diss

4595254 Philip Hines
1
Cycling Road Accident Casualties in Great Britain,
1985-2010.
Author: Philip Hines
Abstract
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Cycling as a means of transport is risky, and the UK has seen a decline in
participation over the last century with most trips being undertaken by cars. Yet
increasing participation is a policy objective within health, environment and
transportation fields. This study investigated the risk of cycling by looking at the
relationship of key risk factors on cycle casualties in the UK over the period 1985-
2010. It used the UK police STATS19 database of road accidents to construct
regression models for: the mean age of casualties, car mileage and HGV mileage,
against casualty rates over the period. The study found: an absolute and exposure
adjusted reduction in cycle casualties over the period, an increase in the mean age
of the casualty, cars acted as a better predictor than HGVs for cycling casualties.
The results add to the evidence of increasing road safety for cyclists in the UK.

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Contents
1.Introduction…………………………………………………………………………... 3
1.1 Risk Factors………………………………………………………………... 4
1.2 Objectives and Hypotheses……………………………………………... 7
2. Method………………………………………………………………………………… 8
2.1 Model Construction……………………………………………………….. 9
2.2 Model Accuracy…………………………………………………………..... 10
3. Results…………………………………………………………………………..…… 11
3.1 Descriptive Trends……………………………………………………….. 11
3.2 Multiple Linear Regression Models…………………………….……... 16
4. Discussion…………………………………………………………………………... 22
4.1 Risk Factors……………………………………………………………….. 22
4.2 Limitations and Strengths………………………………………………. 23
4.3 Conclusions……………………………………………………………….. 25
5. Acknowledgements…………………………………………………………………..
6. References……………………………………………………………………………...

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1. Introduction
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The risks associated with cycling are well known and many people have experienced
them first hand, on the bike and off. This is both a deterrent for prospective cyclists
and an important public health issue in itself (Winters et al., 2011). Yet increasing
participation in cycling as an ‘active transport’ means is a policy objective across
local, national and international institutions (Dora and Phillips, 2000; UNEP, 2010).
The coupled nature of risk perception and participation in cycling means policies will
have to be implemented that not only seek to increase participation but also reduce
the associated danger. Whilst there are many examples of effective policy in Europe
that Britain could learn from, the policies have to be transferable (Maibach et al.,
2009). As can be seen internationally with bicycle helmet legislation, and with
epidemiological matters in general, the multi-causal nature of risk often means blunt
policy tools have diminished effectiveness (Clarke, 2012; Goldacre and
Spiegelhalter, 2013). Therefore for policies to be successful, the risks involved in
cycling must be fully understood (Gigerenzer and Edwards, 2000. Laflamme and
Diderichsen, 2000).
The costs of increasing participation and decreasing risk must be justified by the
benefits. A substantial proportion of the benefits come from health, deriving from
both exercise and reduced pollution (Lindsay et al., 2011). They include protection
from cardiovascular disease and cancer, both major sources of preventable deaths
in the UK (Murray et al., 2010). A recent study modelled increased participation in
active transport ,walking and cycling, and estimated National Health Service savings
at £17bn over 20 years (Hamer and Chida, 2008; Jarrett et al., 2013;Rutter et al.,
2013). Furthermore, a report by Grouse (2013) calculated the direct benefit of cycling
to the UK economy at £2.9bn. The total benefits directly and indirectly of cycling in
the European Union (EU-27) reaches over £200bn. Around 80% of this figure arises
from health benefits (European Cyclists Federation [ECF], 2013). Beyond the
economics, increasing cycling is set to have an important role in the decarbonisation
of the UK’s transport system (Department for Transport [DfT], 2004; Maibach et al.,

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2009). Transport makes up 21% of the UKs total emissions, and 23% of the EU’s
(European Commission, 2010; Department for Energy and Climate Change, 2012).
The UK has legislated for an 80% reduction in CO2 emissions by 2050, in tandem
with an EU wide agreement to reduce greenhouse gas emissions by 80% for the
same date (Committee on climate change [CCC], 2008). This means that strategies
to increase cycling will likely gain traction across the EU. Indeed if every country in
the EU-27 achieved the same level of cycling as Denmark, then bicycle use would
produce 12-26% of the reductions set for transport in the EU 2050 target (ECF,
2011).
Despite the economic and health benefits, cycling has only recently gained
momentum as a public health policy. This is may be due to cycling’s comparatively
greater danger relative to most major transport means (DfT, 2013b). Car travel in
2011 had a killed or seriously injured casualty rate of only 2.2% to that of cycling,
after adjusting for distance travelled (House of Commons, 2013)‎. It may be worth
noting however that pedestrians have a higher fatality rate than cyclists with this
adjustment. This danger has contributed to a 21 percent decrease in distance cycled
over the period 1985-2010 (DfT, 2006a;Pucher and Buehler, 2008). Alongside a 54%
increase in distance travelled by car over the same period, compounding cycling risk
(Jacobsen, 2003; Department for Transport, 2007).
1.1 Risk Factors
The research conducted on risk factors involved with cycling have largely centred
around bicycle helmet policy (Goldacre and Spiegelhalter, 2013). Although various
other risk factors have been looked at, the putative ones being: age, visibility,
poverty, road vehicles, proximity to junction and cycling volume (Boufous et al.,
2012; Johnson et al, 2010; Thornley et al., 2008). Age as a risk factor for cycling
road accidents involving vehicles has some consensus, with younger groups being
vulnerable. Tin Tin et al., 2013 found that younger age groups had an increased risk
of collision in comparison with older age groups. Similarly Sacks et al., (1991) found
that 76% of bicycle accidents happened to children less than 15 years of age.
Martínez-Ruiz et al., (2014) looked at a Spanish road accident database, and
adjusting to exposure, discovered that cyclists younger than 30 and older than 65

5
being at increased risk. This study will look at how age has altered through time in
the amount of cycling road accidents, and predicts an increase in mean age of
casualties across the period 1985-2010.
Goods vehicles above 3.5 tonnes (HGVs), are also known to be a risk factor for
cyclists, especially in cities. In London fatalities from HGVs make up between 30 and
50% of total cycling fatalities (McCarthy and Gilbert, 1996; Morgan et al., 2010).
Whilst the focus of academic and media attention has been on HGVs in London, not
much work has been conducted about their role in nationwide. This study will look at
HGV’s impact on road accident casualties amongst cyclists across the whole of the
UK. It will test whether HGVs are responsible for comparatively more deaths than
cars.
The “safety in numbers” effect, whereby more cyclists on the roads cause vehicles to
adopt safer behaviour, will be greater with increased participation (Jacobsen, 2003).
Although the interplay between a consequent reduction in motor vehicle use and
“safety in numbers” contrasts with inherent risk of riding a bike. Schepers and
Heinen, (2013) suggest that absolute road accident fatalities remain the same, yet
serious injuries increase. They observe an age dependent effect whereby older age
groups see an increase in fatalities balanced by a decrease in the younger
generations. However the ‘safety in numbers’ effect still stands when adjusted for
rate.
Road accidents are recorded by the UK police in a form called STATS19
(Department for transport, 2011a; 2013a). Table 1 details the guidance given for
completion of the STATS 19 form. The form contains 69 different variables from age
of the casualty to direction the vehicle was travelling. Many of the known risk factors
for cyclists are recorded. The data is then collected and put into yearly databases.
The STATS19 databases provide a comprehensive, objective and relevant resource
for looking at risk factors in cycling. In this study, using the STATS19 database for
1985-2010, differing severities of casualties will be analysed against risk factors that
are known from the literature. Updating and expanding upon previous work such as
Stone and Broughton’s (2003) paper on road cycling accidents in the UK through the

6
1990’s. The project will also seek to address some of the popular conceptions
surrounding cycling, for example the danger HGVs pose to cyclists (Ackery et al.,
2012; Tin Tin et al., 2013). The key risk factors will then be statistically analysed to
determine any relationships with cycling accidents through this time period. .
Table 1. Types of Fatal, Serious and Slight injuries to be reported in the STATS 19
form (Department of the Environment, Transport and the Regions, 2001).
Fatal Serious Slight
Cases where death
occurs in less than
30 days as a result
of the accident.
Fracture Sprains, including neck
whiplash injury, not
necessarily requiring medical
treatment
Internal injury Bruises
Severe cuts Slight cuts
Crushing Slight shock requiring
roadside attention.
Burns (excluding
friction burns)
Concussion
Severe general shock
requiring hospital
treatment
Detention in hospital
as an in-patient, either
immediately or later
Injuries to casualties
who die 30 or more
days after the accident
from injuries sustained
in that accident.

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1.2 Objectives and Hypotheses
 To look at the change in road accident casualties over the period 1985-2010.
H1): All severities of road accident casualties amongst cyclists have decreased
over the period 1985-2010.
 To look at HGVs impact on road accidents amongst cyclists across the whole of
the UK.
H2) Heavy Goods Vehicles (HGVs) are responsible for comparatively more
deaths than cars.
 To look at how the age of road cycling casualties has altered across the period
1985-2010.
H3) The mean age of cycling casualties has risen across this period.

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2. Method
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Regression models of the STATS19 database for road accidents were carried out
using SPSS. Firstly 26 years of the STATS19 databases were downloaded from the
UK Data Service (UK data service, 2013). For each year accident data, casualty data
and vehicle data were merged using PASW statistics V.18 (Chicago: SPSS Inc.).
The resulting database was then filtered to leave only incidents involving cyclists.
From these two aggregate databases were created: one with just cycling casualties,
and one with all incidents involving cyclists. The files were used to analyse casualty
numbers and the results recorded, tabulated and graphed in Microsoft Excel 2010
(Wasington: Microsoft). Various other risk factors were also explored: season, day of
the week, time of day, vehicle type, age, sex. These were assessed to look at their
effects on casualties and find any relationships that differed from the literature. For
each risk factor, a new database was created, and crosstabulation between the risk
factor variable, year and casualty severity was analysed. This also enabled a more
holistic approach to finding anomalies in the database.
The key variables were then selected. A good model should be one which uses only
an optimal subset of predictors (Steel and Uys, 2007). This strengthens the models
assumptions, enhancing replicability and potentially improving the identification of
predictors significantly influencing the dependant variable. For this study there were
two main considerations on variable selection. Firstly the putative risk factors
featured in the literature: age, visibility, poverty, road vehicles, proximity to junction
and cycling volume. Secondly the data available through the STATS19 form. Of the
main variables featured in the literature, only a few were suitable given the STATS19
data. These were age, road vehicles and proximity to junction. Unfortunately the
profile of the casualty in STATS19 is limited to sex and age, therefore socioeconomic
circumstances like poverty could not be assessed. Although several factors affecting
visibility were present in the data, such as light conditions and whether, the visibility
of the cyclist themselves was not included. Most of the literature surrounding cycling
visibility has been focused on the cyclist themselves e.g. lights hi-vis clothing, so the
proxy light conditions were considered to have too much uncertainty, and therefore

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could not meaningfully contribute to the literature (Kwan and Mapstone, 2009; Tin
Tin et al., 2013; Williams and Hoffmann, 1979). Incident proximity to junction is
recorded in STATS19, however Stone and Broughton’s (2003) paper already
assessed the role proximity had to casualty rates in the UK. Also incident proximity
to junction was believed to be largely time independent.
2.1 Model Construction
A multiple regression model was chosen for the statistical analysis as four outcome
(dependent) variables, were trying to be explained with four independent (predictor)
variables. Multiple regression was chosen over logistic regression as all variables
were continuous measurement variables (McDonald, 2009; Field, A., 2009).The
regression model was initially run with forced entry inputting all the chosen
independent variables (predictors) accident year, car, HGV, mean age for each
outcome (fatal rate, serious rate etc). This resulted in over fitting with large
correlations seen between all the predictors most of which were >0.7. As the goal of
the multiple regression models on the risk factors was explanatory, then
multicollinearity presents a problem for interpretation of each predictors relationship
with casualties (Field, 2009).
A backward stepwise regression was then constructed to explore the model.
Accident year was the only variable to be removed with a removal criterion of f>=
0.51. However multicollinearity remained high (VIF>10) (Menard, 2002; Myers,
1990). The only model which resulted in VIF values <10 were those with accident
year and one other variable. Therefore individual models were run with accident year
and each of the three other predictors for all 4 independent variables. A principle
component analysis (PCA) was considered, however a sample size of only 26 years
was inadequate (Guadagnoli and Velicer, 1988). Also the descriptive rather than
strictly predictive nature of this study meant that a PCA would obscure the predictors
and make interpreting the risk factors more difficult.

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2.2 Model accuracy
The accuracy of the models was then tested. The standardised residuals and cooks
distance were checked for outlying and/or influential cases. Homoscadisity in each
model was tested visually using histogram and normal probability plots of the
residuals normality. Plots of the standardised residual values against standardised
predicted values were assessed, looking for any noticeable funnelling or curvature.
Testing for the independence of errors was done using a Durbin-Watson test (Durbin
and Geoffrey, 1951). Cross validation of the models was tested using the adjusted
R2
.

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3. Results
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Throughout the years 1985-2010 there were 568,556 cycling casualties recoded in
the STATS19 database. Of which 476068 were slight injuries, 87738 were serious
injuries, and 4750 were fatalities
3.1 Descriptive Trends
The trend in total road accident casualties for cyclists the period 1985-2010 are
shown in Fig 1. A general decline can be seen in total cycling casualties, with a
36.5% decrease between 1985-2010. Slight, serious and fatal casualties displayed
reductions of 32.5%, 50.6% and 61.6% respectively. Notably there is a spike across
all severity types in 1989, as well as a trough in occurring in the mid 2000’s.

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0
1000
2000
3000
4000
5000
6000
1985 1990 1995 2000 2005 2010
NumberofCasualties
Year
Serious
Fatal
Fig 1. a) Road accident casualties year on year involving cyclists slightly injured and
total number injured, 1985-2010. b) Road accident casualties year on year involving
cyclists fatally or seriously injured, 1985-2010.
0
5000
10000
15000
20000
25000
30000
35000
1985 1990 1995 2000 2005 2010
NumberofCasualties
Total
Slight
(b)
(a)

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Fig 2. Road accident rate for cycling casualties. Total casualties and slightly injured
(a), serious and fatally injured (b) (per 1,000,000 miles cycled), 1985-2010.
The road accident rate for cyclists can be seen in Fig 2. The peak accident rates
occur throughout the mid 1990’s for all casualty types. Despite the peak, all casualty
types’ accident rates drop over this period. Fatal, serious, slight and total show a
50%, 37%, 14% and 19% reduction respectively. The spike of 1989 seen in Fig 1
0
1
2
3
4
5
6
7
1985 1990 1995 2000 2005 2010
Accidentrate(per1.000.000
miles)
Year
Total
Slight
0
0.2
0.4
0.6
0.8
1
1.2
1985 1990 1995 2000 2005 2010
AccidentRate(per1,000,000km)
Year
Serious
Fatal

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also occurs in accident rates, indicating an increase in risk resulting in a spike in
casualties. The spike in all accident rates of 2007 is not reflected in the total
accidents (Fig 1). Explainable by a reduction in cycling below trend combined with a
similar level of accidents.
Fig 3. Yearly percentage of road accident casualties amongst cyclists by age group,
1985-2010.
The yearly percentage ratio for the differing age groups of cycling casualties can be
seen in Fig 3. Both the 0-9 age group and the 10-19 age group exhibit a decrease
between 1985-2010, with a notable 54% decrease in the 10-19 age group. The
percentage casualties of the age groups 20-29, 30-39, 40-49 progressively rose from
9% to 117% to the largest change of 185%. The increasing percentages tailed off
through the 50-59 and 60-69 age groups, with a 76% then 20% increase
respectively. Interestingly the 90-99+ age group exhibited a 93% decrease, with the
80-89 age group seeing a 4% increase. This disparity may be due to a small sample
bias in these age groups.
0
5
10
15
20
25
30
35
40
45
50
1985 1990 1995 2000 2005 2010
Percentageoftotalcasualties
Year
0-9
10-19
20-29
30-39
40-49
50-59
60-69
70-79
80-89
90-99+

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The percentage ratio of road cycling accidents for males and females saw a small
trend over the period. There was a marginal increase in the ratio of male accidents of
3.5%. With a corresponding 15.2% reduction in the ratio of female casualties
between 1985-2010.
Fig 4. Percentage of road accident cycling casualties involving HGVs by severity,
1985-2010.
The percentage of cycling casualties caused by HGVs are displayed in Fig 4 for the
period 1985-2010. A decrease occurred in the total casualties, as well as the serious
and slight casualties. Fatalities caused by HGVs fluctuated greatly, possibly due to
the smaller sample size. HGVs make a much larger contribution towards fatal and
serious accidents in comparison to total accidents.
0
5
10
15
20
25
1985 1990 1995 2000 2005 2010
Percetageoftotalcasualties
Year
Total
Slight
Serious
Fatal

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3.2 Multiple Linear Regression Models
Multiple liner regression models were used to examine the affect that the chosen risk
factors: year, mean age and mileage driven in billions of km’s for HGVs and cars,
had on four outcomes of cycling casualties. Table 2 gives the summary statistics for
all the variables. Correlations between the chosen risk factors can be seen in table 3.
Significant zero order positive correlations can be seen between all of the predictor
variables (p<.001). The Pearson’s correlation between all the variables is high in all
but a few relationships. This suggests high levels of multicollinearity (Field, 2009).

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Table 2. Summary statistics for all variables.
N Minimum Maximum Mean Std.
Deviation
Year 26 1985 2010 1997.50 7.64
Mean age 26 25 33 27.89 2.33
Car (Billions
km)
26 156 247 221.02 25.98
HGV (Billions
km)
26 12 18 16.22 1.67
Bike (Billions
of km)
26 4 6 4.60 .603
Fatal casualties 26 104 294 182.69 59.80
Serious
Casualties
26 2174 5366 3374.54 997.67
Slight
Casualties
26 13631 23383 18310.31 3300.58
Total
Casualties
26 16195 28513 21867.54 4253.78
Fatal Rate
(Fatal/Bike)
26 21.67 56.54 39.32 9.79
Serious Rate 26 510.22 957.25 727.84 163.21
Slight Rate 26 2985.22 5209.50 4015.60 767.44
Total Rate 26 3542.83 6209.75 4782.78 904.38

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Table 3. Correlations between all predictors (N=26).
HGV Car Mean
age
Accide
nt year
HGV Pearson
Correlation
1 .957**
.676**
.835**
Sig. (1-tailed) .000 .000 .000
Car Pearson
Correlation
.957**
1 .779**
.917**
Sig. (1-tailed) .000 .000 .000
Mean
age
Pearson
Correlation
.676**
.779**
1 .950**
Sig. (1-tailed) .000 .000 .000
Accide
nt year
Pearson
Correlation
.835**
.917**
.950**
1
Sig. (1-tailed) .000 .000 .000
**. Correlation is significant at the 0.01 level (1-tailed).

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Table 4. Multiple linear regression models to predict the fatal, serious, slight and total rate of cycling casualties from predictors: accident year,
mean age, car mileage, and HGV mileage. *p<0.05. **p<0.01.
1 2 3 4
Constant
Accident
Year
Constant
Accident
Year
Mean
age
Constant
Accident
year
Car Constant
Accident
year
HGV
B 2280.22 -1.122 16979.67 -0.204 -3.17 3492 -1.751 0.202 2968 -1.482 1.975
Fatal
Rate
Std. Error 252.11 .126 12527.04 688.22 0.36 1.181 561.46 0.29 0.085 418.27 0.216
β -.876** -0.159 -.755* -1.367** .536* -1.157** 0.337
Adjusted
R2 .757 0.807 0.796 0.784
B 37874.89 -18.597 16979.7 -7.606 -37.941 45258.5 -22.429 1.23 36641.5 -17.951 -3.541
Serious
Rate
Std. Error 4267.19 2.136 12527 6.556 21.499 10462.7 5.399 1.589 7663.18 3.958 18.102
β -.871** -0.356 -0.542 -1.051** 0.196 -.841** -0.036
Adjusted
R2 0.749 0.77 0.745 0.739
B
102220.2
8
-49.164 -309350 167.315 -747.32 348072 -176.78 40.963 215071 -108.29 323.994
Slight
Rate
Std. Error 35664.68 17.855 64825.9 33.926 111.257 68433.4 35.311 10.393 57367.6 29.632 135.515
β -.490* 1.667** -2.271** -1.762** 1.387** -1.079** 0.706*
Adjusted
R2 0.208 0.721 0.507 0.338
B
142375.3
9
-68.882 -291836 159.505 -788.43 396822 -200.96 42.395 254681 -127.72 322.428
Std. Error 39186.9 19.618 76486.7 40.028 131.269 78002.5 40.248 11.846 64425.2 33.277 152.186
Total
Rate
β -.583* 1.35** -2.03** -1.70** 1.218** -1.08** .596*
Adjusted
R2 0.312 0.72 0.539 0.399

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Coefficients from the linear regression modelling of casualties accounted for by the
predictors: accident year, mean age, car and HGV, can be seen in table 4. A significant
negative relationship in all four models is seen with accident year, as expected from the
hypothesis and the downward trend seen in figure 2 (p<0.05). The risk factor HGV shows a
positive relationship with slight and total rates but not with fatal rate. Car on the other hand
has a positive relationship with all but serious models. Mean age has a negative relationship
with all but serious rate.
Some of the beta values in the regression model exceed the -1,1 bounds, this is a sign of
multicollinearity between the variables (Deegan, 1978). The adjusted R2
values are high
(>0.7) in fatal and total rate, suggesting a well fitted model. In total and slight rate they are
between 0.2 and 0.7 suggesting a less fitted model. Testing for independence of the errors
was done using a Durbin-Watson test. As all the models had two variables and 26 cases,
they had the same corresponding parameters of dL=1.000 and dU = 1.311 For the fatal rate
model, they were all above the dU, showing no autocorrelation between adjacent residuals.
The serious, slight and total rate models all had values less than one This suggests positive
first order autocorrelations (Durbin and Watson, 1951).
The collinearity of the predictors car and HGV was acceptable (VIF<10), with a VIF of 6.28
and 3.29 respectively. However mean age had a VIF of 10.24 suggesting high levels of
collinearity (Myers, 1990). Furthermore the models’ average VIF factor was >1 at 6.5 strongly
suggesting bias in the model from multicollinearity (Bowerman and O'Connell, 1990). There
were no outlying and/or influential cases; none of the cases exceeded a Cooks value >1, nor
had standardised residuals >±2, suggesting accuracy (Cook and Sanford, 1982; Field,
2009).

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4. Discussion
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4.1 Risk factors
The 35% reduction in road cycling accidents across the UK over the period 1985-
2010 is substantial, and the reductions weighting towards fatal and serious injuries
can be seen as a success for policy makers such as the department for transport
(DfT, 2004; 2011b). Yet these trends in absolute terms mean little. They need to be
assessed as a rate, taking into account population and distance travelled, and as
such absolute targets set by the DfT and Transport for London (TFL) are not
representative of true risk reduction (TFL, 2013). The rate of reduction in total
accidents showed a smaller but still notable 19% decrease. This supports the
hypothesis that all severities of road accident casualties amongst cyclists have
decreased over the period 1985-2010.
Mean age’s significance as a risk factor across all but serious casualty types is
characterized in a negative relationship, with the increase seen in mean age relating
to a reduction in casualties. This shift in the age demographic of cycling casualties is
notable, and the 54% reduction in the ratio of casualties amongst the previous
highest risk age group 10-19 is positive. Conversely this was balanced by a rise in
percentage casualties amongst the middle ages. Such a shift may theoretically have
implications on the number of fatalities, as the older the casualty the greater the
chance of fatality; interestingly this was not seen in this study (Stone and
Broughton’s, 2003; Tin Tin et al., 2013). The reduction in the 10-19 age group,
considered to be one of the most at risk, may in part be due to improved cycle
training. However it is most probably due to a reduction in cycling, with school
transportation shifting from active transport to cars and public transport (Mills, 1989).
The increase in ratio of middle aged casualties could to some extent be due to the
positive image of autonomy cycling has gained in certain cultures over recent years,
resulting in an increase in participation (Steinbach, et al., 2011). Mean age’s
significant negative beta in all but serious rate, supports the hypothesis that the
mean age of cycling casualties has risen across this period. Unfortunately there was

22
no exposure data available for the different age groups, and so it cannot be said that
there has been a shift in the age demographic of cyclists themselves.
In line with the literature; casualties involving HGVs had a larger ratio of fatal
casualties than that of cars (McCarthy and Gilbert, 1996). Yet HGV mileage was a
non-significant predictor of fatal and serious casualties, whereas cars showed
significance in all but serious accidents. Therefore it rejects the hypothesis that
HGVs are comparatively more responsible for road cycling fatalities than cars. This
discrepancy may be due to fatalities’ small sample sizes. It may also be explained by
an effect with HGVs in rural environments; whilst it is known that HGVs are larger
risk factors than cars in urban environments, there is little research into their risk
posed in rural settings, which this study incorporates (McCarthy and Gilbert, 1996;
Morgan, et al., 2010). It could also be explained by their incidence rate being similar
to that of cars, yet their outcomes being more severe.
4.2 Limitations and Strengths
The relatively small time span (n=26) meant that over fitting of the model was
encountered when more than two predictors were incorporated. The small number of
degrees of freedom prevented a larger, more holistic model from being fitted, and so
smaller less predictive models were used. The autocorrelation of errors seen in the
serious, slight and total rate model suggests that cycling risk has some other link
perhaps to shifting cycling demographics or riding styles.
The database’s significant correlations between the key predictor variables posed a
problem in terms of multicollinearity (collinearity at the predictor number used). At an
extreme level if collinearity between predictors is too high then the similarity in their
effect on the outcome variable will make it impossible to tell which one is important,
and so one will be made redundant. At a lesser level one may be underrepresented
because of the overlap between each variable being attributed to one. This means
that some the key variables’ unique variance (predictive power) was lost through
collinearity. However the presence of collinearity doesn't affect the efficacy of
extrapolating the model to new data provided that the predictor variables follow the
same pattern of collinearity in the new data. Therefore whilst these models were a

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good fit, caution should be taken in extrapolating it beyond the UK, or taking the
individual predictor results out of context.
The underreporting of accidents to the police presents a big problem for the
completeness of STATS19 data. A review of studies done on underreporting of
accidents using hospital admission data was conducted by DfT (2006). It found that
due to hospitals misclassifying road accident cycling admissions there are few
conclusions that can be drawn about its prevalence. However previous studies have
given figures of the percentage reported to the police at between (22-70%) (Austin,
1992; Broughton et al., 2005; Simpson, 1996). It is known that the more serious the
injury the more likely it is to be reported, with higher reporting rates for fatal and
serious accidents (DfT, 2006b). So as a snapshot the STATS19 database may
contain a representation bias towards the more serious accident. Reporting may also
be skewed towards different risk factors, for example accidents involving HGVs see
different levels of reporting to those of a car. Providing these biases remains
constant, trends throughout time maintain their accuracy. However a complete
picture of the road accidents and their proportion cannot be had. This should be
bared in mind with policy makers.
Despite these limitations the comprehensive nature of the STATS19 database
provided a robust indicator of cycling road accidents across the UK. The chosen risk
factors used in the regression models had a good background in the literature, as
well as displaying statistical significance in the models, particularly fatal, slight and
total. The time period used was a period of large change amongst the risk factors,
providing a good environment to model their effects (DfT, 2006a; Pucher and
Buehler, 2008).

24
4.3 Conclusions
 There was a decrease in all severities of road accident casualties over the period
1985-2010 (fig 1a,b).
 Despite HGVs accounting for a larger ratio of fatalities than that of cars. HGVs
were weaker predictors of all types of road accident casualties than cars (table
2).
 The mean age of cycling casualties rose across the period 1985-2010 (table 4;fig
3) .
The conclusions of this study are mainly ones of encouragement; rates of all types of
casualties have decreased across the study period. This background of safer cycling
in the UK as a whole, despite a reduction in participation opposes the “safety in
numbers effect” (Jacobsen, 2003). Whilst factors such as improving emergency care
and safer car builds have undoubtedly improved casualty outcomes, and possibly
lowered fatalities, the causes of risk reduction in cycling remain unclear (European
Road Safety Action Program, 2003). The increase observed in the mean age of
cycling casualties may in part explain a risk reduction; provided the cycling
casualties are representative of cyclists as a whole, a shift from the more vulnerable
young to the middle age would lower risk (Sacks et al., 1991). The risk posed by
HGVs appears inconclusive when compared with cars; HGVs accounted for a larger
ratio of fatalities, but were weaker predictors of casualties. This could be explained
by their incidence rate being similar to that of cars, yet their outcomes being more
severe. However the models here did not show this, possibly due to the smaller
sample sizes of HGVs (table 4). It would be beneficial to conduct further research on
this.

25
This reduction in the risk of cycling should make facilitating participation easier
(Pucher and Buehler, 2008). However, a half century long trend of decreasing
participation will have to be reversed for this to occur (DfT, 2011c). However
demand is there from local, national and international policy makers; with much
needed environmental, health and transport benefits on offer (DfT, 2004; ECF 2011).
Whilst this is promising, it is demand from the population itself that is required, and
for that to happen cultural, gender and class barriers, on top of safety will have to be
addressed (Green and Datta, 2011;Maibach et al., 2009; National Statistics, 2013;
Steinbach et al, 2003). Therefore road safety policy in combination with
communication will aid both uptake, and may help reduce participation barriers
(Maibach et al., 2009). Further research into the causes of increasing cycle safety
over the study period, despite reductions in participation, would be useful to advance
understanding of the “safety in numbers effect”. This understanding combined with
research into risk as a barrier to participation will be needed for effective policy
converting safer roads into participation to be constructed.
5. Acknowledgments
----------------------------------------------------------------------------------------------------------------
I am grateful for the willing help shown by Andy Jones. The UK data service for a
providing the STATS19 databases.

26
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