Suffolk Motorcycle Study
Version 1.0
Tanya Fosdick
September 2013
CONTENTS
EXECUTIVE SUMMARY ............................................................3
INTRODUCTION ......................................................................7
RISK PROFILE .........................................................................10
COLLISION PROFILES ........................................................10
WHAT? ......................................................................................................................................................... 10
WHEN? ......................................................................................................................................................... 15
WHERE? ....................................................................................................................................................... 19
HOW? ........................................................................................................................................................... 24
MOTORCYCLE RIDER PROFILES......................................................................................................................... 27
MOSAIC ANALYSIS ............................................................................................................................................ 33
INDEX OF MULTIPLE DEPRIVATION (IMD) ........................................................................................................ 39
PERSONAS ........................................................................................................................................................ 41
ENGAGEMENT PLAN......................................................................................................................................... 43
TRAINING COURSES ..................................................................................................................................... 43
ENGAGEMENT DAYS .................................................................................................................................... 43
COMMUTER RIDER SUPPORT ...................................................................................................................... 44
OTHER MOTORISTS ...................................................................................................................................... 44
ENFORCEMENT ............................................................................................................................................ 44
BIKER MAGAZINE ......................................................................................................................................... 44
WEBSITE & APPS .......................................................................................................................................... 45
BRAND BLINDNESS ....................................................................................................................................... 45
MESSAGES .................................................................................................................................................... 45
CURRENT LOCAL SCHEMES .............................................................................................................................. 47
Suffolk Ride Brand........................................................................................................................................ 47
Suffolk Ride Motorcycle Show, Felixstowe, May 2009 ................................................................................ 47
Young Rider Scheme .................................................................................................................................... 47
Rider Plus Scheme ........................................................................................................................................ 47
Moped Days ................................................................................................................................................. 47
Wheels To Work Scheme ............................................................................................................................. 48
BikeSafe ....................................................................................................................................................... 48
Fire Bike Project ........................................................................................................................................... 48
SUMMARY OF OTHER EVIDENCE AND SUCCESSFUL SCHEMES ........................................................................ 48
Summary Of Other Evidence........................................................................................................................ 48
Existing Schemes .......................................................................................................................................... 52
Appendix A – TRL Segment Profiles ...................................................................................................................... 55
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EXECUTIVE SUMMARY
This Insight Study has been created following a request from Suffolk County Council to understand
more about the crashes involving motorcyclists on their roads. As well as looking at the many factors
that make up a collision, this report also looks at what kinds of intervention are available and which
are most likely to suit the local rider demographic.
Motorcyclists are at an elevated risk on the roads as demonstrated by the statistic that despite only
making up 0.9% of the traffic, they represented 21% of KSI casualties on Britain’s roads in 2012. There
is some good news, however, as casualty rates have decreased over the last decade at a national level.
Risk varies according to the type of motorbike ridden and this study continually highlights the
significant differences between those riding motorcycles with different engine sizes. Based on
national ownership levels, mopeds are three times more likely than cars to be involved in an injury
collision, with motorbikes as a single group at double the risk.
These national trends and statistics are largely replicated on Suffolk’s roads with a small reduction in
collisions over the last five years and similar vehicle ownership risk levels. There are distinct
differences between the four motorcycle classes, based on engine size, with only 10% of casualties
being on bikes with ‘medium’ capacities (126-500cc). Smaller bikes (125cc or lower) account for more
than half of the casualties (53%) with the remainder on ‘Big’ bikes of over 500cc. The split between
‘big’ and ‘small’ bikes is examined in detail throughout this study and it is essential in order to
appropriately tackle the different patterns of use. Much of the recent, albeit small reduction in
motorcyclist casualties, has been seen in the big bike group with very little change in casualty rates
for small bikes.
The analysis of when crashes take place is very interesting with small bikes following a distinct
‘commuter pattern’ of crashes on weekdays during peak traffic hours. Large bikes on the other hand
only show significant weekday spikes later in the day, as well as a late-morning to mid-afternoon rise
at weekends. It is worth noting that ‘only’ 38% of crashes that involve big bikes happen at the
weekend so it cannot be said that most bikes are therefore only used for weekend leisure purposes.
Large bikes do however exhibit a significant seasonal variance, with few crashes in the winter months
of December to March. Spring and summer are the times when crashes are most likely to occur with
rates tailing off in autumn. There is much less of a seasonal variance with small bikes and any
differences in monthly results are less pronounced. An interesting spike is seen in September,
however, which could be associated with the start of the new educational year.
It has long been held that there is a link between motorcycle use and weather conditions. Analysis of
historical local weather was undertaken to see if there were any correlations between annual peaks
and troughs in collision involvement and rainfall and temperature. This demonstrated a slight
correlation between rainfall events and collision rates with an inverse relationship present.
Temperature was a less significant factor with only a weak correlation seen in the analysis.
Road characteristics are also different for crashes involving the two referenced size classes. Over
three-quarters of crashes involving smaller bikes happen on 30mph limit roads, with a more even split
between 30 mph and 60mph roads for larger bikes. It is also worth noting that riders of large bikes
are more likely to be injured when not at a junction with almost half of all crashes occurring when
proceeding along the carriageway. Almost two-thirds of small bike crashes are at some form of
Page | 3
junction with T-junctions being the most common. Motorcyclists are more likely to be the ‘victims’ of
a collision at junctions with the theory of inattentional blindness used to explain why drivers of other
vehicles are less likely to respond to their presence at a junction.
Mapping where crashes are more likely to take place is carried out by looking at crash density per
length of road. Unsurprisingly urban areas have higher collision rates for small bikes with a more
scattered distribution amongst large bikes.
As well as looking at junction detail it is also possible to analyse the manoeuvres being undertaken by
the motorcycle associated with the casualty at the time of the crash. This is a complex analysis with
most crashes taking place when the bike was travelling ahead, either straight or on a bend. Almost a
quarter of large bike crashes occur on bends and these are often attributed to rider error. Overtaking
(offside) is also a common manoeuvre when large bikes are involved. Almost half of smaller bikes are
crash-involved when travelling straight ahead, which may indicate they were victims of right of way
violations.
Analysis of Contributory Factors (CFs) is often difficult due to reporting bias, subjective analysis and
lack of time to carry out a thorough investigation. In this study, only collisions attended by an officer
are used and CFs analysed at collision, not vehicle, level have been considered. The analysis for both
size classes shows that ‘Failed to look properly’ comes out on top but with no attribution of blame it
is hard to say whether this is the fault of the motorcyclist or driver of the other vehicle. The assigned
contributory factors for small bikes tend to reflect inexperience, slippery road surfaces, sudden
braking, and right of way violations. Larger bikes are often involved due to loss of control, speed, and
bends.
Analysis of those involved in collisions is deliberately restricted to the residents of Suffolk, not only
because they make up the vast majority (79%) of motorcyclists who crash in Suffolk, but also because
they are easier to target and most likely to respond in any local intervention. In terms of age there is
a massive spike in casualties on small bikes who are under the age of 20 with very few over the age of
25. Riders of large bikes are spread more evenly with riders in the age range 20 – 54 appearing at
similar levels in the collision statistics. There is not a large spike in the 40’s age group which was
previously evidenced in casualty statistics giving rise to the term ‘born-again-biker’. A generation has
passed since this demographic analysis and the picture for riders of big bikes is more complex. One
stereotype of big bikes is true though, with 98% of riders being male, compared to 85% for small bikes.
Comparing the relative safety of local riders with those from elsewhere can be carried out using two
different methodologies: one comparing rates based on vehicle ownership; and a second using
population levels. When looking at crashes per bike owned in Suffolk there is a 25% reduction in the
rate compared to the national average. This rate is similar to the average for neighbouring counties
although there is some variation between Norfolk, Cambridgeshire and Essex. The analysis based on
population however appear to show elevated risk compared to the national average and Suffolk has
the highest risk for any of the Eastern counties. This difference in the two risk measures could possibly
be explained by variation in bike ownership rates per head of population but the only true way to
establish absolute risk rates would be to measure crashes per kilometre driven by local residents; a
figure that is not available.
Page | 4
Riders of small bikes are very likely to crash in Suffolk with only 7% being injured on their bikes
elsewhere. The number is higher for large bikes with around a quarter of Suffolk’s big-bike riders
being injured outside the county. The home location of motorcycle riders shows some variance
against the crash location analysis, especially for large bikes with owners slightly more likely to come
from rural areas.
Socio-demographic profiling of casualties and drivers and riders of vehicles is becoming much more
common thanks to MAST Online, which allows quick and easy analysis of the types of people who are
involved in collisions using the Experian Mosaic classification system. The analysis methodology
focuses on those most at risk relative to the population and distinct peaks can be seen in several
community types. Mosaic Group I is present in both size classes of bike with Groups K and O present
for smaller bikes and groups E and F for larger bikes. This suggests that there are several different
types of motorcyclist from Suffolk who are involved in collisions and that a one-size-fits-all approach
is not appropriate and different types of intervention will be required to target different types of rider.
Deprivation levels are relatively low for all motorcyclists although those on larger bikes tend to be
even better-off.
This analysis combined with the other information about the people involved from the STATS19
analysis allows for the creation of four ‘personas’ which are used to typify those involved in crashes
by size of bike:
Jack – A teenage student who rides a moped to travel to college and tends to be involved in collisions
due to inexperience and other road users not seeing him. He lives at home in a family with a relatively
low income. Lack of money is the main factor in him being a motorcyclist.
Dave – A low-paid worker in his 20’s who can’t afford a car and uses public transport or motorbike to
get around. Having a lot in common with Jack, Dave uses a bike because he doesn’t have access to a
car or simply doesn’t want to use one. He is not likely to spend a lot of income on expensive protective
gear.
James – A car-owner who uses the bike for fun at the weekends, often on his own. He has a family
and a good income and can afford a large bike and matching leathers.
Paul – A leisure rider like James with access to other vehicles for commuting purposes but more
experience of riding. In his 50’s he is less likely to own a sports bike but still enjoys going fast.
Developing an all-encompassing engagement plan to cover these distinct personas is not likely to be
possible but a shared brand with individual campaigns may work well. It is important to consider the
key messages for each group as well as the most suitable engagement platform. Getting the
motorcyclists attention is tough; outdoor events can work well and would appeal to those less likely
to actively seek information but such events are expensive. Websites are a common feature of road
safety campaigns but are less likely to work with some demographics, typically those associated with
small bike crashes. Whatever delivery method is chosen it needs to offer an attractive message to
motorcyclists at risk and not just those who are already well-trained and safer on the road.
More training is a common theme seen in other campaigns as well as current local initiatives.
Understanding which types of motorcyclist are more likely to participate in training is essential as it is
often the harder to reach bikers who need the most help. Selling training as skill-raising rather than
Page | 5
safety-awareness, and marketing it through appropriate channels such as colleges or workplaces is a
great way to get to those who may not usually volunteer for these schemes.
Although this Insight Study does not provide all of the answers it goes a long way to helping Suffolk
County Council and its road safety stakeholders in better understanding the risks associated with
motorcycling in the county, and the risk to the motorcyclists from the area. Any future interventions
would be advised to take note of the analysis and use an evidence-led approach to the design and
delivery of the scheme.
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INTRODUCTION
Motorcycle riders and pillion passengers are over-represented in the casualty statistics globally. In the
EU, more than 6,500 two wheeled motor vehicle users die each year.1 In 2012, in Great Britain, 328
motorcycle users were killed, 5,000 were seriously injured and 19,310 were slightly injured. 2
Motorcycle users represented 21% of Great Britain’s killed or seriously injured casualties in 2012 yet
only accounted for 0.9% of vehicle miles travelled.3 Since 1994, the number of licensed motorcycles
has increased by 70%4 but still only account for 3.5% of total licensed vehicles.5 Motorcycle users are
therefore particularly vulnerable on the roads.
There have been recent reductions in motorcycle casualty rates, as shown in the chart below. The
columns show the number of motorcycle user casualties by severity since 2000 and the line shows the
number of fatal motorcycle user casualties indexed to the 2005-09 average. The bars show that the
number of motorcycle user casualties has fallen from 28,212 in 2000 to 19,310 in 2012 and the line
shows that, compared to the 2005-09 average, every year since 2007 has had an index lower than 100.
35000
140
30000
120
25000
100
20000
80
15000
60
10000
40
5000
20
0
Fatal Index
Number of Motorcycle User Casualties
FIGURE 1 - GB MOTORCYCLE USER CASUALTIES BY SEVERITY AND INDEXED AGAINST 2005-09 AVERAGE
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Fatal
Serious
Slight
Fatal Index
Despite the recent reductions in motorcycle user casualty rates, this group, representing a fifth of all
KSI casualties, still present a road safety challenge. Large engine motorcycles have often been the
focus of analysis and intervention design as these machines have had a high severity ratio which needs
to be targeted. Moped and scooter riders (on motorcycles up to 50cc) are also at significant risk,
however. Figure 2 shows the relative collision risk against vehicle ownership. On average, between
2004 and 2011, 9.69 out of every thousand vehicles were involved in injury collisions. This is used as
a base for comparison in the chart to create 100-based indices. The analysis found that cars have a
slightly lower rate of 8.86 in every thousand cars involved in an injury collision, producing an index of
91. Motorcycles, however, are twice as likely to be involved in an injury collision, given the levels of
ownership. Moped riders are at even greater risk – 29.28 in every thousand mopeds are involved in
Page | 7
an injury collision, which is three times the risk of all vehicles.6 Mileage figures indicate the risks could
be even higher for mopeds as motorcycles of any engine size are seven times as likely to be involved
in an injury collision, given the annual mileage ridden 7 . Whilst mileage data for mopeds are not
available, the risk per mile ridden is likely to be higher than for all motorcycles after accounting for
the increased risk from low ownership and also that mopeds tend to be used on local urban roads for
short distances, rather than long leisure rides like larger engine bikes. The analysis will explore the
relative risk, by engine size, of motorcyclists within Suffolk to explore the circumstances locally.
FIGURE 2 - INDICES OF GB COLLISION INVOLVEMENT RATES PER THOUSAND LICENSED VEHICLES
Index of Risk by Vehicle Ownership
350
300
250
200
150
100
Cars
MC
Mopeds
50
0
Index of Risk by Vehicle Ownership
This report sets out analysis undertaken using STATS19 collision data collected by Suffolk Police for
2008 to 2012; and data from MAST, an online analysis tool which combines casualty and collision data
from the Department for Transport with socio-demographic insights created by Experian through
Mosaic Public Sector. The postcodes of drivers and casualties involved in collisions are used to
determine which Mosaic Groups and Types these individuals are likely to belong to and this can be
used by road safety professionals to understand who needs to be targeted in road safety
interventions. The report looks at motorcyclists involved in collisions in Suffolk and also focuses on
motorcyclists who live in Suffolk who have been involved in injury collisions. The intention of this
report is to provide the road safety practitioner in Suffolk with a full understanding of the types of
collision involving motorcyclists and to equip them with the tools to target the issue.
The report works through the analysis by first determining the extent to which motorcyclists are
involved in collisions in Suffolk and in what context they are involved. There were 21,432 licensed
motorcycles in Suffolk in 2012, representing 4.6% of licensed vehicles8 but motorcycles represent 18%
of the vehicles involved in KSI collisions in Suffolk. The residency of the riders will be examined to
determine if the motorcyclists involved in collisions on Suffolk’s roads are local to the area.
Other factors, such as when, where and how the motorcyclists were involved in collisions are explored
to provide information on the topics and issues that could be focused upon within an intervention.
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A large part of the analysis focuses on profiling the motorcyclist, with the aim of producing ‘personas’
that can be used to visualise the target audience. These personas are created using a variety of sociodemographic data, including looking at Indices of Multiple Deprivation, rurality and Mosaic Groups.
Profiling in this way allows the practitioner to understand how motorcyclists will respond to a road
safety intervention and in what way it should be delivered.
All of this culminates in an ‘Engagement Plan’, where experts from Road Safety Analysis have used all
the available information from the analysis, external research, and learning outcomes from other
motorcycle schemes, to create an intervention design.
Page | 9
RISK PROFILE
This profile covers two distinct areas: information about the crash and information about the person
involved. Both are relevant to the analysis and are considered separately.
The collision analysis focuses on motorcycle riders involved in injury collisions in Suffolk between 2008
and 2012. Riders involved in collisions tend to be injured – on average, 85% of the casualties injured
by a motorcycle in Suffolk were the riders themselves (3% were pillion passengers, 2% were
pedestrians hit by motorcyclists and 8% were drivers of other vehicles, including cyclists) and
therefore the focus of the report is on the riders (and not other casualties). This should ensure that
the findings can be used to direct interventions to the group requiring it the most i.e. those in control
of the vehicle.
Looking at residency, 79% of the riders involved in injury collisions in Suffolk live in Suffolk.
COLLISION PROFILES
WHAT?
Motorcycle riders are over-represented in injury collisions in Suffolk. As cited in the Introduction, there
were 21,432 licensed motorcycles in Suffolk in 2012, representing 4.6% of licensed vehicles 9 but
motorcycles represent 18% of the vehicles involved in KSI collisions in Suffolk.
FIGURE 3 - INDICES OF SUFFOLK COLLISION INVOLVEMENT RATES PER THOUSAND LICENSED VEHICLES
Index of Risk by Vehicle Ownership
350
300
250
200
150
100
50
0
Cars
Motor cycles
Light goods
Heavy goods
Buses and
coaches
Other vehicles
Index of Risk by Vehicle Ownership
One-hundred-based indices, using vehicle ownership in Suffolk in 2012 and Suffolk residents involved
in collisions anywhere in the country between 2007 and 2011, were created (Figure 3) to show the
relative risk of injury collision involvement. The indices show that cars are involved in injury collisions
at rates expected, given the number of licensed cars in Suffolk. Light goods vehicles and other vehicles
Page | 10
(rear diggers, lift trucks, rollers, ambulances, Hackney Carriages, three wheelers and agricultural
vehicles) at lower rates than expected whilst motorcycles, heavy goods vehicles and buses and
coaches are over-represented. The bus and coach index is likely to reflect the low number of injury
collisions involving this type of vehicle and the low number of these vehicles registered in Suffolk.
The number of collisions in Suffolk which involve a motorcycle are shown in Figure 4 below. There
were 98 killed or seriously injured (KSI) collisions in 2008 and this reduced to 83 in 2012. The number
of collisions involving a motorcycle and resulting in slight injury reduced from 205 in 2008 to 179 in
2012. There has been a general downward trend in motorcycle collisions in Suffolk since 2008, with
2010 experiencing especially low numbers of incidents.
FIGURE 4 - NUMBER OF COLLISIONS INVOLVING MOTORCYCLES IN SUFFOLK BY SEVERITY
350
300
250
200
150
100
50
0
2008
2009
2010
Fatal
Serious
2011
2012
Slight
Motorcyclists are not a homogeneous road user type – engine size, machine type and journey purpose
all attract very different types of rider. In order to gain an insight into the types of rider involved in
injury collisions in Suffolk, engine size was examined for motorcyclists involved in collisions between
2008 and 2012.
TABLE 1 - MOTORCYCLISTS INVOLVED IN COLLISIONS IN SUFFOLK 2008-2012, BY SEVERITY
Page | 11
Engine Size
Fatal
Serious
KSI
Slight
Total
MC up to 50cc
MC 51-125cc
MC 126-500cc
MC Over 500cc
Total
1
4
11
27
43
65
113
49
208
435
66
117
60
235
478
351
284
89
286
1010
381
401
149
521
1452
KSI
Ratio
17%
29%
40%
45%
33%
TABLE 2 - PERCENTAGES OF MOTORCYCLISTS BY ENGINE SIZE INVOLVED IN EACH SEVERITY OF COLLISION IN SUFFOLK, 2008-2012
Engine Size
MC up to 50cc
MC 51-125cc
MC 126-500cc
MC Over 500cc
Total
Fatal
2%
9%
26%
63%
100%
Serious
15%
26%
11%
48%
100%
KSI
14%
24%
13%
49%
100%
Slight
35%
28%
9%
28%
100%
Total
26%
28%
10%
36%
100%
Tables 1 and 2 show the number and percentages of riders of different sized engines who were
involved in collisions in Suffolk by severity and that those with larger engine machines have higher KSI
ratios. The analysis shows that 63% of the riders involved in fatal collisions and 48% of those involved
in serious collisions were on machines with engines over 500cc. It also shows that there are higher
numbers of riders on smaller engine machines who tend to be involved in slight collisions; 54% of the
riders involved in Suffolk motorcycle collisions were on machines with an engine size of 125cc or lower.
Examining the classes of casualties injured in collisions in Suffolk shows a high percentage of the riders
themselves being injured. Table 3 shows casualty class against severity and the type of related vehicle:
‘Driver’ shows the percentage of injured motorcyclists; ‘Passenger’ shows the percentage of injured
motorcycle pillion passengers; and ‘Pedestrian’ shows the percentage of pedestrians injured by a
motorcycle hitting them. All types of vehicle have been shown as a comparison. It shows much higher
percentages of motorcyclists being injured than drivers of all vehicles and as such, suggests that the
analysis should focus on the riders and not their injured passengers or pedestrians.
TABLE 3 - CASUALTIES INJURED IN COLLISIONS IN SUFFOLK BY CASUALTY CLASS AND RELATED VEHICLE
Driver
Passenger
Pedestrian
All Vehicles
MC up to 125cc as related vehicle
MC over 500cc as related vehicle
All Vehicles
MC up to 125cc as related vehicle
MC over 500cc as related vehicle
All Vehicles
MC up to 125cc as related vehicle
MC over 500cc as related vehicle
Fatal
69%
100%
95%
13%
0%
0%
18%
0%
5%
Serious
70%
96%
97%
15%
1%
3%
15%
4%
1%
KSI
70%
96%
97%
15%
1%
2%
15%
3%
1%
Slight
70%
96%
87%
23%
1%
12%
7%
2%
1%
Total
70%
96%
91%
22%
1%
8%
8%
3%
1%
Based on the numbers of riders involved in collisions, the analysis will focus on two motorcycle groups:
those riding motorcycles with engines sizes of up to 125cc and those with engine sizes of over 500cc.
The number of motorcyclists, by engine size, involved in collisions in Suffolk since 2004 have been
analysed, using STATS19 and MAST data. Figures 5 and 6 show the number of riders, by severity, who
were involved in collisions in each year. The line shows the KSI index for each year against the 200509 average. Changes to the STATS19 form in 2005 mean that data is not available for motorcycles over
500cc in 2004.
Page | 12
Figure 5 shows that since 2007 there has been little divergence from the 2005-09 KSI average for the
small motorcycles and that the overall number of motorcyclists on machines of this size has not
changed significantly after a reduction in 2007. There was a reduction in the number of riders of 125cc
or under machines involved in slight collisions in 2010 and a reduction in those involved in KSI collisions
in 2006.
FIGURE 5 - SUFFOLK 125CC OR UNDER MOTORCYCLISTS BY SEVERITY AND INDEXED AGAINST 2005-09 KSI AVERAGE
160
140
200
120
100
150
80
100
KSI Index
Number of 125cc or Under Motorcyclists
250
60
40
50
20
0
0
2004
2005
2006
Fatal
2007
2008
Serious
2009
Slight
2010
2011
2012
KSI Index
Figure 6, analysing motorcyclists on machines with engines over 500cc, shows the high severity ratio.
The numbers of riders involved in injury collisions were at their lowest in 2006 and 2012 and the
number of riders involved has reduced every year since a peak in 2009. Both 2011 and 2012 saw the
number of riders of large motorcyclists below the 2005-09 average.
Page | 13
140
140
120
120
100
100
80
80
60
60
40
40
20
20
0
KSI Index
Number of Over 500cc Motorcyclists
FIGURE 6 - SUFFOLK OVER 500CC MOTORCYCLISTS BY SEVERITY AND INDEXED AGAINST 2005-09 KSI AVERAGE
0
2005
2006
2007
Fatal
2008
Serious
2009
2010
Slight
2011
2012
KSI Index
The number of vehicles involved in motorcycle collisions in Suffolk were analysed to see if the incidents
were single vehicle collisions (SVC) only involving a motorcycle or where there was at least one other
vehicle. Surprisingly, one-quarter of collisions involving motorcycles up to 125cc were single vehicle;
for motorcycles over 500cc, 28% were single vehicle collisions. The percentages of KSI collisions
involving single vehicles were higher: 33% of KSI collisions involving motorcycles up to 125cc and 31%
of KSI collisions involving motorcycles over 500cc.
FIGURE 7 - NUMBER OF VEHICLES INVOLVED IN MOTORCYCLE COLLISIONS IN SUFFOLK
Up to 125cc
Over 500cc
0%
5%
8%
25%
64%
70%
1
Page | 14
28%
2
3 or 4
1
2
3 or 4
5+
Where conflict with other vehicles occurred, 85% of the other vehicles in collisions involving
motorcycles up to 125cc and 80% of collisions involving motorcycles over 500cc were cars. Light goods
vehicles also featured: lights goods vehicles represented 6% of the vehicles in small motorcycle
collisions and 8% of those involving large motorcycles.
WHEN?
This section of the analysis looks at when riders were involved in collisions in Suffolk between 2008
and 2012 and is separated into the two engine groups.
For larger motorcyclists (Figure 8), there are commuter time peaks on weekdays, especially in the
afternoon. At weekends, motorcyclists are most likely to be involved in collisions between 10am and
5pm.
FIGURE 8 - OVER 500CC MOTORCYCLISTS INVOLVED IN COLLISIONS IN SUFFOLK BY HOUR
60
50
40
30
20
10
Weekday
11PM
10PM
9PM
8PM
7PM
6PM
5PM
4PM
3PM
2PM
1PM
NOON
11AM
10AM
9AM
8AM
7AM
6AM
5AM
4AM
3AM
2AM
1AM
Midnight
0
Weekend
The smaller engine motorcycles are shown in Figure 9 and shows there is a much higher morning peak
on weekdays with a less of a daytime peak at weekends. There is a much clearer commuter pattern
with the smaller motorcycles.
Page | 15
FIGURE 9 - UP TO 125CC MOTORCYCLISTS INVOLVED IN COLLISIONS IN SUFFOLK BY HOUR
90
80
70
60
50
40
30
20
10
Weekday
11PM
10PM
9PM
8PM
7PM
6PM
5PM
4PM
3PM
2PM
1PM
NOON
11AM
10AM
9AM
8AM
7AM
6AM
5AM
4AM
3AM
2AM
1AM
Midnight
0
Weekend
The day of the week on which the two types of rider were involved in collisions differed (Figure 10).
As with the times of day analysis, riders of motorcycles with engines of 125cc or less are involved in
collisions during the week, reflecting the use of these machines for commuting. Conversely,
motorcyclists with engines of over 500cc have a higher number of collisions at weekends, with 11%
riders being involved in KSI collisions on Mondays, Wednesdays and Thursdays and 19% on Saturdays
and Sundays.
FIGURE 10 - DAY OF WEEK OF MOTORCYCLISTS INVOLVED IN COLLISIONS IN SUFFOLK 2008-2012
160
140
120
100
80
60
40
20
0
Mon
Tue
Wed
Up to 125cc
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Thu
Over 500cc
Fri
Sat
Sun
The month of the year in which the motorcyclists were involved in collisions was analysed (Figures 11
and 12). For motorcyclists riding machines with engines over 500cc, there is a clear peak in collision
involvement in the summer months. The peak starts in April and continues until August and there is a
clear increase in involvement in KSI collisions in these months. There are divergences from this general
trend: there were 6 riders involved in fatal collisions in the month of October and four of these
occurred in October 2011.
FIGURE 11 - MONTH OF YEAR OF MOTORCYCLISTS OF OVER 500CC INVOLVED IN COLLISIONS
70
60
50
40
30
20
10
0
Jan
Feb
Mar
Apr
May
Fatal
Jun
Jul
Serious
Aug
Sep
Oct
Nov
Dec
Oct
Nov
Dec
Slight
FIGURE 12 - MONTH OF YEAR OF MOTORCYCLISTS OF 125CC AND UNDER INVOLVED IN COLLISIONS
100
90
80
70
60
50
40
30
20
10
0
Jan
Feb
Mar
Apr
May
Fatal
Page | 17
Jun
Serious
Jul
Aug
Slight
Sep
The pattern for the smaller motorcycles is quite different: their involvement in KSI collisions is fairly
evenly spread across the year with a peak in September. The number of riders involved in slight
collisions increases slowly throughout the year to a peak in September. Given the attraction of mopeds
and scooters to young people, the September peak could coincide with starting college or sixth form
The weather conditions at the time the motorcyclists were involved in the collisions were examined
(Tables 4 and 5). For the larger motorcycles, 90% of the riders were involved in collisions in fine and
still weather, suggesting there is a choice when they ride. For those on smaller motorcycles, 77% were
involved in collisions in fine and still weather.
TABLE 4 - WEATHER CONDITIONS OF MOTORCYCLISTS ON OVER 500CC MACHINES INVOLVED IN INJURY COLLISIONS
Weather
Conditions
Fine & Windy
Fog or Mist
Other
Wet & Still
Wet & Windy
Fine & Still
Not Known
Fatal
Serious
KSI
Slight
Total
0
0
0
3
1
23
0
5
0
1
13
0
188
1
5
0
1
16
1
211
1
4
0
6
15
3
257
1
9
0
7
31
4
468
2
TABLE 5 - WEATHER CONDITIONS OF MOTORCYCLISTS ON 125CC OR UNDER MACHINES INVOLVED IN INJURY COLLISIONS
Weather
Conditions
Fine & Windy
Fog or Mist
Other
Wet & Still
Wet & Windy
Fine & Still
Not Known
Fatal
Serious
KSI
Slight
Total
0
0
0
0
0
5
0
3
1
4
16
2
152
0
3
1
4
16
2
157
0
18
5
18
105
2
446
5
21
6
22
121
4
603
5
Analysis of historical local weather was undertaken to see if there were any correlations between
annual peaks and troughs in collision involvement and rainfall and temperature. Monthly average
maximum and minimum temperatures and millimetres of rain were extracted for the Wattisham
weather station. 10 Deviations from monthly averages for rainfall, maximum and minimum
temperatures and collision involvement were determined. In 71% of months where there were higher
numbers of motorcyclists involved in collisions in Suffolk than the average, there was less rainfall than
average. Conversely, in 60% of the months where there were lower numbers of motorcyclists involved
in collisions than the average, there was higher than average rainfall. Temperatures seemed to have
less of a correlation with collision involvement rates: 59% of peaks coincided with higher than normal
maximum temperatures and 53% of troughs coincided with lower than average maximum
temperatures. With minimum temperatures, 35% of the months with higher than average collision
involvement had higher minimum temperatures than normal and 60% of the months with lower
collision involvement also had lower minimum temperatures than average. Whilst not conclusive, the
Page | 18
analysis does seem to indicate that collision involvement increases in good weather, which might be
a result of leisure riders making conscious decisions about which days to take the motorcycle out.
Most motorcycle riders involved in collisions in Suffolk had crashes in daylight: 76% of riders of
motorcycles up to 125cc and 85% of riders of motorcycles over 500cc. When riders were involved in
collisions in darkness, they tended to be on roads where streetlights were lit: for motorcyclists on the
smaller bikes, 19% were involved in collisions in darkness with light lit as were 8% of those on larger
bikes.
Lastly, looking at when Suffolk collisions involving motorcyclists occurred, journey purposes of riders
were examined. Table 6 shows the percentages of riders involved in injury collisions in Suffolk by
recorded journey purpose. Over three-quarters of riders involved in KSI collisions were recorded as
travelling for ‘other’ purpose in STATS19. Reasons could include shopping, leisure or that the recording
officer did not know the journey purpose. Around a quarter of motorcyclists of both engine sizes were
recorded as commuting or riding for work purposes.
TABLE 6 - JOURNEY PURPOSE OF MOTORCYCLISTS INVOLVED IN COLLISIONS IN SUFFOLK
KSI
Work
Commute
School Pupil
School Run
Other
Up to 125cc
10%
12%
2%
1%
75%
All
Over 500cc
5%
14%
0%
0%
80%
Up to 125cc
12%
14%
2%
0%
71%
Over 500cc
9%
15%
0%
0%
75%
WHERE?
The next section looks at where motorcyclists were involved in collisions in Suffolk. There were
differences in the classification of road the two groups of motorcyclists were involved in collisions on:
50% of those riding motorcycles with engines over 500cc were involved in collisions on A roads whilst
35% of those on motorcycles with engines of 125cc or below were on A roads and 30% were on
unclassified roads.
Figure 15 shows the speed limits on which the two groups of motorcyclists were involved in collisions.
It shows differences in location between the two types of rider: 77% of those on motorcycles with an
engine of 125cc or less were in 30mph limits compared to 38% of those on motorcycles with an engine
of over 500cc. Higher speed limits appear to result in higher severity collisions: 51% of riders of over
500cc motorcycles were involved in KSI collisions on 60mph roads compared to 35% of those involved
in slight collisions. A similar trend is evident for those riding smaller motorcycles: 23% of those
involved in KSI collisions were on 60mph roads compared to 10% of those involved in slight collisions.
Page | 19
FIGURE 13 - SPEED LIMITS WHERE MOTORCYCLISTS WERE INVOLVED IN COLLISIONS IN SUFFOLK
700
600
500
400
300
200
100
0
20
30
40
Up to 125cc
50
60
70
Over 500cc
Motorcycles in both engine size groups overwhelmingly had collisions on single carriageway roads:
this was the case for 83% of riders on motorcycles up to 125cc and 79% of those on motorcycles over
500cc. Four percent of those on smaller motorcycles and 8% of those on larger machines were on dual
carriageways.
Junction details were also analysed and are displayed in Figures 16 and 17. For the smaller machines,
one-third of the riders were involved in collisions away from junctions; this is lower than the 46% of
motorcyclists on larger machines who were not at junctions. There were few differences in the
percentage of riders involved in collisions at roundabouts but there were higher percentages of
motorcyclists on smaller bikes at T-junctions or crossroads than those on larger motorcycles. These
findings support research undertaken in 2004 for the Department for Transport which looked at
motorcycle collisions in detail. 11 The research found that 38% of the cases involved right of way
violations (ROWV) and that “less than 20% of these involve a motorcyclist who rates as either fully or
partly to blame for the accident. The majority of motorcycle ROWV accidents have been found to be
primarily the fault of other motorists.”12The research found that the majority of ROWVs occurred at
T-junctions, which were three times as common as at roundabouts or crossroads. The Motorcycle
Accident In-Depth Study (MAIDS) report explored the primary causes of 921 motorcycle collisions
across France, Germany, the Netherlands, Spain and Italy and found the results displayed in Table 7:
Page | 20
TABLE 7 - PRIME CAUSES OF COLLISIONS INVOLVING MOTORCYCLES FROM THE MAIDS REPORT13
Frequency
341
464
6
72
37
921
Human – Motorcycle rider
Human – Other Vehicle driver
Vehicle
Environmental
Other Failure
Total
Percent
37.1
50.4
0.7
7.7
4.1
100.0
FIGURE 14 - JUNCTION DETAILS OF MOTORCYCLISTS ON UP TO 125CC MACHINES
Roundabout
12%
Mini roundabout
1%
Slip
0%
Other
1%
Private
7%
No Junction
36%
Multi-Junction
1%
Crossroads
4%
T Junction
38%
FIGURE 15 - JUNCTION DETAILS OF MOTORCYCLISTS ON OVER 500CC MACHINES
Roundabout
12%
Mini roundabout
0%
Other
2%
Slip
1%
Private
8%
Multi-Junction
0%
No Junction
46%
Crossroads
1%
T Junction
30%
Page | 21
Previous research has suggested that there is a need to change the attitudes of drivers towards
motorcyclists. The Think! Named Rider campaign is based on several recent pieces of research which
to seek to explain why drivers look but do not see motorcyclists. Firstly, there is the theory of
inattentional or perceptual blindness, where a person fails to notice something that is in plain sight.
A related theory – that of cognitive conspicuity – shed further light. Conspicuity – or ‘mental visibility’
– “greatly increases if a stimulus is relevant to the observer (Green, 2003).” Could it be that
motorcyclists were simply not relevant or meaningful to the observer, or driver? Did they not care
enough?14
Focus groups found that motorcyclists and drivers were not meaningful to each other and in fact many
found the other group a nuisance. A further study found that the antipathy between drivers and
motorcyclists was based not just on road behaviour, but on the symbolism of motorcyclists’ distinctive
protective gear.
The highly functional uniform of the motorcyclist, with his leathers and crash helmet, closely resembles
and therefore signifies such nightmarish figures from myth, fiction and the world at large whose attire
is the way it is for nefarious functional reasons (e.g. bank robber), for ceremonial, symbolic purposes
(e.g. Ku Klux Klan member), or very specifically because it has been designed by the storyteller to cast
a sinister spell on the observer (e.g. Jason/Eminem)…. These cultural signs not only demonise the rider,
they also make it difficult for the driver to see the motorcyclist as human, and deserving of empathy.15
These explanations for inattentional blindness (namely a lack of cognitive conspicuity caused by an
absence of empathy for riders) led to the DfT building a campaign based on humanising riders and
making drivers see them as ordinary people with ordinary lives.
As well as the road and junction types, it is possible to examine the physical locations of motorcycle
collisions. Analysis of the top Middle Super Output Areas (MSOAs) in Suffolk where collisions involving
the two groups of motorcyclists occurred are shown in the maps below.
The first map (Figure 18) shows that there are concentrations of collisions involving motorcyclists on
machines up to 125cc in the urban areas of Lowestoft, Bury St. Edmunds, Mildenhall, Newmarket,
Haverhill, Sudbury, Stowmarket/Needham Market, Beccles and the Ipswich/Woodbridge/Felixstowe
areas north and south of the River Orwell. Collisions also occurred in the area around Bungay and
south of Brandon.
The second map (Figure 19) shows the locations of collisions involving riders of motorcycles with
engines over 500cc. There were concentrations of collisions in the urban areas of Lowestoft, Bury St.
Edmunds, Haverhill, Beccles and the Ipswich/Woodbridge/Martlesham Heath and Wherstead area.
There were also concentrations of collisions in the more rural areas of
Newmarket/Mildenhall/Brandon/Ixworth area; the Sudbury area; and also around the A140 from
Stowmarket across to Debenham/Framsden and from Wetheringsett down to Coddenham.
Page | 22
FIGURE 16 - MSOAS WHERE COLLISIONS INVOLVING MOTORCYCLES (UP TO 125CC) OCCURRED
FIGURE 17 - MSOAS WHERE COLLISIONS INVOLVING MOTORCYCLES (OVER 500CC) OCCURRED
Page | 23
HOW?
After looking at the when and where of the motorcyclists involved in collisions in Suffolk, the analysis
now explores how these collisions occurred.
The manoeuvres of the motorcyclists involved in collisions in Suffolk are shown in the following chart.
A quarter of the motorcyclists on the larger bikes were travelling ahead on a left or right hand bend
(compared to 16% of the smaller capacity motorcyclists). In the ‘In-depth Study of Motorcycle
Accidents’, 15% of the total cases examined involved loss of control on a bend, corner or curve.
This type of accident is almost always regarded as primarily the fault of the motorcyclist rather than
other road users, and it has already been shown that such accidents are more associated with riding
for pleasure than accidents of other types. Hurt et al. (1981) found that rider error in such cases
consisted of ‘slideout and fall due to overbraking, running wide of a curve due to excess [inappropriate
speed], or under-cornering’16
The research found that riders involved in this type of collision were nearly three times as likely to be
rated as ‘inexperienced’ riders by researchers. It was found that these riders had a full motorcycle
licence which they had only recently acquired or had recently returned to riding after a break (‘born
again’ bikers). Analysis of weather conditions found that these incidents were not more likely to occur
on damp, wet or icy roads although there was evidence of some riders hitting oil, gravel or mud on
rural bends.17
FIGURE 18 - MANOEUVRES OF MOTORCYCLISTS INVOLVED IN COLLISIONS IN SUFFOLK
60%
Up to 125cc
Over 500cc
50%
40%
30%
20%
10%
0%
Forty-eight percent of riders on smaller capacity machines and 42% of riders on larger capacity
machines were travelling straight ahead when involved in collisions in Suffolk (Figure 20). There were
Page | 24
two types of collision identified in the DfT research for motorcyclists travelling ahead. The first, right
of way violations, was mentioned earlier and involved vehicles turning out of junctions into the path
of motorcyclists. For riders involved in collisions in Suffolk, 63% of those on motorcycles with engines
up to 125cc and 65% of those with engines over 500cc were near a junction when they were travelling
straight ahead and could therefore have been victims of right of way violations. The second type of
collision in the DfT study involving riders travelling straight ahead resulted in a rear end shunt.
Shunts account for over 11% of all motorcycle accidents in the sample, and riders are typically found
to be more likely to be at fault than in accidents of other types. The evidence is that ‘at fault’ riders in
shunt accidents tend to be younger, more inexperienced riders, on smaller capacity machines…. It could
be that these relatively inexperienced riders are experiencing difficulties in bringing their machines to
a controlled stop, especially in wet and slippery road conditions. Lightweight bikes with separate front
and rear brakes are relatively easy to break into a skid on.18
The last manoeuvre to be examined is overtaking, with 11% of riders on smaller capacity machines
performing an overtake (2% of these on the nearside) and 13% of riders on larger capacity machines
overtaking on the offside. In the Department for Transport study, 16.5% of the collisions where a rider
was judged to be fully or partially to blame involved a motorcyclist overtaking other vehicles.19 “These
riders have a tendency to be slightly younger than the rest of the sample, and the indications are that
they have a tendency to be riding machines of a higher engine capacity than other accident-involved
drivers.”20 The research also examined instances where riders were taking the opportunity to pass
slow moving or stationary traffic by ‘filtering’. Whilst only 5% of the whole sample involved a rider
filtering, the research found that other drivers were more than twice as likely to be considered at fault
in such collisions as the motorcyclists involved21, presumably from performing manoeuvres (such as
U-turns) without checking for motorcycles. Motorcyclists on machines with engines 125cc or under
involved in collisions in Suffolk might have been filtering rather than overtaking, especially since these
motorcycles are not capable of higher speeds and some overtakes were performed on the nearside
suggesting movement through slow moving traffic.
It is possible to analyse the contributory factors (CFs) recorded by a police officer when completing
the collision records. The following analysis only looks at collisions investigated at the scene by an
officer and even then, it needs to be remembered that these factors reflect the officer’s opinion at
the time of reporting and might not be the result of extensive investigation. Analysis has been
undertaken at the collision level and therefore looks at the number of collisions where certain CFs
have been used and not at which vehicles were assigned the particular contributory factor. It is
therefore not possible to attribute ‘blame’ through this analysis but it is possible to get a general
impression of how the collisions occurred.
Page | 25
TABLE 8 - MOST COMMONLY ASSIGNED CONTRIBUTORY FACTORS TO COLLISIONS IN SUFFOLK
Collisions involving motorcycles up to
125cc
Collisions involving motorcycles over 500cc
1
Failed to look properly
38.5%
Failed to look properly
39.5%
2
Learner or inexperienced
driver/rider
27.3%
Poor turn or manoeuvre
22.7%
3
Slippery road (due to weather)
19.6%
Loss of control
21.4%
4
Failed to judge other person’s
path or speed
18.3%
Failed to judge other person’s path or
speed
17.3%
5
Loss of control
18.0%
Careless, reckless or in a hurry
13.9%
6
Poor turn or manoeuvre
14.5%
Sudden braking
12.5%
7
Sudden braking
12.9%
Travelling too fast for conditions
8.4%
8
Careless, reckless or in a hurry
12.7%
Exceeding speed limit
7.5%
9
Travelling too fast for conditions
7.8%
Road layout (e.g. bend, hill, narrow
carriageway)
6.1%
10
Following too close
6.2%
Following too close
5.7%
11
Exceeding speed limit
5.7%
Learner or inexperienced driver/rider
5.5%
12
Stationary or parked vehicle(s)
4.6%
Slippery road (due to weather)
5.5%
13
Swerved
4.0%
Aggressive driving
4.8%
3.8%
Deposit on road (e.g. oil, mud,
chippings)
4.1%
3.7%
Dazzling sun
3.4%
14
15
Road layout (e.g. bend, hill,
narrow carriageway)
Deposit on road (e.g. oil, mud,
chippings)
Table 8 shows the top 15 most commonly assigned contributory factors to collisions involving
motorcyclists in Suffolk. For collisions involving both types of motorcyclist, ‘Failed to look properly’
was the most commonly assigned contributory factor and both groups had a higher percentage of
collisions with this CF than for all Suffolk collisions (35%). Whilst it is not possible to say which party
failed to look in these collisions, the higher percentages of motorcycle collisions with this CF could be
an indication of inattentional blindness by the other driver.
For collisions involving either group of motorcyclists, about one-third of assigned contributory factors
in the table are associated with driver or rider error or reactions. The assigned contributory factors
reflect the DfT study with inexperience; slippery road surfaces; sudden braking; and right of way
violations featuring for collisions involving motorcycles with engines up to 125cc. For collisions
involving motorcycles over 500cc, the study is reflected in the loss of control; speed-related; bends;
and oil and gravel deposits CFs used in Suffolk.
Page | 26
MOTORCYCLE RIDER PROFILES
Moving away from the ‘when, where and how’ questions, we can now explore the ‘who’ question. It
is essential to understand more about the people involved in the collisions, including information
about their everyday lives, as well as demographics.
This section will concentrate on Suffolk resident motorcyclists and not necessarily those who are
involved in collisions in Suffolk. The rationale for this is threefold: that local residents are easier to
engage with than visiting motorcyclists from other areas; that the type of motorcyclist from Suffolk
who could crash elsewhere could also crash in Suffolk; and lastly, there is a responsibility to keep local
residents safe, regardless of where they could have been involved in a collision. Data has been
extracted from MAST Online for 2008 to 2012 for Suffolk residents involved in collisions nationally.
FIGURE 19 - AGE OF RIDERS FROM SUFFOLK BY ENGINE SIZE
500
450
400
350
300
250
200
150
100
50
Up to 125cc
NK
85-89
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
16-19
11-15
0
Over 500cc
The ages of riders from Suffolk, involved in injury collisions anywhere in the country, are shown Figure
21. It shows that the largest group of Suffolk riders are 16 to 19 year olds on motorcycles with engines
up to 125cc. A more detailed age analysis, by engine size, is shown in the charts below.
Figure 22 shows riders from Suffolk on 125cc or lower motorcycles by age and severity. It shows a
clear peak of 16 to 19 year olds involved in KSI and slight collisions. After 25 years old, there are very
few riders of 125cc or lower motorcycles who are involved in injury collisions.
Page | 27
FIGURE 20 - AGE OF RIDERS FROM SUFFOLK ON 125CC OR BELOW MOTORCYCLES BY SEVERITY
500
450
400
350
300
250
200
150
100
50
Fatal
Serious
NK
85-89
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
16-19
11-15
0
Slight
FIGURE 21 - AGE OF RIDERS FROM SUFFOLK ON OVER 500CC MOTORCYCLES BY SEVERITY
80
70
60
50
40
30
20
10
Fatal
Serious
NK
85-89
80-84
75-79
70-74
65-69
60-64
55-59
50-54
45-49
40-44
35-39
30-34
25-29
20-24
16-19
11-15
0
Slight
Figure 23 shows the age profile of riders from Suffolk on motorcycles with engines larger than 500cc.
It shows a much larger age range of collision-involved riders and shows a much higher severity ratio.
There appears to be two peaks of riders of over 500cc motorcycles: those aged between 20 and 35
years old and those aged 40 to 50 years old.
Motorcyclists from Suffolk who are involved in collisions are overwhelmingly male: 85% of those on
the smaller machines and 98% of those on larger machines were male.
Page | 28
Analysis has shown that 93% of riders of motorcycles 125cc and under and 74% of riders of
motorcycles over 500cc who live in Suffolk were involved in collisions in Suffolk. Tables 9 and 10 show
the ten local authority districts which had the highest number of collisions involving Suffolk resident
riders. They show that for both sizes of engines, riders from Suffolk are most likely to be involved in
collisions in Ipswich. There were more riders of 125cc and under motorcycles involved in KSI collisions
in Mid Suffolk and the most riders of over 500cc motorcycles were involved in KSI collisions in Suffolk
Coastal District.
TABLE 9 - NUMBER OF SUFFOLK RIDERS OF 125CC AND UNDER MOTORCYCLES INVOLVED IN COLLISIONS IN THESE DISTRICTS
Local Authority District
Ipswich
Waveney
Suffolk Coastal
Bury St Edmunds
Mid Suffolk
Babergh
Forest Heath
Great Yarmouth
East Cambridgeshire
Colchester
Fatal
1
0
0
0
2
1
0
0
0
0
Serious
31
29
29
20
29
15
4
6
4
1
KSI
32
29
29
20
31
16
4
6
4
1
Slight
138
131
84
74
46
43
19
8
2
4
Total
170
160
113
94
77
59
23
14
6
5
TABLE 10 - NUMBER OF SUFFOLK RIDERS OF OVER 500C MOTORCYCLES INVOLVED IN COLLISIONS IN THESE DISTRICTS
Local Authority District
Ipswich
Suffolk Coastal
Babergh
Bury St Edmunds
Waveney
Mid Suffolk
Forest Heath
South Norfolk
Great Yarmouth
Braintree
Fatal
4
1
5
3
3
2
1
0
0
1
Serious
21
29
23
24
21
19
8
9
6
6
KSI
25
30
28
27
24
21
9
9
6
7
Slight
51
34
32
30
32
28
13
9
4
2
Total
76
64
60
57
56
49
22
18
10
9
In order to put motorcycle rider involvement into context, figures have been obtained which show the
number of licensed motorcycles per local authority area. This has been used to determine the resident
rider risk by licensed motorcycles for each year, using a 100 based index to compare each authority
with the national relationship between ownership levels and collision involvement. Figures from MAST
for 2008 to 2012 have been used to determine the number of Suffolk resident motorcyclists involved
in collisions anywhere in the country. Experian’s Mosaic classification has been used to determine the
most similar authorities in terms of socio-demographic composition to Suffolk’s districts. Only
highways authorities have been included as vehicle licensing information is not available at the district
level. Figure 24 shows the indices compared to the national norm, so any figure over 100 is a resident
rider risk higher than expected given the motorcycle ownership level of that area. The dashed circle
represents the overall index of 75 for Suffolk and its neighbours, indicating that this region is below
Page | 29
the national resident rider risk when numbers of licensed motorcycles are taken into account. With
an index of 75, Suffolk has a lower resident risk that the neighbouring counties of Essex and
Cambridgeshire but higher than Norfolk. Of the eleven most similar authorities, Suffolk has a higher
resident risk than nine of them (lower than the two cities of Plymouth and Portsmouth).
FIGURE 22 - RESIDENT RIDER RISK INDEXED BY NUMBER OF LICENSED MOTORCYCLES
150
Portsmouth City
117
GREAT BRITAIN
100
125
100
75
Somerset County
Newport City
49 Devon County
45
52
Plymouth City
90
50
Gloucestershire County
52
25
Essex County
81
Wiltshire (from 2009)
55
0
Shropshire (from 2009)
59
Cambridgeshire County
77
Cumbria County
62
SUFFOLK & NEIGHBOURS
75
SUFFOLK
75
North Yorkshire County
65
Worcestershire County
Norfolk County
65
65
Suffolk and Neighbours Index of 75
Licensed MC
An alternative way of looking at relative risk is by head of population. Figure 25 compares the police
force areas of the Eastern Region. It shows that the annual average number of motorcyclists per head
of population is higher for Suffolk than elsewhere in the Eastern region and is higher than the rate for
Great Britain. Bedfordshire has the lowest rate of the region and Cambridgeshire and Norfolk have
similar rates per head of population as do Essex and Hertfordshire.
Page | 30
Riders/100,000 population
FIGURE 23 - AVERAGE ANNUAL MOTORCYCLISTS INVOLVED IN COLLISIONS PER HEAD OF POPULATION BASED ON RESIDENCE (2008-2012)
50.0
45.0
40.0
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
The home location of Suffolk riders involved in injury collisions anywhere in the country has been
analysed. These are shown in the following two maps (Figures 26 and 27). The first map shows the
home locations of riders on motorcycles up to 125cc who were involved in collisions anywhere in the
country. Given local adult populations, there were high rates of these riders in the Waveney district in
Lowestoft and Beccles; in the coastal area east of Woodbridge, including Rendlesham; south and west
of Ipswich; south of Hadleigh; north of Long Melford; the centre of Bury St Edmunds; and a large rural
area north west of Stowmarket.
Motorcyclists involved in collisions on large bikes tend to be live in the more rural areas of Suffolk.
There are over-representations in the coastal area east of Woodbridge; in the Southwold area; in a
large rural area north of Ipswich, stretching from Grundisburgh in the east to Lavenham in the west
and up to the north of Stowmarket. There are also higher numbers of motorcyclists involved in
collisions from an area covering the western border of Suffolk, from Clare to Lakenheath; and smaller
concentrations in parts of Lowestoft, Ipswich, Felixstowe and the East Bergholt/Capel Saint Mary area.
Page | 31
FIGURE 24 - HOME LOCATION OF SUFFOLK MOTORCYCLE RIDERS (UP TO 125CC)
FIGURE 25 - HOME LOCATION OF SUFFOLK MOTORCYCLE RIDERS (OVER 500CC)
Page | 32
Rurality classification systems have been developed by the Government which define the rurality of
small area geographies (known as Lower Layer Super Output Areas in England and Wales and Data
Zones in Scotland and have average populations of 1,400 people). Each of these small areas was
defined as either ‘Rural’, ‘Town’ (which is a sub-class of ‘Rural’) or ‘Urban’ (which are settlements with
over 10,000 residents). The following tables show the percentage of Suffolk riders in the two engine
size groups by severity and the three levels of rurality. Table 11 shows that just over 50% of Suffolk
motorcyclists of both engine sizes who were involved in KSI collisions live in urban areas. The
percentages of urban dwellers increases for both groups of riders when total collision involvement is
considered.
TABLE 11 - HOME RURALITY OF SUFFOLK RESIDENT RIDERS INVOLVED IN COLLISIONS
Rurality Level
Rural
Town
Urban
KSI Collision Involvement
Up to 125cc
Over 500cc
26%
25%
20%
19%
54%
56%
All Collision Involvement
Up to 125cc
Over 500cc
24%
25%
16%
17%
60%
58%
MOSAIC ANALYSIS
As well as demographic and spatial analysis of motorcyclists, we can also undertake sociodemographic analysis using Mosaic. Mosaic is intended to provide an accurate and comprehensive
view of citizens and their needs by describing them in terms of demographics, lifestyle, culture and
behaviour. By matching postcodes we can segment the motorcycling community into one of 15 groups
and analyse their relative representation in the statistics based on population figures.
Analysis has been carried out using MAST Online and focuses on riders from Suffolk involved in
collisions between 2007 and 2011. Mosaic data for 2012 will be in MAST soon but was not available
at time of writing.
Figures 28 and 29 below show Suffolk resident motorcycle riders involved in collisions anywhere in
the UK, grouped by Mosaic Group of the community in which they live. The 15 Groups are shown in
the order in which they feature. Mosaic classification is based on the individual postcodes provided in
STATS 19 records for each casualty and uses the Experian Mosaic socio-demographic classification
system (for details see http://publicsector.experian.co.uk/Products/Mosaic Public Sector.aspx).
Typically 85% of postcodes can be matched to a Mosaic Type, so this analysis is based on about five
out of six of all Suffolk resident riders.
The shaded area indicates the number of motorcyclists in each Mosaic Group, with figures
corresponding to the left hand vertical axis. The darker bars show the “Index” for each Mosaic Group.
An Index value of 100 indicates that the number of motorcyclists is in proportion to the population of
Suffolk’s communities where that Group predominates. A value of 200 would mean that this Group is
involved in collisions at twice the expected rate; a value of 50 would imply half the expected rate.
Displaying the data overlaid on a single chart allows quick and easy analysis of total motorcyclists and
relative risk. The Index value becomes less significant as the number of riders decreases and random
change lowers confidence levels.
Page | 33
When carrying out Mosaic analysis you initially look for both levels of high representation and high
index scores in individual Groups and this is the case with Group K for small motorcycles and Group E
for large motorcycles. Groups I and O are over represented compared to the local population but do
not represent a high number of actual motorcyclists of 125cc or below machines. The same applies to
Groups F and I for large motorcycles.
It is worth noting that Group B are the most represented groups in both small and large bikes but their
index scores are only around the expected level. They are therefore no safer than to be expected and
should be considered as part of any intervention design, even if they are not the focus of it.
FIGURE 26 - RIDERS OF 125CC OR BELOW MOTORCYCLES FROM SUFFOLK INVOLVED IN COLLISIONS IN GB, GROUPED BY MOSAIC GROUP
(2007-2011)
150
200
186
180
158
160
140
129
100
113 110
107
120
75
100
81
50
89
80
89
74
Index Value
Number of Riders
125
60
40
25
Riders
20
Index
0
0
B
K
J
E
A
D
I
H
O
F
L
M
G
N
C
MOSAIC TYPE
Residents with sufficient incomes in right-to-buy social housing (Mosaic Group K) is over-represented
against the Suffolk population and consists of a high number of motorcyclists on machines up to 125cc.
This Group consists of council tenants, living in former council estates with comfortable lifestyles. The
number of riders of motorcycles up to 125cc involved in collisions from communities of Lower income
workers in urban terraces in often diverse areas (Mosaic Group I) is a lot lower than Group K but are
over-represented compared to the Suffolk population. These communities are often ethnically diverse
and consist of young singles and couples in routine occupations. Families in low-rise social housing
with high levels of benefit need (Mosaic Group O) are the most over-represented amongst Suffolk
riders on small motorcycles but represent relatively small numbers. These communities consist of
many unemployed, one parent families who are dependent on the state. More information on these
Groups is provided throughout this section. All three of these Mosaic Groups become more overrepresented when indexed against average annual mileage as opposed to population, indicating that
they are more collision-involved than expected, given their low mileage. However, mileage figures in
Mosaic refer to all traffic and not specifically motorcycle miles ridden.
Page | 34
FIGURE 27 - RIDERS OF OVER 500CC MOTORCYCLES FROM SUFFOLK INVOLVED IN COLLISIONS IN GB, GROUPED BY MOSAIC GROUP (20072011)
100
200
180
160
140
139
140
119
111
120
109
100
50
100
98
97
80
85
Index Value
Number of Riders
75
60
25
40
Riders
20
Index
0
0
B
E
A
J
D
K
F
I
H
L
O
N
M
G
C
MOSAIC TYPE
Middle income families living in moderate suburban semis (Mosaic Group E) are the most overrepresented compared to the Suffolk population and represent a high number of collision involved
riders of motorcycles with engines over 500cc. These communities consist of middle aged, married
manual and white collar workers with children. Couples with young children in comfortable modern
housing (Mosaic Group F) are also over-represented but there are smaller numbers of motorcyclists
in this Group. These families live in comfortable homes with their young children and have good
incomes. As with the riders of 125cc or below motorcycles, Mosaic Group I is also over-represented.
Analysing the indices by annual average mileage changes the over-representation: Groups E and I
become more over-represented when mileage is taken into account where Group F has an index of
100, suggesting that this Group’s collision involvement is exactly as expected, given their annual
mileage.
Tables 12 and 13 below summarise some of the main characteristics of the Mosaic Groups overrepresented by the two motorcycle engine sizes. The first table shows that there is a coherent group
of motorcyclists on the small engine machines who share the characteristics of low incomes and high
numbers of benefit claimants. The Mosaic Profiles reflect the STATS19 analysis of riders with having
high numbers of 16 to 19 year olds and owners of motorcycles with engines 125cc and below. The
shared characteristics provide an insight to communicating with these motorcyclists: they have a poor
opinion of the police so perhaps there are other organisations who would be more appropriate
communicators. These riders also have low internet use so delivering road safety messages via
websites might be inappropriate. There are clearly preferred communication channels for the parents
of these young riders: face-to-face interventions and information via local newspapers would work
with this group. These channels could be used to communicate safety messages to parents and could
also work with the riders themselves.
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TABLE 12 - CHARACTERISTICS OF MOSAIC GROUPS OVER-REPRESENTED AMONGST RIDERS OF 125CC OR LESS MOTORCYCLES
Group K Group I Group O
Presence of 16 to 19 year olds
Presence of adult children
3 or more children
Low incomes
Benefit claimants
Own 125cc or under motorcycle
Poor opinion of police
Low internet use
Communication Preferences (of adults within the home)
Face-to-face
Local newspapers
Interactive TV
National newspapers
Magazines
Telephone
Mobile phone
Post
Internet
The second table, for riders of over 500cc motorcycles, shows more diverse Groups. There are
differences in income levels; age; age of children; numbers of cars; opinion of police; and internet
usage. The communication preferences also differ across the three Groups. The age groups do reflect
the two peaks highlighted in the STATS19 analysis and the number of cars perhaps shows the
differences in journey purposes: 24% of the motorcyclists on the larger bikes were commuting or
riding for work and these could be those from Group I as they don’t have alternative forms of
transport. Conversely, Groups E and F, who have multiple cars and choice of transport, could be leisure
riders.
The Mosaic profiling suggests that there are several different types of motorcyclist from Suffolk who
are involved in collisions and that a one-size-fits-all approach is not appropriate and different types of
intervention will be required to target different types of rider. The lack of homogeneity amongst
motorcyclists is explored further in the ‘Summary of Other Evidence’ section with the TRL
segmentation of motorcyclists. The STATS19 and Mosaic analysis are used with the TRL segmentation
to create ‘personas’ later in this document to provide a complete insight into the types of Suffolk
motorcyclist involved in collisions.
Page | 36
TABLE 13 CHARACTERISTICS OF MOSAIC GROUPS OVER-REPRESENTED AMONGST RIDERS OF OVER 500CC MOTORCYCLES
Group E Group F Group I
Aged 20-35 years old
Aged 40-55 years old
Young children
Adult children
Low income
Medium Income
High Income
Own over 500cc motorcycle
Own 2 or more cars
Fair/good opinion of police
Use internet at home/work
Communication Preferences (of adults within the home)
Face-to-face
Local newspapers
Interactive TV
National newspapers
Magazines
Telephone
Mobile phone
Post
Internet
The following map (Figure 30) shows the LSOAs within Suffolk where the five main Mosaic Groups
identified within this report are the dominant Groups. For further information about super output
areas, refer to
http://neighbourhood.statistics.gov.uk/dissemination/Info.do?page=aboutneighbourhood/geogr
aphy/superoutputareas/soa-intro.htm.
The map shows that Middle income families living in moderate suburban semis (Group E) are most
dominant in parts of Haverhill, Bury St Edmunds, Sudbury and Stowmarket; Claydon; Shotley;
Holbrook; Rendlesham; the outskirts of Ipswich; the Trimley St Mary area; parts of Beccles; the
outskirts of Lowestoft; and north west of Mildenhall. Couples with young children in comfortable
modern housing (Group F) dominates the areas of Lakenheath; RAF Mildenhall; parts of Newmarket,
Bury St Edmunds, Stowmarket and Haverhill; the outskirts of Ipswich and Lowestoft; RAF Wattisham;
Rushmere and Kesgrave; Sutton; Ixworth and Elmswell. Lower income workers in urban terraces in
often diverse areas (Group I) dominates pockets of Ipswich; and small areas of Haverhill and Lowestoft.
Residents with sufficient incomes in right-to-buy council house (Group K) dominates Mildenhall; parts
of Newmarket, Bury St Edmunds, Stowmarket, Haverhill, Sudbury, Ipswich, Lowestoft and Felixstowe;
in Brandon; Saxmundham; Kessingland; Beccles; and around Southwold. Families on low-rise council
housing with high levels of benefit need (Group O) dominate small areas of Lowestoft, Stowmarket,
Ipswich and Sudbury.
Page | 37
FIGURE 30 – AREAS OF RESIDENCE FOR SPECIFIC MOSAIC GROUPS IN SUFFOLK
Table 14 overleaf provides a summary of some main characteristics of these over-represented Groups
and these can be used to create a picture of the target audience in terms of economic and educational
position; and family life. This information is invaluable for understanding target audiences and
knowing how to communicate with them.
Page | 38
TABLE 14 - SUMMARY OF CHARACTERISTICS OF OVER-REPRESENTED MOSAIC GROUPS
Group E
Group F
Group I
Group K
Group O
Middle income
families living in
moderate suburban
semis
Couples with young
children in
comfortable modern
housing
Lower income
workers in urban
terraces in often
diverse areas
Residents with
sufficient incomes in
right-to-buy council
houses
Families in low-rise
council housing with
high levels of benefit
need
These
areas
contain
a
population
of
mostly
married
people of middle
age,
living
together with their
children in owneroccupied family
houses
built
between
the
1930s and the
1960s. Most of
these residents are
comfortably off,
though few are in
either the highest
or lowest income
brackets. People
belonging to this
Group mostly live
in what might be
described
as
residential
neighbourhoods;
places from which
people commute
daily by train, bus
or car rather than
on foot or by
bicycle.
These
communities
contain
mostly
young
married
couples and cohabitees
whose
lives focus on the
needs of their
growing families
and the creation of
a
comfortable
home in which to
enjoy family life.
Most of these
people
have
acquired sufficient
qualifications to be
well set on a
technical, junior or
middle
management
career in which
they benefit from
annual increments
or
periodic
promotions.
These
areas
contain
people
with
poor
qualifications who
work in relatively
menial,
routine
occupations and
live close to the
centres of towns in
streets of small
terraced houses
built in the years
prior to the first
world war. The
majority
of
residents
are
young; some are
still single and
others live with a
partner and often
look after children
of nursery school
or primary school
age. The Group is
most common in
London and in the
inner areas of
provincial cities.
Examples
also
occur in small
industrial towns
and around the
core of many
moderate
sized
market towns.
Many of Group K
live on former
council
estates,
ones which were
comparatively well
built
and
pleasantly laid out
and where a large
proportion
of
properties have
been purchased
under right-to-buy
legislation.
In
general,
these
communities
contain
people
whose
parents
might
have
described
themselves
as
belonging to the
“working
class”
but
who,
as
consumers, aspire
to a “middle class”
lifestyle, at least in
terms
of
the
products
and
services they buy.
These
areas
contain some of
the
most
disadvantaged
people in the UK,
including
significant
numbers
who
have been brought
up in families that
have a history of
dependency
on
the state for their
welfare. Residents
of
these
communities are
surrounded
by
others who find it
difficult to make
ends meet and
whose
children
will find it more
difficult to achieve
any
sort
of
educational
attainment. Many
work in semiskilled jobs on
modest salaries,
others may be
unemployed, on
long term sick or
raising children on
their own.
INDEX OF MULTIPLE DEPRIVATION (IMD)
As well as looking at the Mosaic socio-demographic classifications, it also possible to look at relative
wealth using the UK IMD values for each postcode. IMD uses a range of economic, social and housing
data to create a single deprivation score for each small area of the country. The analysis (Figure 31)
uses deciles, which creates ten groups of equal frequency, ranging from the 10% most deprived areas
to the 10% least deprived areas.
Page | 39
FIGURE 31 - SUFFOLK RESIDENT RIDERS INVOLVED IN INJURY COLLISIONS ON 125CC OR UNDER MOTORCYCLES BY IMD
5%
6%
6%
8%
LeastDeprived10%
LessDeprived20%
10%
LessDeprived30%
16%
LessDeprived40%
LessDeprived50%
4%
MoreDeprived50%
MoreDeprived40%
MoreDeprived30%
16%
MoreDeprived20%
17%
MostDeprived10%
12%
Suffolk resident riders on motorcycles up to 125cc tend to come from the 30% least deprived to the
50% most deprived areas and therefore are not particularly over-represented amongst the most or
least deprived areas. The results may be slightly surprising as it could be expected that highly deprived
areas would have the most moped and scooter rider residents as it is a cheaper form of transport. To
some extent, the results reflect the relatively affluence of populations within Suffolk but it also
highlights the fact that riding smaller motorcycles is something that young people choose to do in
order to gain some freedom of mobility.
For Suffolk resident riders on motorcycles over 500cc, there are over-representations in the least
deprived 20-50% and also the more deprived 40-50%. The riders are in the middle deciles and
therefore do not live in the most or least deprived areas. As with the smaller motorcycles, in some
respects this reflects the relative affluence of Suffolk communities.
Page | 40
FIGURE 32 - SUFFOLK RESIDENT RIDERS INVOLVED IN INJURY COLLISIONS ON OVER 500CC MOTORCYCLES BY IMD
4%
3%
9%
LeastDeprived10%
9%
14%
LessDeprived20%
LessDeprived30%
5%
LessDeprived40%
LessDeprived50%
MoreDeprived50%
13%
15%
MoreDeprived40%
MoreDeprived30%
MoreDeprived20%
MostDeprived10%
12%
16%
PERSONAS
Following the analysis of risk, it is necessary to combine the elements of rider and collision profiling to
create a persona or personas which capture the key characteristics of those communities or groups
most at risk. Although a persona will not typify all, or perhaps even a majority of those involved in
collisions, it should represent a significant proportion of those who are most vulnerable.
The analysis of the socio-demographic data as well as the collision information has allowed a picture
to be built up about the kinds of motorcyclists from Suffolk who are involved in collisions. More than
one type of rider of motorcycles over 500cc emerged, both in terms of socio-demographic profiling
and collision analysis. The findings, taken alongside the TRL segmentation22 of motorcyclists discussed
later in the document and the DfT in-depth analysis of motorcycle collisions23, allow key characteristics
to be collated into personas. Parallels have been drawn from the multiple data sets in the creation of
these personas to ensure alignment along clear data points.
There are 4 personas which have emerged from the analysis (shown in order of the number of
motorcyclists they represent):
1. ‘Jack’ – is in his later teens and is a student at college. He is most likely to be involved in a
collision on weekdays on the way to or from college and is most vulnerable in the first few
months of the autumn term when he is riding his motorbike consistently for the first time. The
collisions occur in 30mph areas on A or unclassified roads. Junctions, particularly T-junctions
and crossroads present problems for Jack and these could be due to poor manoeuvres due to
inexperience or right of way violations on behalf of other drivers, which could be reduced by
improving Jack’s visibility through appropriate clothing or road positioning. He is also involved
in rear end shunts, caused through loss of control from sudden braking, often on wet surfaces.
Page | 41
Filtering can also be a problem. Jack lives in Mosaic Group K and lives at home with his mum
and teenage siblings in a low income home. Lack of money might influence the quality of
safety equipment purchased and maintenance of the motorbike. However, Jack is likely to fall
into the ‘Car aspirant’ segmentation of TRL’s research and whilst this group had low road
safety knowledge, they displayed positive attitudes after information had been given to them
and this affords opportunities to improve his knowledge through engagement. (Between 2008
and 2012, there were 103 riders from Suffolk involved in collisions that fit this persona).
2. ‘Paul’ – is in his early 50s and works in middle management. He lives in Mosaic Group E and is
married with two teenage children. There are multiple cars in the household and he
commutes to work in one of them, leaving the motorbike for weekends for leisure rides. He
has a comfortable income and now the children are older, he has more disposable income for
motorcycling as a hobby. He is returning to motorcycling after a break and lack of recent
experience can be an issue. Paul’s collisions are often single vehicle and are often on bends
and away from junctions. Overtaking manoeuvres can also be a problem. Collisions take place
at weekends, particularly at lunchtime or early afternoon. Paul, as a leisure rider, tends to get
the motorbike out in fine weather in the summer months. The collisions occur on 60mph A
roads in rural areas. Whilst it is not possible to use the TRL questionnaire to determine which
segment Paul should belong to, comparisons of key characteristics of Paul and the segments
suggest that Paul is a ‘Riding Hobbyist’. More information is provided in Appendix A including
how this segment try to avoid risky situations and choose to purchase their motorcycle and
equipment new from specialist outlets. Despite attempting to avoid risky situations and
ensuring they wear the correct clothing, ‘Riding Hobbyists’ have the lowest levels of training.
(Between 2008 and 2012, there were 70 riders from Suffolk involved in collisions that fit this
persona).
3. ‘James’ – is in his mid-30s and lives with his wife and 3 young children in a comfortable home
in a community of Mosaic Group F households. He earns a good income through his higher
management role. As with Paul, there are multiple cars in the household and he commutes to
work by car. He is a relatively novice rider who has picked up the hobby recently. His collisions
follow a similar pattern to Paul’s and there are similarities in their communication preferences
of magazines, post and internet; as well as their good opinion of the police which means that
Paul and James could be targeted for interventions together, bearing in mind that one is a
novice and the other is returning to motorcycling. James seems to fit into the ‘Performance
Hobbyist’ segmentations, who are solitary, summer-only riders. This group admits to overestimating their abilities and have taken risks to impress others and have ridden when tired.
This group are also most likely to wear their helmet after dropping it on a hard surface. They
are least likely to wear high visibility clothing and will not be put off from riding after seeing a
serious collision involving a motorcyclist. Unlike the ‘Riding Hobbyists’. ‘Performance
Hobbyists’ will undertake training. (Between 2008 and 2012, there were 36 riders from Suffolk
involved in collisions that fit this persona).
4. ‘Dave’ – is in his mid-20s and lives in the deprived community of Mosaic Group I. He is
separated from his partner and three young kids, who live nearby. He works as a plant
operative but has previously been unemployed. He doesn’t own a car and either travels to
work by public transport or commutes on his motorbike. Twenty percent of Group I’s who
own a motorcycle have one with an engine size of 501-700cc. Dave’s collisions are more like
Jack’s: they tend to occur on weekdays at commuter times, especially between 4 and 6pm.
Page | 42
The collisions tend to be in urban areas in 30mph limits and like Jack, junctions can be an issue
as can filtering. Dave appears to be a ‘Car rejecter’ who chooses his bike for reliability, comfort
and fuel consumption. This group do not care for motorcycles but do care for low-cost
mobility. They rate motorcycling as risky but don’t necessarily wear protective trousers or
boots. If they have high visibility clothing, they do tend to wear it on every trip. (Between 2008
and 2012, there were 35 riders from Suffolk involved in collisions that fit this persona).
ENGAGEMENT PLAN
This section looks at the existing Suffolk Ride brand and how it sits alongside the collision data and
rider profiles. As a gap analysis, suggestions are made where refinements could be made to the brand
to ensure that it is as effective as possible, in terms of using appropriate communication channels for
the target audience and that the messages are sculptured to fit the personas’ needs.
TRAINING COURSES
Suffolk Ride provides training courses for young riders through the Young Rider scheme and for novice
and returning riders via the Rider Plus scheme, as detailed in the next section. Improving skills for all
four rider personas should reap benefits and providing additional information on appropriate
equipment and clothing as part of the courses should also be beneficial. Recruitment methods will
differ across the personas – low internet use by ‘Jack’ and ‘Dave’ mean they are unlikely to find
information on the courses via the web. As the summary of the Enhanced Rider Scheme in the ‘Existing
Schemes’ section showed, motorcyclists are unlikely to seek out training and they need to be
encouraged to attend. The courses need to be carefully marketed as to the benefits – for ‘Jack’ and
‘Dave’, money is tight and there needs to be a real financial incentive to attending a training course.
ENGAGEMENT DAYS
Engagement days are a positive way of communicating with all types of motorcyclist by improving
knowledge, breaking down barriers and encouraging training course attendance. The moped days
detailed in the next section are perfect for ‘Jack’ by offering practical advice on equipment, bike
maintenance and training as the TRL segmentation showed that whilst he has a low knowledge of the
risks of motorcycling, small amounts of information can improve attitude to risk. These days are also
attractive to Jack as they cost nothing in terms of his personal time or money and he doesn’t have to
seek them out as they occur at his college.
BikeSafe is also a positive method of engagement, particularly with ‘Paul’ and ‘James’ who have a
positive opinion of the police. The workshops should provide them with information about equipment
and signpost them on to post-test training. Website marketing of the scheme should work with these
groups.
Page | 43
Opportunities for engagement, such as the Suffolk Ride Motorcycle Show (detailed in the next section)
could be replicated. By hosting an event in a popular visitors’ space, such as Felixstowe sea front, there
are opportunities to speak to motorcyclists and other motorists alike. Breaking down barriers between
motorcyclists and other motorists is important to combat inattentional blindness and events where
bikers are de-mystified could reap large rewards. Marketing of such an event should be via local
newspapers and websites in order to capture all of the persona groups. The use of social media here
could be worthwhile, as a means of originating contact and creating an ongoing community.
COMMUTER RIDER SUPPORT
The Wheels to Work scheme, explained in the next section, provides transport via a loaned motorcycle
to those finding it difficult to travel to education or employment. This scheme would be of most
benefit to ‘Jack’ and ‘Dave’ and there are opportunities to engage with these riders when talking about
the clothing and equipment provided and in the training provided. There are areas within Suffolk
Coastal District where ‘Jack’ and ‘Dave’ live but it would be worth considering extending the scheme
to Waveney and Ipswich districts, from a road safety viewpoint.
OTHER MOTORISTS
As suggested under ‘Engagement Days’, there are opportunities to communicate with other motorists
in order to reduce the levels of risk experienced by motorcyclists. Whilst it is important to increase
the skills and improve the attitudes and behaviour of motorcyclists, only a quarter of the collisions are
single vehicle. It is therefore necessary to ensure that other motorists are aware of the need to look
out for motorcyclists and to account for them in their manoeuvres. Using materials created for the
DfT THINK BIKE ‘Named Riders’ campaign24 or creating local bespoke materials, such as the Someone’s
Son campaign cited in the ‘Existing Schemes’ section, could help to ‘personalise’ motorcyclists and
help other motorists see them as other than nuisances or non-entities. Campaigns aimed at other
motorists should also help motorcyclists feel less victimised and show them that other parties involved
in collisions are also being targeted.
ENFORCEMENT
Whilst the personas and TRL segmentations did not reveal extensive illegal riding, exceeding the speed
limit did feature in the contributory factors. Mobile speed enforcement is undertaken by Suffolk
SafeCam and it is likely that enforcement is targeted, where appropriate, towards motorcyclists.
Emulating specific motorcycle enforcement operations, such as Operation Achilles carried out by
Humberside Police, could be considered.25 The Operation, which was highly commended in the 2012
Prince Michael International Road Safety Awards, involves using intelligence and a range of vehicles
to target specific motorcycle routes. Offending motorcyclists who meet the attendance criteria are
offered a diversion from prosecution via the RIDE scheme; an evaluation of the scheme is discussed in
the ‘Existing Schemes’ section.
BIKER MAGAZINE
The ‘Local Biker’ magazine is produced by IND Media and is tailored to a local area, based on analysis,
local issues and content agreed with the procuring authority. It is currently produced for a number of
areas, including Essex. An example of the magazine can be found at:
http://www.saferrider.org/wpcms/wp-content/uploads/52367-Essex-Printers-Lo-Res-Proof.pdf
Page | 44
The magazine is funded through advertising and provides a low cost option to disseminate local
messages to motorcyclists. The magazine can be distributed through a range of outlets, including
motorcycle dealerships. A Suffolk version of the magazine could be produced to provide information
on training; clothing and safety equipment; vehicle checks; specific routes; and events.
WEBSITE & APPS
The elements above could be underpinned by website information. A positive development would be
to disaggregate advertising and content so that events are promoted in conjunction with ongoing
dialogues with motorcyclists about training, equipment and issues that matter to them. Strong
examples of using websites and social media for continual engagement, such as Safer Rider and
Motorcycling Matters, are explored in the ‘Existing Schemes’ section.
Social media platforms, such as Facebook and Twitter, could be considered to disseminate messages
and encourage engagement.
Clear links to clubs and training organisations, such as the IAM and RoSPA, will encourage a community
feel to the website and provide significant allies in the form of post-test training groups.
BRAND BLINDNESS
If it can be achieved, making one brand the overall rider intervention brand and other initiatives
subservient to the overall aim may help in securing greater recognition for marketing and PR.
Therefore, if Wheels to Work, engagement days and the Fire Bike are all seen as initiatives of Suffolk
Ride it strengthens the overall voice, rather than fracturing it.
MESSAGES
K EY T HEME
Enjoy motorcycling safely. Riding skills and the right equipment are an important feature of
modern biking.
M EDIA M ESSAGES
 ‘Get Prepped’ – Encouraging riders to undergo post-test training through explaining the
benefits in terms of improving their riding without compromising their enjoyment
 ‘Get Kitted’ – Making sure that motorcyclists where the correct protective clothing
 ‘Get Spotted’ – Making motorcyclists aware that they need to take care in certain road
situations where other vehicles may turn across them. Riding defensively, accepting their
vulnerability and increasing their visibility can help make other road users aware of their
presences.
F ACTS
WHEN?


Page | 45
Commuter riders are involved in collisions in morning and afternoon rush hours and
leisure riders are involved in collisions at weekends in the afternoons.
Most riders are involved in collisions in daylight, with leisure riders during the summer
and those on motorcycles with engines up to 125cc at the beginning of the autumn
term.
HOW?



Inexperience; slippery road surfaces; sudden braking; and right of way violations
appear to feature in collisions involving motorcycles with engines 125cc and under.
For collisions involving motorcycles over 500cc, loss of control; speed-related; bends;
and oil and gravel deposits contributory factors were used in Suffolk.
The most common reason was ‘Failed to look properly’
M EASURES
Clear aims and objectives for Suffolk Ride will inform which measures are appropriate to determine
the efficacy of the brand. Current aims and objectives may need to be re-evaluated to ensure that
they are aligned with this research. The list below are some suggestions of measures which could be
adopted.
Number of training courses attended
Number of engagement days held
Number of Wheels to Work motorcycles loaned
Numbers of RIDE referrals
Website traffic
Page | 46
CURRENT LOCAL SCHEMES
There have been a number of schemes implemented in Suffolk to address the motorcycle collision
issues.
SUFFOLK RIDE BRAND
Over the past decade, motorcycle safety has been promoted using the Suffolk Ride brand
(http://www.suffolkride.net/). The brand has been used to deliver training and awareness initiatives,
including those detailed below. Road safety professionals regularly met with motorcycle dealers, clubs
and trainers to discuss road safety, training and motorcycling issues.
A number of pit stop days were organised with the Police under the Suffolk Ride brand, which involved
ride outs and assessed rides.
SUFFOLK RIDE MOTORCYCLE SHOW, FELIXSTOWE, MAY 2009
A major event took place in Felixstowe in May 2009, involving over 50 stands and stalls of motorcycle
dealers, trainers, clubs and enthusiasts. Suffolk County Council’s road safety team played a key role
on the day, offering safety information and free DVDs from their road safety display van. “The
emphasis of the day was to enjoy motorcycling safely. Riding skills and the right equipment are an
important feature of modern biking.”26
YOUNG RIDER SCHEME
The Young Rider Scheme is part of the Suffolk Ride brand and is aimed at 16 to 19 year olds with
mopeds or small machines up to 125cc. The cost of the course is £75 and provides an insurance
discount to attendees on completion. The course aims to promote a positive riding attitude through
exploring peer pressure, group riding and attitudes to other road users. It also aims to help attendees
develop risk reduction strategies through wearing protective clothing, understanding visibility issues
and learning machine control techniques. The course allows attendees to develop observation,
anticipation and planning skills in urban and rural settings and through exploring filtering and
positioning.27
RIDER PLUS SCHEME
The Rider Plus Scheme is also part of the Suffolk Ride brand and is aimed at Suffolk based riders who
have recently passed their DSA ‘big bike’ test or have returned to motorcycling after a break.
Attendance of the course allows participants to gain the DSA Enhanced Rider certificate. The cost of
the course is £125 and also entitles participants to an insurance discount on completion. The aims of
the course are very similar to those of the Young Rider Scheme by focusing on promoting a positive
riding attitude; developing risk reduction strategies; and developing observation, anticipation and
planning skills.28
MOPED DAYS
There have been a number of events at schools and colleges aimed at moped riders. Otley College has
hosted several of these days in recent years, which involved multiple organisations. Mechanics and
local dealers carried out free bike safety checks and the Police attended to talk to young riders about
training. Road safety officers from Suffolk County Council operated a driving simulator and discussed
defensive riding techniques. They also promoted wearing appropriate protective clothing.29
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WHEELS TO WORK SCHEME
Suffolk County Council supports the Community Action Suffolk Wheels to Work scheme which loans
scooters to residents of Suffolk Coastal District Council who have difficulty accessing suitable transport
to get to work, training or college. For a small administrative fee and the cost of fuel, the scheme loans
residents in need a scooter and provides insurance, MOT, tax, training and safety and security
equipment.30
BIKESAFE
Suffolk County Council supports Suffolk Constabulary’s BikeSafe scheme. The courses are run in the
spirit of the nationally recognised Police-run BikeSafe initiative and involves workshops aimed at riders
who want to improve their skills and ability to become better and safer riders.31
The national format of the course involves a workshop which explores the issues facing modern
motorcyclists. The principles of advanced riding are explored through an on-road element and a
BikeSafe observer undertakes an assessment of the motorcyclist’s riding and provides feedback on
where development is required. BikeSafe intends to ‘bridge the gap’ to post-test training by
encouraging attendees to undergo Institute of Advanced Motorists (IAM), Royal Society for Prevention
of Accidents (RoSPA) or Enhanced Rider Scheme (ERS) courses.32
FIRE BIKE PROJECT
Suffolk Fire and Rescue Service are investigating obtaining a ‘Fire Bike’, which would be used to
promote existing initiatives via motorcycle shows and events. Hampshire Fire and Rescue Service
currently have a Honda Fireblade Legend which is used as an engagement tool to promote their
RideSMART brand. RideSMART has similar aims to Suffolk Ride in the form of promoting key messages
of skills, maintenance, hazards and risks, respecting other road users and clothing.33
SUMMARY OF OTHER EVIDENCE AND SUCCESSFUL SCHEMES
SUMMARY OF OTHER EVIDENCE
The STATS19 and MAST analysis of Suffolk motorcyclists has shown that there is not a stereotypical
type of rider to target. Research has been undertaken to try to provide a better understanding of the
needs, motivations and perspectives with respect to road safety.34 The research aimed to address:



How do motorcyclists make decisions about issues that impact on their safety?
How do these decision making strategies which motorcyclists use relate to their actual risk
associated with their choice? And
What are the opportunities which would influence the decision making process of
motorcyclists in a positive way?35
The research used a combination of qualitative and quantitative approaches to try to meet the aims.
The qualitative part of the research yielded an understanding of the motivations of 66 riders from
different areas of the country and who rode different types of motorcycle and for different purposes.
The qualitative work was used to design and interpret the quantitative element of the study, which
involved asking 1,019 motorcyclists questions from a structured questionnaire. The responses
resulted in the creation of seven segments of motorcyclists, as shown in Table 15.
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TABLE 15 - TRL SEGMENTATION OF MOTORCYCLISTS36
No.
1
Segment
Riding hobbyists
2
Performance disciples
3
Performance hobbyists
4
Look-at-me enthusiasts
5
Riding disciples
6
Car aspirants
7
Car rejecters
Description
These are older, summer-only riders who enjoy the social
interaction with other riders almost as much as the riding itself –
and who like to look the part
These are committed, all-year riders with a total focus on high
performance riding – and a strong dislike for anything that gets in
the way of it
These are solitary, summer-only riders, for whom riding is all
about individual experiences and sensations – and who are not
concerned about what other riders are doing
These are young (or never-grew-up) riders with limited
experience but limitless enthusiasm, for whom riding is all about
self-expression and looking cool
These are passionate riders for whom riding is a way of life, built
on a strong relationship with the bike itself and membership of
the wider fraternity of riders
These are young people looking forward to getting their first car
when age/finances allow – but for the time being just happy to
have got their own wheels
These are escapees (a higher proportion of women than in any
other segment) from traffic jams, parking tickets, fuel costs and
other problems of car use – who don’t care for motorcycles, but
do care for low-cost mobility
Two main dimensions of the segments were quantified: how passionate members of the segment are
about riding and how important performance in terms of the bike and the rider are. The relationships
between performance and passion are shown in the diagram below for the segments.
The questionnaire was designed to collect the information required to apply TRL’s model of accident
liability in order to project the likely accident propensity of each segment…. Some clear patterns are
apparent in the summary figures.




On either measure (accidents-per-year or accidents-per-mile), Riding Disciples and Riding
Hobbyists have a relatively low accident propensity. Both have mean accident propensity
scores significantly lower than the overall mean.
Performance Disciples have a higher accident propensity, although in part this is because of a
higher annual mileage.
At the other end of the spectrum, Car Aspirants and Look-at-me Enthusiasts have the highest
accident propensity on either measure. Both have mean accident propensity scores
significantly higher than the overall mean.
While not as risky, Car Rejecters and Performance Hobbyists also have somewhat higher
accident propensities – although lower annual mileages mean they may not have accidents as
often as Performance Disciples.37
Page | 49
FIGURE 283 - COMPLETE SEVEN SEGMENT STRUCTURE38
Respondents were asked a range of questions which were designed to understand their perceptions
of risk. The research found that Riding Disciples’ approach to motorcycling involves active
management of risk whereas Riding Hobbyists take personal responsibility for avoiding risk. The two
approaches are similar but differ in that Riding Hobbyists try to avoid risk altogether by limiting
exposure to the most risky situations (long rides; in a rush or after work; with a minor fault on the
bike; after drinking strong coffee to wake up; or only wearing t-shirt, shorts and trainers) whereas
Riding Disciples ensure that they have the correct safety gear to manage risks.39 “In contrast with the
previous segments, Performance Disciples exhibit what might be called a precautionary fatalism about
the risk of accident in pursuit of high performance.”40This segment are willing to live with the risks
involved in motorcycling and this is not due to over-confidence as they are less likely than average to
rate motorcycling as very or quite safe and are more likely to rate themselves as very or quite risky.
Their response to risk is to wear safety gear, especially body armour, and improve their skills as riders
through advanced rider training.41 Performance Hobbyists have a less clear attitude towards risk than
the other segments – there is some indication of an acceptance as risk as part of riding and that
“performance is a means to pleasure, and the thrill created by risk is an element of that pleasure.”42Car
Aspirants appear to have given the risk of riding less thought than some of the other segments.
A very limited amount of information and engagement seems to make Car Aspirants significantly more
risk-conscious that they were before…. The combination of a low ‘resting awareness of risk’ with a
Page | 50
tendency to take risk seriously when they do become aware may explain some otherwise puzzling
patterns in Car Aspirants’ reported behaviour. On the one hand, they appear to more likely than
average to consider riding in jeans and T-shirt… On the other hand, they are significantly more likely
than average to say they would definitely not ride after dropping their helmet on a hard surface (43%
against 31% of total sample). It would seem that messages about the risks attached to a dropped
helmet have reached this audience more effectively than messages about safety gear. On balance, the
attitudes of Car Aspirants to risk may be described as low awareness but high educability.43
As the young riders on motorcycles with engines up to 125cc who are involved in collisions in Suffolk
are likely to be Car Aspirants, this finding is important. It would suggest the moped days organised in
Suffolk should be successful in raising awareness of the importance of safety gear and training for this
segment.
Car Rejecters have a high awareness and dislike of the risks attached to motorcycling. They are more
likely than average to rate themselves as risky and are always thinking about the risk. Conversely,
Look-at-me Enthusiasts exhibit blasé confidence towards risk.
They are significantly less likely than the average to rate motorcycling in general as very or quite risky
– but significantly more likely than the average to rate themselves personally as very or quite safe….
To the extent that they do acknowledge risk, they seem to have an even stronger attraction to the thrill
elements than Performance Hobbyists, being significantly more in agreement than riders in general
with “Life without risk would be boring.44
More detailed profiles of the segments are included in Appendix 1. They provide an insight into the
types of motorcyclist who may be riding in Suffolk and can be used, alongside the personas created
for the STATS19 and Mosaic data, to create targeted interventions. It appears that ‘Jack’ is a Car
Rejecter and awareness raising would be of benefit to him. ‘Paul’, as a Riding Hobbyist, seeks to
mitigate risk by avoiding risky situations and wearing protective equipment. As a returning
motorcyclist, targeting the benefits of training to him via an assessment such as BikeSafe, might help
to improve his skills. ‘James’ appears to fit with the characteristics of Performance Hobbyists. Whilst
he is most likely to receive training, he admits to over-estimating his abilities and may see risk as part
of the riding experience. Awareness raising might be of benefit. Lastly, ‘Dave’ seems to most closely
fit with the Car Rejecter and sees riding as risky and dislikes that risk. He has poor knowledge of helmet
safety standards and wears less protective clothing than other types of rider.
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EXISTING SCHEMES
The following are examples of existing high profile or long running schemes and interventions,
designed to address motorcycle issues.
The first group refer to smaller motorcycle schemes:
BARE BONES - www.bare-bones.org
The Bare Bones scheme operates in Derbyshire, Leicestershire and Nottinghamshire and is aimed
at motorcycle riders aged 16 to 19 years old. It provides road safety messages about wearing
protective clothing and riding defensively, delivered by a website and through CBT trainers
promoting these messages and providing an information pack. The project has been operating in
Nottinghamshire since 2004 and distributes about 1000 packs a year to CBT trainees in the
Nottinghamshire area.
CAMBRIDGE MOPED CHECKS http://www.cambridgeshire.gov.uk/CMSWebsite/Apps/News/Details.aspx?ref=578
Cambridgeshire County Council joined forces with Cambridgeshire Police and Cambridge Regional
College to undertake safety checks of students’ mopeds. Twenty mopeds and scooters checked and
90% had faults ranging from illegal tyres, no mirrors, no eye protection for the riders and
modifications which enabled the bikes to exceed the speed limit imposed by the rider’s licence.
Riders were advised of the penalties they would face if the defects were not rectified and they were
stopped again. The penalties included fines, penalty points and the risk of bikes being seized and
crushed. The faults were to be put right during a drop-in workshop organised by the college as the
aim of the session was to educate the riders and give them the opportunity to rectify the faults,
rather than face prosecution.
KEEP YOUR WHEELS - http://www.keepyourwheels.com
Keep Your Wheels is a Bristol-based project which “promotes an exchange, whereby after 12
months of meeting certain behavioural criteria (like submitting paperwork and completing
questionnaires) and avoiding transgressions, [young riders] are rewarded with a cash incentive of
£100. It has recently been developed to include more face-to-face elements like Karting sessions
and observed rides, to link to social networking sites like Facebook and Twitter and to offer
equipment discounts from popular retailers.” 45
PED SAFE - http://www.think.norfolk.gov.uk/motorcyclists/links/moped-links
Norfolk County Council has developed a presentation to be delivered to Year 11 students in schools,
called ‘Ped Safe’. It draws on the Bare Bones project and concentrates on the incorrect wearing of
crash helmets, inadequate clothing and the situations riders face on the road. It also provides advice
on the implications of de-restricting mopeds, both legal and financial.
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The next group of schemes refer to interventions aimed at the whole motorcycling community or
those riding motorcycles with engines over 500cc:
MOTORCYCLING MATTERS - http://www.motorcyclingmatters.org/
The Motorcycling Matters website provides a ‘one-stop-shop’ for motorcyclists in Bedfordshire. It
contains information on training; events (such as the Winter Skills and Ride Free annual events);
access to a free magazine; and a forum with over 100 registered members discussing all aspects of
motorcycling. The coherent and established brand overarches motorcycling activities in
Bedfordshire, providing links between the forum discussions, the advice pages and the large scale
events. The events reflect the collision data with spring events aimed at the leisure rider and winter
skills for those commuting. Whilst the events have road safety messages at the core, stunt riders
and motorcycle club stands attract large scale audiences to disseminate information to as many
people as possible.
SAFER RIDER - http://www.saferrider.org/
“Based on the impact created through a viral video that has achieved a remarkable 5 million views
online, the Safer Rider team have established an ongoing dialogue with hundreds of members of
the motorcycling community promoting key messages about undertaking post-test training
alongside advice on a range of motorcycling issues. Analysing the demographic profile of Safer Rider
Facebook users and mapping this over the original socio-demographic data for the highest risk
riders demonstrates that the campaign has been extremely effective in hitting the precise group
that is set out to target. Among the campaign’s strengths is its use of expertise such as highly
respected police motorcycle riders to discuss safety issues and professional mechanics to offer
advice on critical bike maintenance. The level of engagement has also impressed the motorcycle
industry, with more than a dozen leading manufacturers of parts and accessories providing
equipment for product tests and competition prizes. With continued support from police and local
authorities, Safer Rider is utilising blogs, tweets, videos, posts and competitions to maximise the
effect of social media channels to promote rider safety with excellent effect.”46
SOMEONE’S SON - http://www.someones-son.co.uk/
The Someone’s Son campaign is a collaboration between the road safety partnerships of West
Yorkshire, South Yorkshire and Humberside and aims to humanise and personalise motorcyclists
and improve the motorcyclist/driver relationship. Based on the inattentional blindness research
and analysis of local collision data, the campaign seeks to encourage drivers to look out for
motorcyclists, especially at junctions and roundabouts. It also asks riders to give drivers a chance to
see them by selecting good road positioning, appropriate speed and wearing high visibility gear.
The campaign regularly features on radio, targeting the main times when crashes occurred. The
bespoke website had been visited by 2,500 people (as at mid-2012), over 1,000 high visibility vests
were distributed and 32,000 riders received information leaflets via a popular biking magazine. The
campaign won a Prince Michael International Road Safety Award in 2012.
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RIDE - http://www.driver-improvement.co.uk/index.php/home/ride
The Rider Intervention Developing Experience Course (RIDE) has developed from the National
Driver Improvement Scheme and was launched in September 2007. The RIDE Scheme has been
designed as an intervention for those motorcyclists whose behaviour has brought them to the
attention of the Police. “This scheme is designed to address the behaviour of those motorcyclists
whose riding could be described as thrill or sensation seeking and also those who by the very nature
of their riding could be defined as anti-social or careless, thereby attracting a criminal prosecution.
It can also be used for those motorcyclists who have demonstrated careless riding leading to a
collision… The RIDE course invites offenders to question their own assumptions about their ability
to ride a motorcycle and to alert them to the vulnerability that reckless, careless or anti-social riding
can attract. The aim of the course is to prevent riders from re-offending or worse, becoming a
casualty.47
An in-depth evaluation of RIDE scheme attendees from 2008 to 2010 found that “riders who
received an invitation to a RIDE course or a ‘hearts and minds chat’ with a police officer are more
ready to change their riding to become safer and more responsible than those who received a fixed
penalty…. After the course clients are more ready to change their riding to become safer and more
responsible. In contrast, the Control group regress and become less open to change. Clients have a
very positive response to the course, and believe that it gives them a better understanding of
hazards on the roads and that it will help them ride more safely, and they intend to do so in the
future.”48
ENHANCED RIDER SCHEME - https://www.gov.uk/enhanced-rider-scheme/overview
The Enhanced Rider Scheme (ERS) is a Government-supported training scheme which checks a
rider’s skills and provides training to help them improve. Discounts on motorcycle insurance are
offered on completion of the scheme. It is aimed at fully licensed motorcyclists who have just
passed their test; returning to riding after a break; upgrading to a more powerful motorcycle; or for
those who want to check their riding standard. It involves a rider assessment with an expert trainer
in different road and traffic conditions and the scheme is available through local providers. Suffolk’s
Rider Plus Scheme is part of Enhanced Rider.
A qualitative study which explored the marketing of ERS with motorcyclists found that the
respondents didn’t go looking for training and would need to be encouraged to attend. There are
stereotypical images of the types of motorcyclist who undertakes training, which could be
addressed in marketing. The research found that the initial assessment should be free and that
insurance discounts do not motivate the majority of riders (as they have low insurance premiums).
Changes to this aspect of the scheme would have to come from central Government. The DSA
branding was found to be a positive trigger and should be maximised. The report provided a number
of recommendations to improving the marketing of ERS, both in terms of message and
communication channels.49
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APPENDIX A – TRL SEGMENT PROFILES 50
RIDING HOBBYISTS (Segment 1) – Size: 14.5%
Riding Motivations:
Primarily defined by low importance attached to Self Sufficiency (in particular), Power of Bike and
Challenge of Riding
Marginally higher importance attached to Belonging and Sensations
Significantly higher than mean on:
Also high, though not at 95% significance level
Social interaction
on:
Feeling at one with the machine
Weather/scenery
Belonging and camaraderie
Wind rushing past
Looking good
Heritage/tradition
Passion: Average
54% very passionate, compared to all rider
average of 57%
Commitment: Average
78% (77% average) will definitely ride in 3 years
Male and older (peaks in 45+ age range)
63% aged 45+ (compared to an average for all
riders of 43%)
Married and living together, with children
80% married/living together, 57% with children
(averages 60%, 48%)
Better off: high incidence of company directors 19% & 12% respectively (averages 10% & 7%)
and retired
Touring bikes, bigger than 700cc. Segment with Touring (29% vs 12% average). 53%>700cc, 23%
biggest proportion of bikes .1000cc. Classic bikes >1000cc (averages 36%, 16%). 12% Classic
also prominent.
(average 6%)
Segment that owns most bikes and rides the 41% own more than one bike, & for this 41% the
oldest bikes
average number is 2.75 (averages 34% & 2.40).
Average age of most often ridden bike is 1.75
years (verses 7.99 yrs average)
Most likely to wear full face helmet but Full face – 70% (74%); Open face – 17% (12%).
significant proportion of open face wearers. 34% have a helmet three years old or older
Helmets are older than average.
(average 28%)
Wear leather, but without armour
70% wear a leather jacket and 68% leather
trousers (average 53%, 49%)
2nd longest riding career & almost half have had 63% have been riding for 10+ years (average
a break
50%) & 44% taken a break (average 40%)
Low mileage riders, primarily for summer leisure 69% <4Kp.a. (53%), 48% summer only (34%),
& fun on rural roads
39% rural roads (21%)
Highest % car drivers driving high mileage
90% drive (ave 72%) & 53% driver >10Kp.a (ave
37%)
High percentage of full licence holders but low 96% have a full licence (81%), 34% had no
levels of training
training at all (25%)
Average assessment of general & personal risk Mean scores (7= very safe); 3.65 (3.66) and 4.43
of motorcycling
(4.48)
Lowest level of collision propensity (TRL 35% in lowest quartile (21% average)
measure)
Lowest experience of risky situations
37% none in last 3 years (28% average)
Page | 55
Lowest on “constantly thinking” about risk of
riding
Values brand/make & classic style (open ended
response) in choosing bike
More likely to buy new bike, but if second-hand
to buy privately
Least likely to continue to wear helmet if
dropped on hard surface
Most likely to have bought helmet from
specialist store
Wear helmet every time they ride
Helmet choice is based on brand/reputation of
manufacture
Highest awareness & compliance with SHARP
rating
Purchase gear from specialist outlets
Brand/reputation primary factor in safety gear
choice
Below average wearing of high visibility clothing
34% (average 39%)
21% & 9% respectively (average 14% & 3%)
39% new: 11% privately if second-hand
(averages 35% and 5%)
14% (average 17%)
92% (average 86%)
99% (average 97%)
67% great importance to brand (average 60%)
26% & 10% (average 22% & 6%)
92% (average 91%)
54% great importance to brand (average 55%)
30% wear & of these 50% every time they ride
(average 39% & 48%)
Less likely than average to ride when fatigued, in 40% (ave 24%) would definitely not ride after
a rush or in poor visibility
bad night’s sleep, 33% (13%) after a hard day’s
work, 21% (11%) after riding for more than 2
hours, 25% (11%) when in a rush & 46% (17%) in
bad weather
Page | 56
PERFORMANCE DISCIPLES (Segment 2) – Size: 8.3%
Riding Motivations:
High on Power of Bike and quite high on Belonging. Low on Feeling/Sensations and Showing Off
Significantly higher than mean on:
Get places quicker
Exhilaration
Going really fast
Power vs car
Acceleration
Achievement
Getting away faster than cars
Balancing bike
Test self & abilities
Feeling at one with machine
Belonging and camaraderie
High on passion (second only to Segment 5)
65% very passionate, (compared to 76%
Segment 5 and 57% average)
Very high on commitment
90% (77% average) will definitely ride in 3 years
Most masculine segment, mid-life (peaks in 25- 95% male (average 88%), 51% aged 25-44
44, low in 45+)
(average 40%)
Higher than average in terms of marriage and 65% married/living together, 55% with children
having children
(averages 60%, 48%)
Average on all other demographics
Rides Sports Bike, large bikes 750cc and above
69% ride Sports Bike (average 40%), 47% >750cc
(average 32%)
Average number of bikes owned
35% have other bikes in the house hold (average
34%)
Full – face helmets of average age
90% (average 74%) of mean age: 2.82 years
(average 2.28 years)
Wear armour, lots of textile clothing
70% wear armoured jackets and 55% armoured
trousers (average 51%, 36%)
Longer than average riding career, second most 71% have been riding for 5+ years (average 64%)
likely to had a break
& 48% taken a break (average 40%)
Highest mileage riders, riding all year round for 65% >4Kp.a. (47%), Only 30% summer only
business & pleasure
(34%)
High % of car drivers driving the most miles
80% drive (ave 72%) driving on average 14K
miles p.a. (average 11K)
Full licence holders most likely to have/consider 84% have a full licence (81%), 13% (85) have
advanced training
taken advanced training & 18% (12%) are
considering it
Segment rating riding risk highest – both 40% (32%) consider riding very/quite risky
personally and generally
generally & 27% (19%) very/quite risky for them
personally
Moderately high risk on Accident Liability (TRL 63% (52%) levels 2 or 3 collision propensity
measure)
Across board, higher declared incidence of risky 37% (25%) have ridden when tired, 12% (65)
events, including riding when tired
when too tired to ride safely & 5% (2%) have
been involved in a collision due to tiredness
Most likely to attribute riding fatalities to lack of 8% (4%)
training
Page | 57
Highest on “I can live with the risk”
On open question, when choosing bike, go for
speed, performance & size of bike (comfort not
an issue compared to other segments)
On closed question: go for acceleration &
power: not interested at all in fuel consumption
72% (58%) can live with risk of riding
18% (6%) choose on speed, 13% (7%) on
performance & 6% (2%) on size – only 14% (20%)
choose on the basis of comfort. 58% (45%) place
great importance on power, 63% (41%) on
acceleration. Only 23% (39%) look at fuel
consumption
Less likely than average to continue to wear 14% (average 17%)
helmet if dropped on hard surface
Average source of purchase of helmet
86% (average 86%) from specialist dealer
Wear helmet every time they ride
98% (average 97%)
Average for factors in choosing helmets except All scores close to average, Noise reduction 8%
noise reduction
(3%)
Highest awareness of all helmet standards 55% (43%)
particularly ACU Gold Sticker
Distinguished by wearing armoured jackets, 70% (51%), 55% (36%), 46% (32%), 44% (30%)
trousers, gloves and boots. But not worn on plus back plate 49% (32%). 77% (77%) wear on
every occasion
every occasion
Purchase gear from specialist outlets
94% (average 91%)
Average for all factors when choosing clothing
All scores close to average
Lower than average wearing of high visibility 30% wear & of these 44% every time they ride
clothing
(average 39% & 48%)
Most likely segment to ride in a rush, when tired Above average for riding more than two house
or after a long distance
(3.87: 3.48), riding after a bad night’s sleep
(3.13: 2.78), riding at the end of hard day’s work
(3.82: 3.51), riding when in a rush (4.02: 3.58),
after seeing a collision (3.92: 3.57), at night
(4.22: 3.91), after traveling a long distance (4.13:
3.70)
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PERFORMANCE HOBBYISTS (Segment 3) – Size: 14.7%
Riding Motivations:
High on Feelings/Sensations and quite high on
Showing Off
Significantly higher than mean on:
Not rely on others
Get places quicker
Exhilaration
Going really fast
Power vs car
Acceleration
Getting away faster than cars
Balancing bike
Sounds/smells
Noise/vibration
Weather/scenery
Wind rushing past
Average passion about riding
Average expectation about riding in 3 years’
time
Male, mid aged (25-44)
Power of Bike. Low on Belonging, quite low on
85% very/quite passionate (average 88%)
73% (77% average) will definitely ride in 3 years
90% male (average 88%), 47% aged 25-44
(average 40%)
Average marital status & presence of children
55% married/living together, 46% with children
(averages 60%, 48%)
Likely to be living in London/SE & most ABC1
30% (25%) & 65% (60%)
Sports bikes with engine capacity of 500-700cc
45% own Sports Bike (average 40%), 30% 500700cc (21%)
Unlikely to own another bike
33% own more than one (34%)
Full face helmet less than 3 years old
80% wear a full face helmet (74%), on average
2.16 years old (average 2.28 years)
Above average presence of armour – particularly 57% wear armoured jackets (average 51%)
in jacket
Average length of riding career & incidence of 60% have been riding for 5+ years (average 64%)
break
& 39% taken a break (average 40%)
Summer only for commuting and pleasure
42% only ride in summer (34%), 22% for
commuting (14%)
Average car drivers
73% drive (72%), around 10K miles p.a.
Full licence holders, significantly more likely to 83% have a full licence (81%), 76% have
receive training than Riding Hobbyists (the other undertaken some form of training (69%)
summer-only segment)
Marginally likely to view risk higher for self and Self: 4.34 vs 4.48. General: 3.55 v 3.66 (where
motorcyclists generally
Very Safe = 7)
Segment contains both high and low collision 27% in Level 1 (21%) and 29% in Level 2 (25%)
propensity groups
Most likely to admit having overestimated their 24% have overestimated their abilities (16%).
abilities, taken a risk to impress others & ridden 12% taken a risk to impress (7%), and 33% ridden
when tired
when tired (25%)
Most likely to respond “Life without risk would 71% (65%)
be boring” to fatality statistics
Page | 59
Most significant factor in bike purchase is
acceleration
Segment most likely to buy second hand bike
More likely than anyone to continue wearing
after dropping helmet
Purchase helmet from specialist motorcycle
shops
Wear helmet every time they ride
Least likely to consider brand/reputation of
helmet in purchase
Average awareness of helmet safety standards
Average wearing of protective clothing &
wardrobe worn
Average source of purchase for clothing
50% place great importance (average 41%)
74% (65%)
19% (average 17%)
89% (average 86%) from specialist dealer
97% (average 97%)
No importance: 14% (9%)
96% wear protective clothing (94%) & 78% wear
every time (77%)
91% (average 91%) from specialist motorcycle
shop
Average for all factors when choosing clothing
Low incidence of wearing high visibility clothing 36% (39%) of whom only 32% wear on every trip
and lowest wearing it
(48%)
Most likely (with Segment 2) to carry on riding 60% claim it would have little/no impact
having seen serious collision involving
motorcyclist
Page | 60
LOOK-AT-ME ENTHUSIASTS (Segment 4) – Size: 24%
Riding Motivations:
Very high on Showing Off. Quite high on all other factors except Relationship with Bike and Release
Significantly higher than mean on:
28 of the 30 statements
Significantly higher than ALL 6 other segments
on:
Never knowing what will happen next
Demonstrate skills
Pitting self against others
Looking good
Passionate about riding
93% very/quite passionate (average 88%)
BUT only average about commitment
77% (77% average) will definitely ride in 3 years
Averagely male but significantly young <25 years 28% aged under 24 (17%)
old
Single and living with parents
31% (19%)
High incidence of students and skew towards 11% students (6%) and 29% living in SE/London
living in SE/London (similar to Cluster 6)
(25%)
Surprisingly average across engine size and bike Average by type and size of bike
types
Low level of multiple bike ownership in 29% (34%)
household
Variety of helmets worn, mostly full face
74% wear a full face helmet (74%)
Indistinguishable by protective clothing worn
Average
Highest group of novices (except Segment 6)
30% have been riding for less than 2 years (21%)
Ride all year round for everything
75% (66%) ride all year round, 17% (12%) as part
of job. 56% (44%) for commuting, 82% (72%) for
leisure and 82% (71%) for fun
Among those who have car licence, less likely to 25% (18%) do not have car licence. Of those that
drive regularly
do, 17% (11%) do not drive regularly
High on provisional licence holders only and 25% have a provisional licence (19%) and 87%
highest on intention to get full licence. Most intend to obtain a full licence (81%). Only 18%
likely to have received some form of training
have not been trained (25%)
Rate motorcycling, generally and personally, Mean scores (7= very safe): General 3.90 (3.66).
safer than anyone else
Personal 4.66 (4.48)
Highest collision propensity, according to TRL 38% in top quartile (average 25%)
measure
Highest admittance of experiencing risky 79% (72%) have experienced at least one
situations but none specifically
No specific reason to explain motorcycle 44% (41%)
fatalities except other road users
Most likely to agree with “I am a good rider so 33% (24%) agree with this statement
the risk does not apply to me”
Most important factors in bike choice are looks: Of great importance: Looks 73% (61%),
acceleration, power and sound
Acceleration 60% (41%), Power 58% (45%) and
Sound 50% (38%)
Likely to purchase bike like everyone else, 66% second hand (65%), more likely from dealer
second hand rather than new
40% (38%) or small ad/EBay 31% (29%)
Average response to dropping helmet
18% would continue to wear (average 17%)
Page | 61
Purchased current helmet from specialist
motorcycle shops
Wear helmet every time they ride
Like bikes, looks are very important in choice of
helmet. Only comfort is more important
Average awareness of helmet safety standards
and compliance of own helmet
Very marginally less likely to wear protective
clothing or wear it all the time. Most likely to
entertain riding in T shirt and trainers
Lowest incidence, albeit marginal, of buying
from specialist outlets
Really, really care about how gear looks. Above
or average on all other aspects of clothing,
including safety/protection certification
Average wearing and frequency of wearing high
visibility clothing
More likely to ride on all occasions than the
average. The main exception is after “having
seen a serious accident involving a motorcyclist”
Page | 62
84% (average 84%); average age of helmet: 2.0
years (2.28)
96% (average 97%)
21% (14%) choose looks as the main factor in
choice. Comfort 27% (30%)
92% (94%) wear protective clothing and 75%
(77%) wear on every trip. 66% would definitely
not ride in T shirt and trainers (average 76%)
88% (average 91%). 13% of purchase made by
mail order/online (11% average)
51% (36%) state looks are of great importance.
84% (80%) give safety great importance
39% (39%) wear and those that do, 48% wear
every time (48%)
Significantly: After too many drinks; At end of
hard day’s work; Minor fault with bike
RIDING DISCIPLES (Segment 5) – Size: 16.3%
Riding Motivations:
High on Belonging, Sensations, Self-sufficiency, Release. Highest segment on Relationship with Bike.
Low on Showing off.
Significantly higher than mean on:
Not rely on others
Get places quicker
Not bother with others
Getting away
Exhilaration
Achievement
Balancing bike
Sounds/smells
Noise/vibration
Weather/scenery
Wind rushing past
High awareness
Relaxed
Understand bike
Social interaction
Riding = identity
Feeling at one with the machine
Heritage/tradition
Belonging and camaraderie
Sky high on passion!
76% very passionate (average 57%)
Also high on commitment
90% will be riding in 3 years’ time (average 77%)
Most male group, and older (peaks in 45+ age 93% male (88%); 60% aged 45+ (43%); 17%
group; high on retired)
retired (7%)
Married and living together (but less likely than 64% married (60%), 50% with children (48%)
Segment 1 to have children)
Most likely to be C2DE
45% (38%)
Riding large bikes > 1000cc or Classic and >1000cc 21% (16%), Classic 11% (6%), Custom
Custom bikes
9% (6%)
Highest multiple ownership but largest 50% own more than one bike (34%). On average
collections
2.38 (2.40)
Majority wear full face but highest incidence of Full face 72% (74%), Open face 18% (12%) Equal
open face helmets
highest average age of helmet 2.81 years old
(2.28)
Like Segment 1 wear leather but armoured like Leather jacket 63% (53%) Armoured trousers
Segment 2
45% (36%)
Riders who have ridden longest and most likely 73% (50%) have ridden for more than 10 years.
to have taken a break
55% (40%) have taken a break in their riding
career
All year round riders primarily for leisure and All year round leisure 78% (72%): fun 77% (71%);
fun; on both urban and rural roads
on both urban and rural roads 72% (64%)
Heavy car drivers like Segments 1 & 3
46% drive 10K+p.a. (37%)
nd
2 highest proportion of full licence owners but 93% (81%) have a full licence, 37% (28%) have
least likely to have received any training or seek received no training whatsoever and 44% (31%)
it in the future
do not intend to get it in the future
Page | 63
Average attitude to risk generally and personally
but a minority who consider themselves very
safe
Spread of collision propensity (according to TRL
measure) but skew towards lower collision risk
Below average experience of all risk events
except being fined for speeding
Strong views that “protective clothing will
reduce risk”, “primary purpose in riding is to
arrive safely”, “constantly thinking about risk
when riding”
Primary factors in bike choice are comfort,
manoeuvrability & manufacturer/brand
Similar to all bikers, more likely to buy secondhand from a dealer
Segment least likely to ride if they had dropped
helmet on a hard surface
More likely than anyone to mention
spontaneously comfort & good fit as main
reason for selecting a helmet
Average awareness of safety standards and
certification of their own helmet
Distinguished by wearing leather & armoured
gear, similar to Segment 1 on the former &
Segment 2 on the later. Like all bikers, will not
necessarily wear every time they ride
Comfort is of greatest importance in choice of
gear
Most likely to own high visibility clothing – but
only wear it when conditions require
Page | 64
17% very safe personally (12%) – but spread of
attitudes
61% (54%) in lowest two quartiles
9% have been fined for speeding (6%)
Strongly agreeing to: “protective clothing” 50%
(40%); “arrive safely” 86% (75%); “constantly
thinking about risk” 47% (39%) BUT strongly
disagreeing to “giving up” 36% (26%)
Considered of great importance by 79% (72%0;
71% (63%) & 60% (54%) respectively
Bought second-hand 64% (65%) from dealer
38% (38%)
44% (31%) would definitely not ride
37% (31%) and 26% (21%) respectively
Example: Leather trousers 58% (49%); armoured
trousers 45% (36%).
75% (77%) would wear every time they rode
96% (92%)
45% (39%) wear high visibility clothing but only
when conditions require 37% (31%)
CAR ASPIRANTS (Segment 6) – Size: 11.2%
Riding Motivations:
High on Challenge of riding, Self-sufficiency (especially “saving on fuel and parking”), and marginally
on Relationship with bike. Low on everything else, especially Release
Significantly higher than mean on:
Fuel/parking saving
Not rely on others
Passionless!
27% indifferent or without passion (13%)
Least committed to motorcycling
Only 59% will definitely by riding in three years’
time (77%)
Male and youngest riders
Male 88% (88%); 29% (17%) aged under 24
Single living with parents
31% (19%)
Most likely to be students, living in London/SE
Students 14% (6%), living in London/SE 33%
(25%)
Ride scooters & mopeds so biased towards 20% (11%) scooters; 18% (7%) mopeds; 23%
<50cc
(9%) under 50cc
Lowest level of multiple bike ownership
21% (34%)
Higher than average on flip front helmets (and 16% (14%); average age 1.92 years (average 2.28
newest helmets)
years)
Protective clothing most likely to be textile, no Textile jacket 56% (44%)
leather, no armour
Shortest careers in motorcycling
38% (21%) less than two years
All year round commuting and low mileage
61% (44%) all year round commuting; 61% (53%)
<4000 miles p.a.
High on urban-only riding
26% (15%)
Least likely to have a full licence to drive a car
63% (79%)
Most likely to hold a provisional motorbike 36% (19%)
licence
Rate motorcycling, generally & personally, safer Mean scores (7 = Very safe): Generally 3.77
than anyone else other than Look-at-me (3.66), Personally 4.61 (4.48)
Enthusiasts
Higher risk group according to TRL collision 35% (25%) at level 4 (Top quartile)
propensity measure
Unlikely to have experienced many of the 34% (28%) experienced none
dangerous situations presented
Most likely cluster to attribute “not being seen” 29% (24%)
as main reason for motorcycle fatalities
Most likely to “consider giving up” in response Mean score (5 = strongly agree): 3.00 (2.79)
to risk statistics
Bike choice is all about running costs. 14% (7%) on the open question; 59% (39%) of
Economical/fuel economy (open ended); Fuel great importance on the closed question
consumption (closed)
Average purchasing behaviour re new/second- 64% (65%) buy second-hand & 40% (38%) do so
hand & source of purchase of bike
from a specialist dealer
nd
2 only to Cluster 5 in stating definitely would 43% (31%)
not ride if dropped helmet on hard surface
Least likely to have purchased helmet from 79% (86%); most likely to have purchased on the
specialist outlet
high street (6%) or mail order/on-line (9%)
Wear a helmet on every occasion
96% (97%)
Page | 65
Main factors in choice of helmet are comfort and In spontaneous mention, it is safety/safety
safety certification
features that is significant: 28% (20%)
Lowest awareness of safety standards
10% (6%) could not name one, despite owning
the newest helmets
Average wearing of protective clothing but 92% (94%) claim to wear protective clothing.
significant minority could not specify its features 27% (15%) could not specify the trousers & 27%
(13%) the boots. Suggesting a significant
minority only wear protective jackets & gloves
Safety certification is most important in Safety important to 86% (80%); Looks
purchase decision of protective clothing, not unimportant to 24% (14%)
looks
Most likely to ride wearing T shirt and trainers 13% (8%) would ride in a T shirt; 43% (31%)
but least likely if they dropped their helmet on a would not ride if they dropped their helmet; 21%
hard surface, going on a long journey or if they (11%) after having a strong coffee/caffeine, 13%
needed a strong coffee or caffeine drink
(8%) if they had to travel a long distance
Page | 66
CAR REJECTERS (Segment 7) – Size: 10.1%
Riding Motivations:
High on Release. Low on Power of Bike, Belonging and Feeling Sensations
Significantly higher than mean on:
Fuel/parking saving
Not rely on others
Not bother with others
Passionless
26% indifferent or without passion towards
motorcycling (13%)
Second lowest segment on commitment to Only 61% will definitely by riding in three years’
riding in three years’ time
time (77%)
This is the segment with more women than any 28% (12%) are female
other. Average age profile with slight peak at 25- 43% (40%) are aged 25-44
44
Married/living together, with children
64% (60%) married/living together, 55% (48%)
with children
Lower income – skew to under £20K p.a.; living 19% (11%) <£20K: 30% (20%) live in SW/Wales
in SW/Wales
Ride bikes under 125cc, most significantly 51% (26%) <125cc; 26% (11%) scooters; 17%
scooters and mopeds less than 4 years old
(7%) mopeds; 38% (32%) less than 4 years old
Less likely to have more than one bike in 26% (34%) own more than one bike in
household but if they do they have the highest household. Those that do have the largest
number
collection: average 2.96 (2.40)
Majority wear full face helmets but significant Full face: 65% (74%); Flip front 20% (14%)
minority wear flip front helmets. The average
age of these helmets is less than 2 years old
Textile clothing is preferred to leather and 51% (44%) wear textile jackets; 46% (37%) wear
armour is present at the lowest level of any textile trousers
segment
Not the newest riders but almost a third have 31% (21%) less than two years
only been riding for less than 2 years
Riding is all year round commuting and summer 50% (44%) all year round commuting; 31% &
leisure and fun. Very low mileage
36% (23% & 26%) summer leisure and fun. 33%
(25%) <2000 miles p.a.
High on urban-only riding
24% (15%)
Over two thirds also drive a car with an average 68% (72%) drive a car
mileage c10K p.a.
2nd highest incidence of provisional licence 33% (19%) provisional
holders. Lowest intention to get full licence. Two 58% (81%) of provisional licence holders intend
thirds have received some form of training, to gain full licence
primarily CBT
67% (69%) have received training. 57% (59%)
only CBT
Rate motorcycling in general and selves as risky 17% (10%) rate motorcycling in general as very
risky & 10% (6%) rate it very risky for themselves
Average on TRL collision propensity measure but 28% (21%) Level 1 (safe) and 28% (25%) at Level
this hides a significant proportion who are very 4 (at risk)
safe & a similar number who are very much at
risk
Not very likely to have experienced any of the 36% (28%) experienced none
riding situations presented
Page | 67
Marginally more likely to attribute motorcycle
fatalities to irresponsible riding
In response to collisions statistics, more likely to
agree with the statements: “My primary
purpose is to arrive safely” and “I would consider
giving up riding”
Primary factors in choice of bike are reliability,
comfort & fuel consumption. The latter is most
significantly different
Segment most likely to buy new bike. If secondhand less likely to go to dealer than friends or
small ads
Response to dropping helmet on hard surface is
the same as everyone else
Like Segment 6, less likely to source helmet from
a specialist shop. Worryingly a few obtained
second hand.
Almost everyone wears their helmet every time
they ride
Safety certification is the primary consideration
in helmet purchase
Low awareness of all safety standards except BS
(Kite mark) & even lower knowledge of their
own helmet’s compliance. Awareness of SHARP
is lowest in this group
The vast majority claim to wear protective
clothing but a significant minority do not wear
protective trousers or boots. The material of
choice is textile rather than leather. Armour is
present in one third of all protective clothing
worn. Lowest incidence of back armour
23% (18%)
79% (75%) strongly agree with “… arrive safely”
and 17% (12%) strongly agree with “… giving up
riding”
54% (39%) rate fuel consumption of great
importance
40% (35%) buy new. 62% (55%) of second hand
purchasers bought from friend or small ad. Only
25% (38%) bought from dealer
71% (71%) would not continue to wear it
80% (86%) purchased from specialist
7% (3%) acquired second hand
96% (97%)
98% (92%)
9% (6%) could not mention any safety standard.
17% (8%) could not remember their own
helmet’s accreditation. Only 17% (22%) were
aware of SHARP
91% (94%) claim to wear protective clothing
The most popular item is a textile jacket: 46%
(37%)
21% (15%) do not wear protective trousers or
protective boots 20% (13%). Only 23% (32%)
wear back armour.
75% (77%) wear every time they ride
Most likely to purchase clothing from specialist 89% (91%) buy from a specialist. 11% (4%) buy in
store but significant minority purchase on the the high street
high street
High importance given to safety certification of 85% (80%) great importance
clothing
Average incidence of high visibility clothing but 45% (39%) wear and 60% (48%) wear on every
those who have it are the most likely to wear trip
every time they ride
Page | 68
1
http://ec.europa.eu/transport/road_safety/specialist/knowledge/vehicle/safety_design_needs/motorcycles.h
tm
2
Lloyd, D., Reported Road Casualties in Great Britain: Main Results 2012, (Department for Transport, London,
June 2013), p. 4
3
Table TRA0101, DfT National Road Traffic Survey, (Department for Transport, London, June 2013)
4
Grove, J., Vehicle Licensing Statistics: 2012, (Department for Transport, London, April 2013), p. 2
5
Table VEH0102, Vehicle Licensing Statistics 2012, (Department for Transport, London, April 2013)
6
Table VEH0306, Vehicle Licensing Statistics 2011, (Department for Transport, London, April 2012)
7
Table TRA0101, DfT National Road Traffic Survey, (Department for Transport, London, June 2013)
8
Table VEH0105, Vehicle Licensing Statistics 2012, (Department for Transport, London, April 2013)
9
Table VEH0105, Vehicle Licensing Statistics 2012, (Department for Transport, London, April 2013)
10
www.weatheronline.co.uk/weather/maps
11
Clarke, D. D., Ward, P., Bartle, C. and Truman, W., In-depth Study of Motorcycle Accidents: Road Safety
Research Report No. 54, (Department for Transport, London, 2004)
12
ibid., p. 21
13
Association des Contructeurs Européens de Motocycles (ACEM), MAIDS In-depth investigations of accidents
involving powered two wheelers: Final Report 1.2, (ACEM, 2004, http://www.maids-study.eu)
14
2010 Names Rider Post Campaign Research Summary, (Department for Transport, London, 2010), p. 2
15
ibid., p.3
16
Clarke et al., p. 23
17
ibid., p. 24
18
ibid., p. 25
19
ibid., p. 24
20
ibid., p. 24
21
ibid., p. 24
22
Christmas, S., Young, D., Cookson, R. and Cuerden, R., Passion, performance, practicality: motorcyclists’
motivations and attitudes to safety PPR 442, (Transport Research Laboratory, Berkshire, 2009)
23
Clarke et al.
24
https://www.gov.uk/government/news/drivers-urged-to-think-biker
25
http://www.humberside.police.uk/road-safety-and-driving-awareness-campaigns/casualty-reduction-androads-policing/operation-achilles
26
http://www.suffolkroadsafe.net/suffolkroadsafe/news.php?article_id=114
27
http://www.suffolkride.net/training-young.html
28
http://www.suffolkride.net/training-advanced.html
29
http://www.suffolkroadsafe.net/suffolkroadsafe/news.php?article_id=55
30
http://www.communityactionsuffolk.org.uk/what-we-do/community-development/suffolk-wheels-2-work/
31
http://www.bikesafe.co.uk/Police-Forces/Suffolk.aspx
32
http://www.bikesafe.co.uk/Index.aspx
33
http://www.hantsfire.gov.uk/yoursafety/onholiday/ridesmart.htm
34
Christmas, S. et al.
35
ibid., p.i
36
ibid., p.ii
37
ibid., p.41
38
ibid., p. 34
39
ibid., p. 43
40
ibid., p. 44
41
ibid., p. 45
42
ibid., p.45
43
ibid., p.46
44
ibid., p.47
45
Collins, K., Warren, S., Leonard, S., Pressley, A. and Tapp, A., Keep Your Wheels: Final Report, (University of the
West of England, Bristol, 2011), p.7
46
Safer Roads, Social Media Marketing to Motorcyclists: Campaign Performance Report for Safer Rider
Campaign, (Safer Roads, Oxfordshire, 2011), p. 3
47
http://www.driver-improvement.co.uk/index.php/home/ride
Page | 69
48
Broughton, P., Burgess, C., Fylan, F. and Stradling, S., Evaluation of the National RIDE Scheme, (Leeds
Metropolitan University, Leeds, 2011), p.68
49
Enhanced Rider Scheme Marketing Vehicles: Research Report, (Step Beyond Market Research, Staffordshire,
2010)
50
Christmas et al., p. 65
Page | 70