rider (4.7%) than the other driver (0.5%) who had the illegal BAC.

PTW Crashes 2004

2005

2006

2007

2008

FIGURE 1 Crash rates per 10,000 registered PTWs in New South Wales, 2004–2008.

FIGURE 2

Sufficient information was available to classify 79% of the PTWs in reported crashes and 80% of PTWs in the 2008 registration database (Table 1). Whereas the average crash rate per 10,000 registered PTWs

Age of registered owners of PTWs in New South Wales, 2004–2008.

FIGURE 3 Crash rates per 10,000 registered owners by age group in New South Wales, 2004–2008.

de Rome and Senserrick

57

was 183.9, this rate varied by class of PTW. The highest crash rates were for super sports (284.6) and scooters (262.4) followed by unclad sport (247.8) and standard (247.0) motorcycles. Super sports were substantially overrepresented in fatal crashes (30.4%), but their fatal crash rate was comparable with that of dual-purpose PTWs (9.62 versus 9.30).

TABLE 3 Crashes by Vehicle Classification and Crash Category, New South Wales, 2004–2008

SVCs and MVCs

Key Vehicle Crashes were categorized to distinguish between SVCs (41%) and MVCs according to whether the key vehicle was the rider (23%) or the other driver (35%) (Figure 4). The distribution of crash types as in Figure 4 is similar within PTW classifications, although scooters were more likely to be in MVCs caused by the other vehicle and dual-purpose bikes were more likely to have SVCs (Table 3).

SVC Factors SVCs accounted for over two-fifths (43%) of all PTW rider and pillion fatalities. SVCs were almost equally likely to have occurred on curved as on straight road sections (49% versus 51%), but most fatal SVCs (75%) occurred on curves.

Excessive speed for the conditions was identified as a contributing factor in almost half of all SVCs (Table 4). Road surface hazards, such as potholes and diesel or loose gravel on a sealed surface, were contributing factors in almost one-fifth of these crashes. Such hazards were more commonly associated with crashes on curves than on straight sections (23% versus 14%) and were a contributing factor in 10% of fatal crashes on curves. Animals on the road were identified as a contributing factor in a further 6% of SVCs.

MVC Factors The proportion of multivehicle PTW crashes remained constant from 2004 to 2008. Overall, the other driver was the key vehicle in 61% of all MVCs. Riders were most likely to play the major role in

Rider key vehicle (n=620),23%

Other driver (n=935),35%

Crash Category

SVC Rider Only

MVC, Other Driver Key Vehicle

MVC, Rider Key Vehicle

All Crashesa

Classification

No.

%

No.

No.

No.

Super sport Scooter Sport, unclad Standard Sport, touring Dual purpose Cruiser, custom Touring Off-road Otherb Unclassified

874 189 420 461 404 91 475 127 410 51 1,472

39 27 39 35 39 48 41 40 42 38 45

849 366 427 569 408 68 452 120 383 40 977

38 52 40 43 40 36 39 38 39 30 30

535 146 226 304 218 31 222 73 189 42 844

24 21 21 23 21 16 19 23 19 32 26

2,258 701 1,073 1,334 1,030 190 1,149 320 982 133 3,293

Total

4,974

40

4,659

37

2,830

23

12,463

FIGURE 4 Key vehicle by crash category in New South Wales, 2004–2008.

%

a

Model details were available to classify an average of 74% of motorcycles involved in crashes, 2004–2008. b Other category includes mopeds, minibikes, mobility vehicles, all-terrain vehicles, and other small classes and special-usage vehicles.

rear-end and head-on crashes. The PTW was the key vehicle in 62% of all rear-end PTW crashes (n = 1,326), which included 29% of all MVCs in which a PTW was the key vehicle.

The PTW was also the key vehicle in 82% of all head-on crashes (n = 630), including 88% of overtaking crashes (n = 197) and 62% of nonovertaking crashes (n = 433). The majority (83%) of head-on (nonovertaking) crashes occurred on curves. Figure 5 illustrates the types of crashes in which the rider played the major role compared with the other driver.

The most common errors by the other driver were failure to see or give way at an intersection (48%), changing lanes (19%), and entering traffic (10%). Collisions with heavy vehicles such as trucks made up 4% of MVCs but 18% of fatal MVCs. Crashes involving light trucks were more frequent (10%) and included 19% of fatal MVCs. By comparison, collisions with cars were far more common

TABLE 4 Proportion of Factors Involved in SVCs by Road Alignment, New South Wales, 2004–2008

Factor

Single vehicle (n=1096),41%

%

All SVCs Excess speed for conditions Fatigue Road surface hazard Animal on road 17–25 years of age Older than 40 years of age

All Crashes (%) (N = 4,975)

On Curves (%) (n = 2,431)

On Straight Roads (%) (n = 2,543)

100 48

49 84

51 13

15 18 6 26

12 23 3 23

17 14 9 29

36

40

32

58

Transportation Research Record 2265

Almost one-third (30%) of all PTW MVCs occurred at T-junctions, with the other driver the key vehicle in 70%. Crossroads accounted for 19% and roundabouts only 6% of collisions. The other vehicle in intersection crashes was most likely to be a car (82%) or light truck (9%).

License Status Unlicensed riders were more likely to be the key vehicle in both intersection and nonintersection crashes (49% and 69%, respectively) than learners (30% and 47%, respectively), intermediate (27% and 44%, respectively), or fully licensed riders (26% and 44%, respectively) (Figure 6). Riders from other Australian states were more likely than local, New South Wales-licensed riders to be the key vehicle in both intersection and nonintersection crashes.

FIGURE 5 At-fault vehicle in MVCs in New South Wales, 2004–2008.

Age Group

Young riders were more likely to be involved in MVCs (63%) than in SVCs, whereas just over half (54%) of crashes for riders 40 years of age and older were MVCs. In addition, although the other driver was generally more likely to be the key vehicle (62%), young riders were more likely than older riders (42% versus 34%) to be at fault.

On first comparison, the three selected age groups showed similar involvement in intersection crashes; however, further division into subgroups of ages 17 to 20, 21 to 25, 26 to 39, 40 to 59, and 60 years and older revealed a different pattern. The youngest riders were most likely to be at fault in intersection crashes (Figure 7). Riders age 17 to 20 were at fault in 50% of MVCs, including 42% of intersection and 61% of nonintersection crashes. This finding compared with 37%, 28%, and 47%, respectively, for those age 21 to 25.

Riders 60 years of age and older were also somewhat more likely to be at fault in intersection and nonintersection crashes (32% and 50%, respectively), although the total number of crashes involving this age group was relatively small (n = 227).

Intersection Crashes

Crashes by License Status

Over half (56%) of all MVCs occurred at intersections. PTWs were the key vehicle in 39% of all MVCs but only 30% of those at intersections. The other driver was the key vehicle in 70% of intersection crashes. Responsibility for nonintersection crashes was equally likely to be the rider (48%) or other driver (52%).

Unlicensed Riders

(79%) but accounted for a comparatively lower proportion of fatal collisions (59%).

Differences by Age Group

More than half of all unlicensed riders were age 17 to 25 (55%).

They included 7% of all PTW crashes but 26% of all riders in fatal crashes. Unlicensed riders were more likely than licensed riders to be

FIGURE 6 Proportion of intersection and nonintersection MVCs with rider as key vehicle by license status in New South Wales, 2004–2008.

de Rome and Senserrick

59

FIGURE 7 Proportion of riders as key vehicle in intersection, nonintersection, and all crashes by age group in New South Wales, 2004–2008.

at fault in an MVC (59% versus 34%) and in speed-related crashes (29% versus 23%). Nearly one-fifth (17%) of injured unlicensed riders were unhelmeted (or wearing an incorrectly fastened helmet) and accounted for 41% of all unhelmeted PTW casualties. In 2008, unlicensed riders made up 26% of riders with illegal BACs (17% of unlicensed riders compared with 3% of licensed riders).

Crashes involving unlicensed riders were more likely than those with licensed riders to involve a pillion casualty (6% versus 5%) and their pillion casualties were more likely to be unhelmeted (2% versus 0.1%). Unlicensed rider crashes accounted for 9.1% of all pillion casualties and 35.6% of all crashes in which a pillion casualty was not wearing a helmet.

licenses (41% versus 34%), although it must be noted that the large number of unknown cases makes this interpretation tentative (see Table 5).

DISCUSSION OF RESULTS Over the study period, from 2004 to 2008, PTW registrations and crashes increased in New South Wales; however, crash, fatality, and injury rates per 10,000 registered vehicles showed decreases. The reasons for the relative decline in crash rates has not been established but may be part of the general decrease in all crashes over that period (5). Several contributing factors may be considered but are untested. These factors include improvements in emergency retrieval and treatment contributing to reduced fatalities (9) but do not account for the relative reduction in crashes. Mandatory helmet laws, compulsory rider training, and random breath testing have been in place for many years. Educational programs aimed at rider and driver behavior together with the increased presence of motorcyclists on the

Riders from Other Locations Riders from other Australian or overseas jurisdictions represented 5% of all riders involved in fatal crashes and 55% of SVCs and were more likely to be at fault in MVCs than those with New South Wales

TABLE 5

Proportion of Riders in Crashes by License Status and Factors Associated with Crashes, New South Wales, 2004–2008 License Status

Factor All crashes Casualty crashes Fatal crashes MVC (key vehicle) SVC Fatigue Speed Casualty without helmet Pillion casualty without helmet Pillion casualty Younger than 26 years of age Older than 40 years of age

All Riders (N = 12,465)

Learner (n = 1,254)

Intermediate (n = 977)

Standard (n = 7,038)

Unlicensed (n = 917)

Other Jurisdiction (n = 485)

Unknown (n = 1,794)

100 100 100 38 40 7 24 3 0 5 28 31

10 10 4 37 39 6 22 0 0 1 66 7

8 8 4 34 33 4 20 1 0 1 64 2

56 56 61 34 39 5 23 0 0 4 13 43

7 7 26 59 42 12 29 17 2 6 55 13

4 4 5 41 55 10 35 5 1 27 21 42

14 15 1 46 44 9 25 9 1 6 29 24

60

road may be factors. As with the cited benefit of safety in numbers for cyclists (10), research suggests that driver error may be associated with lack of awareness in motorcycle crashes (11, 12).

Riders age 17 to 25 were the registered owners of only 9% of registered PTWs but were involved in 28% of reported crashes.

They were also more likely to be at fault in MVCs than riders 40 years of age and older, particularly at intersections. Although data on registered owners do not account for unlicensed riders, there is evidence that the number of registered owners age 17 to 25 is likely to be a reasonable estimate of that age group in the rider population in New South Wales. A recent New South Wales study found that the majority (84%) of learner riders age 17 to 25 were the registered owners of their PTWs with only 12% riding PTWs registered to another family member (13).

The majority of crashes occurred on roads zoned for 60 km/h (37.3 mph) or less, suggesting that most occurred in predominantly residential areas. The relatively high crash rates for commuter machines such as scooters and standard bikes are consistent with this finding and seem likely due to exposure in urban environments; however, travel data were not available.

The proportion of PTW crashes involving alcohol was higher than that for all vehicle crashes, with the rider most likely to record the illegal BAC in a MVC. Crashes in which the rider had an illegal BAC accounted for 16.3% of all fatal PTW crashes compared with 30% in the United States (14). The relatively lower proportion of motorcycle fatalities associated with alcohol in New South Wales may be partly attributed to the established random breath testing program (15). Other studies have commented that random breath testing results demonstrate that very few motorcyclists drink and ride and that those who do are more likely to be unlicensed (16). The 2007 injury rate in New South Wales is higher than the U.S. rate (166.5 versus 144.3), but the fatality rate is lower (4.7 versus 7.3) (14).

The latter finding is likely due to the mandatory helmet requirements and high compliance in Australia.

Analysis of motorcycle crashes by crash type allows recognition of characteristic error patterns associated with different types of crashes, which may be obscured when crash types are aggregated (17–19). Their identification is valuable in the development of countermeasures. PTWs had a much higher incidence of SVCs than has been found for cars (41% versus 24%) (20). Although SVCs were equally likely on straight roads as on curves, three-fourths of all fatal SVCs occurred on curves. Excessive speed for the conditions may have been a factor in almost half of all SVCs, with road surface hazards contributing to one-fifth, higher on curves and including onetenth of fatalities on curves. There is evidence that such hazards can be reduced by road remediation programs designed to address hazards specific to motorcycles; such programs have achieved substantial reductions (37%) in rider casualty crashes after adjustment for exposure (21).

Other drivers were deemed at fault in over 60% of MVCs, with almost half involving failure to see or give way to the rider at an intersection. T-junctions were the most dangerous intersection for PTWs, being the location of almost one-third of all MVCs. By comparison, a somewhat smaller proportion of all vehicle crashes (25%) occur at T-junctions and 17% at crossroads; this statistic suggests that this finding is unlikely to be due to greater exposure to T-junctions than to other intersection types (5). Similar findings in Malaysia have led to the development of specific engineering treatments to reduce such risks for PTWs (22, 23).

Transportation Research Record 2265

In addition to SVCs, rider errors resulting in MVCs were more commonly rear-end and head-on (mostly not overtaking) crashes.

Recognition of patterns of rider and driver errors can be used to inform countermeasures including advice to riders about appropriate speed, looking ahead, positioning on the road, and maintaining crash avoidance space in rider education and licensing materials (8).

Recognition of the contribution of common patterns of driver errors in motorcycle crashes has also led to the inclusion of motorcycle awareness as a topic in driver education and licensing. Similarly, the patterns of crash type by class of machine provide an opportunity to target those specific segments of the rider population. The overrepresentation of supersport models in crashes is consistent with that recently reported in relation to fatal crashes in the United States for the same year (24); however, annual fatality rates by class were not computed for this study since the numbers were too small.

Unlicensed riders were substantially overrepresented in fatal crashes (26%) despite being only 7% of all riders in crashes. More than half of the unlicensed riders were aged between 17 and 25 years and constituted a substantial proportion of crash-involved riders who engaged in high-risk activities: illegal alcohol, speeding, and not wearing or incorrectly wearing a helmet. Further work is necessary to identify the factors associated with unlicensed riding in order to determine ways of enfranchising these riders into the lawful riding community or to more effectively exclude them from the roads.

LIMITATIONS This analysis is based on crashes reported to police; however, because of the underreporting of motorcycle crashes, the analysis may only account for approximately 60% of serious crashes (25). Crash rates based on 10,000 registered vehicles can be confounded by a range of factors such as vehicle kilometers traveled and urban, regional, and rural riding environments. This information was not available, and it is not possible to determine how the results may differ with their inclusion; however, the authors are working on a project to establish kilometers traveled from odometer readings recorded at annual registration so that this measure may be applicable in the future.

CONCLUSIONS AND IMPLICATIONS Although PTW crash rates showed an encouraging decline, more needs to be done to make motorcycling safer for the increasing proportion of people who choose this type of transport. Well-known demographic and behavioral risk factors were confirmed, including youth and inexperience, motorcycle type, unlicensed riding, excessive speed, and alcohol. The analysis also identified risk factors associated with the road environment, including relatively low-speed urban areas, T-junctions, and road surface hazards on curves.

Specific crash risk patterns were also identified, which could provide valuable information to be highlighted in rider and driver education programs. In MVCs, riders were most likely to be at fault in rear-end and head-on collisions on curves when the vehicle is not overtaking another one. The other driver was most likely to be at fault in intersection crashes, lane changes, and entering traffic from parking or driveways.

These results indicate a need for further work to be undertaken to improve the safety of the road environment and both rider and driver behavior. Auditing programs to address road hazards should target

de Rome and Senserrick

high-risk areas, particularly curves on high-frequency motorcycle routes. Geomapping techniques could be applied to the crash data to identify such high-risk locations. The high involvement of unlicensed riders, including their nonuse of helmets, speeding, and alcohol involvement, indicates a need to target these high-risk riders outside of the licensing system. Effective programs for other drivers to be aware and avoid PTW crashes are also needed.

ACKNOWLEDGMENTS This paper is drawn from the annual report prepared for the Motorcycle Council of New South Wales (www.roadsafety.mccofnsw. org.au) to help riders understand and manage their risks. The paper includes some previously posted results as well as new results and data. The authors thank the Motorcycle Council of New South Wales and the Roads and Traffic Authority for making these data available.

The authors also thank the National Roads and Motorists’ Association of New South Wales Motoring and Services Limited, which cofunds the website, and Tom Brandon, who assisted in analyzing the data. The authors receive funding from a road safety postgraduate scholarship, National Roads and Motorists’ Association ACT Road Safety Trust (de Rome), and a career development award from the National Health and Medical Research Council of Australia (Senserrick).

REFERENCES 1. Jamson, S., and K. Chorlton. The Changing Nature of Motorcycling: Patterns of Use and Rider Characteristics. Transportation Research, Vol. 12F, No. 4, 2009, pp. 335–346.

2. World Report on Road Traffic Injury Prevention (M. Peden et al., eds.), World Health Organization, Geneva, 2004.

3. Johnston, P., C. Brooks, and H. Savage. Fatal and Serious Road Crashes Involving Motorcyclists. Monograph 20. Department of Infrastructure, Canberra, Australia, 2008. http://www.infrastructure.gov.au/roads/safety/ publications/publication_keyword_result.aspx?

4. Lin, M. R., and J. F. Kraus. Methodological Issues in Motorcycle Injury Epidemiology. Accident Analysis and Prevention, Vol. 40, No. 5, 2008, pp. 1653–1660.

5. Road Traffic Crashes in New South Wales: Statistical Statement for the Year Ended December 2008. Roads and Traffic Authority, Sydney, Australia, 2009.

6. CrashLink Reporting System Data Manual. Roads and Traffic Authority, Sydney, Australia, 2007.

7. Motorcycle Rider Age and Risk of Fatal Injury. Motorcycle Safety Monograph 12. Australian Transport Safety Bureau, Canberra, Australia, 2002.

8. Motorcycle Rider’s Handbook. Roads and Traffic Authority, Sydney, Australia, 2009.

9. Global Status Report on Road Safety: Time for Action. World Health Organization, Geneva, 2009.

61

10. Jacobsen, P. L. Safety in Numbers: More Walkers and Bicyclists, Safer Walking and Bicycling. Injury Prevention, Vol. 9, No. 3, 2003, pp. 205–209.

11. Magazzu, D., M. Comelli, and A. Marinoni. Are Car Drivers Holding a Motorcycle Licence Less Responsible for Motorcycle-Car Crash Occurrence? A Non-parametric Approach. Accident Analysis and Prevention, Vol. 38, No. 2, 2006, pp. 365–370.

12. Hurt, H. H., J. V. Ouellet, and D. R. Thom. Motorcycle Accident Cause Factors and Identification of Countermeasures, Vol. 1: Technical Report. NHTSA, U.S. Department of Transportation, 1981.

13. de Rome, L., and R. Ivers. The Novice Rider Study: Final Report.

George Institute for Global Health, Sydney, Australia, 2009.

14. Motorcycles. In Traffic Safety Facts: 2008 Data, NHTSA, U.S. Department of Transportation, 2008.

15. Homel, R. Drink-Driving Law Enforcement and the Legal Blood Alcohol Limit in New South Wales. Accident Analysis and Prevention, Vol. 26, No. 2, 1994, pp. 147–155.

16. Road Risk for Sober, Licensed Motorcyclists. Monograph 27. Federal Office of Road Safety, Canberra, Australia, 1999.

17. Geedipally, S. R., and D. Lord. Investigating the Effect of Modeling Single-Vehicle and Multi-Vehicle Crashes Separately on Confidence Intervals of Poisson-Gamma Models. Accident Analysis and Prevention, Vol. 42, No. 4, 2010, pp. 1273–1282.

18. Savolainen, P., and F. Mannering. Probabilistic Models of Motorcyclists’ Injury Severities in Single- and Multi-Vehicle Crashes. Accident Analysis and Prevention, Vol. 39, No. 5, 2007, pp. 955–963.

19. Ivan, J. N., R. K. Pasupathy, and P. J. Ossenbruggen. Differences in Causality Factors for Single and Multi-Vehicle Crashes on TwoLane Roads. Accident Analysis and Prevention, Vol. 31, No. 6, 1999, pp. 695–704.

20. Road Traffic Crashes in New South Wales: Statistical Statement for the Year Ended December 2007. Roads and Traffic Authority, Sydney, Australia, 2008.

21. Andrea, D. Development of a Strategic Motorcycle Safety Program in Victoria, Australia. Presented at The Human Element: The 2006 International Motorcycle Safety Conference, 2006, Long Beach, Calif.

22. Pai, C. W. Motorcyclist Injury Severity in Angle Crashes at T-Junctions: Identifying Significant Factors and Analysing What Made Motorists Fail to Yield to Motorcycles. Safety Science, Vol. 47, No. 8, 2009, pp. 1097–1106.

23. Harnen, S., R. S. Radin Umar, S. V. Wong, and W. I. Wan Hashim. Predictive Model for Motorcycle Accidents at Three-Legged Priority Junctions. Traffic Injury Prevention, Vol. 4, No. 4, 2003, pp. 363–369.

24. Teoh, E. R., and M. Campbell. Role of Motorcycle Type in Fatal Motorcycle Crashes. Insurance Institute for Highway Safety, Arlington, Va., 2010. http://www.iihs.org/research/topics/pdf/r1131.pdf.

Accessed Oct. 2, 2010.

25. Richardson, D. B., and C. Paini. Amalgamation of Police and Hospital Trauma Data in the Australian Capital Territory 2002–2003. Presented at Road Safety Research, Education and Policing Conference, Southport, Australia, 2006. www.rsconference.com.

This paper does not necessarily reflect the views of either the Motorcycle Council of New South Wales or the Roads and Traffic Authority.

The Motorcycles and Mopeds Committee peer-reviewed this paper.