Suffolk Motorcycle Study

Transcription

1 Suffolk Motorcycle Study Version 1.0 Tanya Fosdick September 2013

2 CONTENTS EXECUTIVE SUMMARY...3 INTRODUCTION...7 RISK PROFILE...10 COLLISION PROFILES...10 WHAT? WHEN? WHERE? HOW? MOTORCYCLE RIDER PROFILES MOSAIC ANALYSIS INDEX OF MULTIPLE DEPRIVATION (IMD) PERSONAS ENGAGEMENT PLAN TRAINING COURSES ENGAGEMENT DAYS COMMUTER RIDER SUPPORT OTHER MOTORISTS ENFORCEMENT BIKER MAGAZINE WEBSITE & APPS BRAND BLINDNESS MESSAGES CURRENT LOCAL SCHEMES Suffolk Ride Brand Suffolk Ride Motorcycle Show, Felixstowe, May Young Rider Scheme Rider Plus Scheme Moped Days Wheels To Work Scheme BikeSafe Fire Bike Project SUMMARY OF OTHER EVIDENCE AND SUCCESSFUL SCHEMES Summary Of Other Evidence Existing Schemes Appendix A TRL Segment Profiles Page 2

3 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 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 ( cc). 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

4 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 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

5 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

6 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. Page 6

7 Number of Motorcycle User Casualties Fatal Index 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 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 average, every year since 2007 has had an index lower than 100. FIGURE 1 - GB MOTORCYCLE USER CASUALTIES BY SEVERITY AND INDEXED AGAINST AVERAGE Fatal Serious Slight Fatal Index 0 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 in every thousand mopeds are involved in Page 7

8 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 350 Index of Risk by Vehicle Ownership Cars MC Mopeds 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 vehicles 8 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. Page 8

9 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

10 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 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 0 Cars Motor cycles Light goods Heavy goods Buses and coaches Index of Risk by Vehicle Ownership Other vehicles 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

11 (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 The number of collisions involving a motorcycle and resulting in slight injury reduced from 205 in 2008 to 179 in 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 Fatal Serious 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 TABLE 1 - MOTORCYCLISTS INVOLVED IN COLLISIONS IN SUFFOLK , BY SEVERITY Engine Size Fatal Serious KSI Slight Total KSI Ratio MC up to 50cc % MC cc % MC cc % MC Over 500cc % Total % Page 11

12 TABLE 2 - PERCENTAGES OF MOTORCYCLISTS BY ENGINE SIZE INVOLVED IN EACH SEVERITY OF COLLISION IN SUFFOLK, Engine Size Fatal Serious KSI Slight Total MC up to 50cc 2% 15% 14% 35% 26% MC cc 9% 26% 24% 28% 28% MC cc 26% 11% 13% 9% 10% MC Over 500cc 63% 48% 49% 28% 36% Total 100% 100% 100% 100% 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 Fatal Serious KSI Slight Total All Vehicles 69% 70% 70% 70% 70% MC up to 125cc as related vehicle 100% 96% 96% 96% 96% MC over 500cc as related vehicle 95% 97% 97% 87% 91% All Vehicles 13% 15% 15% 23% 22% MC up to 125cc as related vehicle 0% 1% 1% 1% 1% MC over 500cc as related vehicle 0% 3% 2% 12% 8% All Vehicles 18% 15% 15% 7% 8% MC up to 125cc as related vehicle 0% 4% 3% 2% 3% MC over 500cc as related vehicle 5% 1% 1% 1% 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 average. Changes to the STATS19 form in 2005 mean that data is not available for motorcycles over 500cc in Page 12

13 Number of 125cc or Under Motorcyclists KSI Index Figure 5 shows that since 2007 there has been little divergence from the 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 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 FIGURE 5 - SUFFOLK 125CC OR UNDER MOTORCYCLISTS BY SEVERITY AND INDEXED AGAINST KSI AVERAGE Fatal Serious Slight KSI Index 0 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 Both 2011 and 2012 saw the number of riders of large motorcyclists below the average. Page 13

14 Number of Over 500cc Motorcyclists KSI Index FIGURE 6 - SUFFOLK OVER 500CC MOTORCYCLISTS BY SEVERITY AND INDEXED AGAINST KSI AVERAGE Fatal Serious Slight KSI Index 0 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% 25% 8% 28% 70% 64% or or 4 5+ Page 14

15 Midnight 1AM 2AM 3AM 4AM 5AM 6AM 7AM 8AM 9AM 10AM 11AM NOON 1PM 2PM 3PM 4PM 5PM 6PM 7PM 8PM 9PM 10PM 11PM 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 Weekday 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

16 Midnight 1AM 2AM 3AM 4AM 5AM 6AM 7AM 8AM 9AM 10AM 11AM NOON 1PM 2PM 3PM 4PM 5PM 6PM 7PM 8PM 9PM 10PM 11PM FIGURE 9 - UP TO 125CC MOTORCYCLISTS INVOLVED IN COLLISIONS IN SUFFOLK BY HOUR Weekday 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 Mon Tue Wed Thu Fri Sat Sun Up to 125cc Over 500cc Page 16

17 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 FIGURE 11 - MONTH OF YEAR OF MOTORCYCLISTS OF OVER 500CC INVOLVED IN COLLISIONS Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Fatal Serious Slight FIGURE 12 - MONTH OF YEAR OF MOTORCYCLISTS OF 125CC AND UNDER INVOLVED IN COLLISIONS Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Fatal Serious Slight Page 17

18 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 Fatal Serious KSI Slight Total Fine & Windy Fog or Mist Other Wet & Still Wet & Windy Fine & Still Not Known TABLE 5 - WEATHER CONDITIONS OF MOTORCYCLISTS ON 125CC OR UNDER MACHINES INVOLVED IN INJURY COLLISIONS Weather Conditions Fatal Serious KSI Slight Total Fine & Windy Fog or Mist Other Wet & Still Wet & Windy Fine & Still Not Known 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

19 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 All Up to 125cc Over 500cc Up to 125cc Over 500cc Work 10% 5% 12% 9% Commute 12% 14% 14% 15% School Pupil 2% 0% 2% 0% School Run 1% 0% 0% 0% Other 75% 80% 71% 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

20 FIGURE 13 - SPEED LIMITS WHERE MOTORCYCLISTS WERE INVOLVED IN COLLISIONS IN SUFFOLK Up to 125cc 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. 12 The 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

21 TABLE 7 - PRIME CAUSES OF COLLISIONS INVOLVING MOTORCYCLES FROM THE MAIDS REPORT 13 Frequency Percent Human Motorcycle rider Human Other Vehicle driver Vehicle Environmental Other Failure Total FIGURE 14 - JUNCTION DETAILS OF MOTORCYCLISTS ON UP TO 125CC MACHINES Mini roundabout 1% Roundabout 12% Other 1% Slip 0% Multi-Junction 1% Crossroads 4% Private 7% No Junction 36% T Junction 38% FIGURE 15 - JUNCTION DETAILS OF MOTORCYCLISTS ON OVER 500CC MACHINES Mini roundabout 0% Roundabout 12% Other 2% Multi-Junction 0% Crossroads 1% Slip 1% Private 8% No Junction 46% T Junction 30% Page 21

22 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

23 FIGURE 16 - MSOAS WHERE COLLISIONS INVOLVING MOTORCYCLES (UP TO 125CC) OCCURRED FIGURE 17 - MSOAS WHERE COLLISIONS INVOLVING MOTORCYCLES (OVER 500CC) OCCURRED Page 23

24 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% 50% Up to 125cc Over 500cc 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

25 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 involved 21, 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

26 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) 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% 6.1% Road layout (e.g. bend, hill, narrow carriageway) Deposit on road (e.g. oil, mud, chippings) 3.8% Deposit on road (e.g. oil, mud, chippings) 4.1% 3.7% Dazzling sun 3.4% 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

27 NK 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 Up to 125cc 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

28 NK NK FIGURE 20 - AGE OF RIDERS FROM SUFFOLK ON 125CC OR BELOW MOTORCYCLES BY SEVERITY Fatal Serious Slight FIGURE 21 - AGE OF RIDERS FROM SUFFOLK ON OVER 500CC MOTORCYCLES BY SEVERITY Fatal Serious 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

29 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 Fatal Serious KSI Slight Total Ipswich Waveney Suffolk Coastal Bury St Edmunds Mid Suffolk Babergh Forest Heath Great Yarmouth East Cambridgeshire Colchester TABLE 10 - NUMBER OF SUFFOLK RIDERS OF OVER 500C MOTORCYCLES INVOLVED IN COLLISIONS IN THESE DISTRICTS Local Authority District Fatal Serious KSI Slight Total Ipswich Suffolk Coastal Babergh Bury St Edmunds Waveney Mid Suffolk Forest Heath South Norfolk Great Yarmouth Braintree 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

30 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 Essex County 81 GREAT BRITAIN 100 Plymouth City Newport City Somerset County 49 Devon County 52 Gloucestershire County 52 Wiltshire (from 2009) 55 Cambridgeshire County 77 SUFFOLK & NEIGHBOURS 75 SUFFOLK 75 Worcestershire County Norfolk County Shropshire (from 2009) 59 Cumbria County 62 North Yorkshire County 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

31 Riders/100,000 population FIGURE 23 - AVERAGE ANNUAL MOTORCYCLISTS INVOLVED IN COLLISIONS PER HEAD OF POPULATION BASED ON RESIDENCE ( ) 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

32 FIGURE 24 - HOME LOCATION OF SUFFOLK MOTORCYCLE RIDERS (UP TO 125CC) FIGURE 25 - HOME LOCATION OF SUFFOLK MOTORCYCLE RIDERS (OVER 500CC) Page 32

33 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 KSI Collision Involvement All Collision Involvement Up to 125cc Over 500cc Up to 125cc Over 500cc Rural 26% 25% 24% 25% Town 20% 19% 16% 17% Urban 54% 56% 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 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 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

34 Number of Riders Index Value 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 ( ) Riders Index B K J E A D I H O F L M G N C MOSAIC TYPE 0 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

35 Number of Riders Index Value FIGURE 27 - RIDERS OF OVER 500CC MOTORCYCLES FROM SUFFOLK INVOLVED IN COLLISIONS IN GB, GROUPED BY MOSAIC GROUP ( ) Riders Index 20 0 B E A J D K F I H L O N M G C MOSAIC TYPE 0 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. Page 35

36 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

37 TABLE 13 CHARACTERISTICS OF MOSAIC GROUPS OVER-REPRESENTED AMONGST RIDERS OF OVER 500CC MOTORCYCLES Group E Group F Group I Aged years old Aged 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 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

38 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

39 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 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. Couples with young children in comfortable modern housing 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. Lower income workers in urban terraces in often diverse areas 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. Residents with sufficient incomes in right-to-buy council houses 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. Families in low-rise council housing with high levels of benefit need 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

40 FIGURE 31 - SUFFOLK RESIDENT RIDERS INVOLVED IN INJURY COLLISIONS ON 125CC OR UNDER MOTORCYCLES BY IMD 6% 5% 6% 8% LeastDeprived10% 10% LessDeprived20% LessDeprived30% 4% 16% LessDeprived40% LessDeprived50% MoreDeprived50% MoreDeprived40% 16% MoreDeprived30% 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

41 FIGURE 32 - SUFFOLK RESIDENT RIDERS INVOLVED IN INJURY COLLISIONS ON OVER 500CC MOTORCYCLES BY IMD 4% 3% 9% 5% 13% 9% 12% 16% 14% 15% LeastDeprived10% LessDeprived20% LessDeprived30% LessDeprived40% LessDeprived50% MoreDeprived50% MoreDeprived40% MoreDeprived30% MoreDeprived20% MostDeprived10% 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 segmentation 22 of motorcyclists discussed later in the document and the DfT in-depth analysis of motorcycle collisions 23, 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

42 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 cc. Dave s collisions are more like Jack s: they tend to occur on weekdays at commuter times, especially between 4 and 6pm. Page 42

43 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

44 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 campaign 24 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: Page 44

45 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 KEY THEME Enjoy motorcycling safely. Riding skills and the right equipment are an important feature of modern biking. MEDIA MESSAGES 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. FACTS WHEN? 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. Page 45

46 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 MEASURES 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

47 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 ( 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 Page 47

48 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. Page 48

49 TABLE 15 - TRL SEGMENTATION OF MOTORCYCLISTS 36 No. Segment Description 1 Riding hobbyists 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 2 Performance disciples 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 3 Performance hobbyists 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 4 Look-at-me enthusiasts 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 5 Riding disciples 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 6 Car aspirants 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 7 Car rejecters 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

50 FIGURE COMPLETE SEVEN SEGMENT STRUCTURE 38 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. 40 This 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. 42 Car 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