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Bicycle Tracks and Lanes: a Before-After Study

Authors:
Søren Underlien Jensen 1
Bicycle Tracks and Lanes: a Before-After Study
Initial Submission Date: 1 August 2007
Submission Date of Revised Paper: 7 November 2007
Word count: 5,184 words + 2 figures + 6 tables
Author:
Søren Underlien Jensen
Member of TRB Committee on Bicycle Transportation
Trafitec ApS
Research Park Scion-DTU
Diplomvej 376
2800 Kongens Lyngby
Denmark
Tel.: (+45) 25246732
Fax: (+45) 88708090
Email: suj@trafitec.dk
Søren Underlien Jensen 2
ABSTRACT
This paper presents a before-after crash, injury and traffic study of constructing bicycle tracks
and marking bicycle lanes in Copenhagen, Denmark. Corrections factors for changes in
traffic volumes and crash / injury trends are included using a general comparison group in
this non-experimental observational study. Analysis of long-term crash trends points towards
no significant abnormal crash counts in the before period. The safety effects of bicycle tracks
in urban areas are an increase of about 10 percent in both crashes and injuries. The safety
effects of bicycle lanes in urban areas are an increase of 5 percent in crashes and 15 percent
in injuries. Bicyclists’ safety has worsened on roads, where bicycle facilities have been
implemented. Design of bicycle facilities and parking conditions for motor vehicles clearly
seems to have safety implications, especially at intersections. The study has revealed a few
points in relation to this. Construction of bicycle tracks resulted in a 20 percent increase in
bicycle / moped traffic mileage and a decrease of 10 percent in motor vehicle traffic mileage,
whereas marking of bicycle lanes resulted in a 5 percent increase in bicycle / moped traffic
mileage and a decrease of 1 percent in motor vehicle traffic mileage. The changes in traffic
do result in health benefits due to more physical activity, less air pollution and less traffic
noise. The positive benefits may well be much higher than the negative consequences caused
by new safety problems.
Søren Underlien Jensen 3
INTRODUCTION
The traditional Danish bicycle track with a curb to the carriageway and a curb to the sidewalk
is depictured in Figure 1 along with a bicycle lane. The first bicycle tracks in Denmark were
introduced in Copenhagen as early as 1910. Since then about 8,000 km of bicycle tracks and
paths with a dividing verge to the carriageway have been constructed so about every ninth km
of road have these bicycle facilities in rural and urban areas in Denmark.
FIGURE 1 Photos of bicycle track (left) and bicycle lane (right).
Many studies of bicycle tracks have been undertaken in Northern Europe. A meta-
analysis of 11 studies shows a reduction of 4 percent in crashes, and the crash reduction is
almost the same for pedestrians, bicyclists and motorists respectively (1). Danish results show
that construction of bicycle facilities leads to fewer and less severe crashes in rural areas, but
more crashes in urban areas primarily due to increasing crash rates at intersections (2).
Studies show that constructing bicycle tracks and paths increase bicycle traffic volumes (1).
Three studies of marking bicycle lanes in urban areas indicate an increase in crashes
of about 10 percent primarily due to more crashes at intersections (3-5). No reliable studies of
bicycle lanes impact on traffic volumes have been found.
The before-after traffic, crash and injury study, which is presented in the following,
includes construction of one-way bicycle tracks on both road sides along 20.6 km of road and
marking of one-way bicycle lanes on both road sides along 5.6 km of road in Copenhagen,
Denmark. These bicycle tracks were constructed during the years 1978-2003 and the bicycle
lanes were marked 1988-2002. The width of bicycle tracks are about 2-2.5 meters, whereas
bicycle lanes are about 1.5-2 meters. The volume of motor vehicles 6-18 o’clock on a
weekday on the studied roads varies from 5,000 to 28,000 and the corresponding volumes of
bicyclists are 1,000-17,000. A Danish report describes the study in detail (6).
SECOND-BEST METHODOLOGY
Randomized experiments (7), where the experimental units like roads are randomized to
treatment like bicycle lanes, are often viewed as the best way to study road safety effects. In
our case, a randomized experiment has not been undertaken.
The safety effects of bicycle facilities are therefore studied using an observational
study methodology. The Empirical Bayes method (8) is viewed by many as the best of the
non-experimental observational methods. The Empirical Bayes method accounts for three
Søren Underlien Jensen 4
major possible biases in before-after crash studies; regression-to-the-mean effects, crash
trends and traffic volumes.
However, the Empirical Bayes method has not been used in this study. One thing is
that using this method includes a very time-consuming effort to calculate many crash models,
which is needed in this case because the bicycle facilities have been applied over a long
period, and hence many different before and after periods are part of the study. Such crash
models include relationships between crashes / injuries and traffic volumes for different types
of intersections and road links.
A second but much more important thing is that some of the roads, where bicycle
facilities have been applied, are the most trafficked in Copenhagen in terms of bicyclists and
pedestrians. The crash models that need to be developed if the Empirical Bayes method were
to be used could be of the kind shown in general in Formula 2 and 3 later in this paper. Such
crash models are relatively reliable to use in order to predict the number of crashes, if traffic
volumes on the road or at the intersection, where you wish to predict crash figures, are pretty
normal compared to the traffic volumes that the crash models are based upon. In the
Copenhagen case, many of the studied roads / intersections are in the far end of the traffic
volume axis, i.e. much trafficked, and we are therefore close to or outside the boundaries of
the possible crash models’ valid area. The prediction of crash figures for these much
trafficked roads / intersections are unreliable, because the beta figures of the crash models
becomes crucial for the prediction, and these beta figures change from model to model
primarily due to uncertainty, because the models are based on a relatively low number of
roads / intersections. The prediction results for regression-to-the-mean effects and figures for
expected crashes and consequently safety effects will therefore be relatively unreliable,
because most of the crashes in this study actually take place on the much trafficked roads.
Instead a stepwise methodology is used. First, a general comparison group is used to
account for crash trends. Second, changes in traffic volumes are taken into account. And
third, an analysis of long-term crash trends is made in order to check for abnormally high or
low crash counts, i.e. regression-to-the-mean, in the before period. It was chosen to use
equally long before and after periods, which for the individual studied roads was of 1-5 years
duration. The expected number of crashes in the after period is calculated based on a formula,
here shown in the general form:
,)1( RTMTrafficTrendBeforeExpected CCCAA
where AExpected is the number of crashes / injuries expected to occur in the after period if
bicycle facilities were not applied, ABefore is the number of crashes / injuries that occurred in
the before period, CTrend, CTraffic and CRTM are correction factors for crash trends, traffic
volumes and regression-to-the-mean respectively.
The study of bicycle facilities is part of project including studies of reconstructions,
markings, signal-control and traffic calming schemes in the City of Copenhagen. A major
effort was made in order to register all physical changes to the road network in the period
1976-2004. Several hundred schemes were identified.
Several intersections and links had undergone more than one reconstruction or other
scheme. Only “clean” schemes are studied, meaning that the roads, where bicycle facilities
have been applied, no other scheme has been implemented in before and after periods and in
the year(s), when the bicycle facility was applied.
Søren Underlien Jensen 5
Unchanged roads with known developments in traffic volumes were used to set up a
general comparison group. The Copenhagen Police District covers the entire area of the City
of Copenhagen, and there is no indication that crashes are registered differently in one city
quarter compared to another. The general comparison group consists of 110 km of roads with
170 locations, where motor vehicle and bicycle / moped traffic is counted yearly or every
fourth to sixth year. A total of 24,369 crashes and 8,648 injuries occurred on the 110 km of
roads in the period 1976-2004.
Since a general comparison group has been chosen instead of a matched comparison
group, an effort was made in order to avoid consequences of larger differences between
general comparison group and treated roads, where bicycle facilities were applied. Trends for
different types of crashes and injuries of the general comparison group were compared.
Trends for intersection and link crashes are very similar, and hence no need for sub-grouping.
However, trends for different crash / injury severities and transport modes exhibit rather
different developments. It was found reasonable to describe trends by 7 crash sub-comparison
groups and 5 injury sub-comparison groups. These sub-groups are defined in Table 1.
TABLE 1 Definition of 12 Sub-comparison Groups (in Brackets: Number of Crashes /
Injuries 1976-2004)
Bicycle/moped a
Pedestrian b
Motor vehicle c
Crashes with killed / severe injuries
1 (2,197)
2 (1,445)
3 (1,584)
Crashes with minor injuries and no killed /
severe injuries
4 (1,289)
5 (1,228)
Property damage only crashes
7 (13,310)
Killed and severe injuries
8 (2,235)
9 (1,477)
10 (1,907)
Minor injuries
11 (1,359)
12 (1,670)
a Crashes involving cyclists / moped riders and injuries in these crashes,
b Crashes between pedestrians and motor vehicles and injuries in these crashes,
c Crashes only with motor vehicles involved and injuries in these crashes.
So the correction factor CTrend is actually 12 different correction factors, which is the
number of crashes / injuries in the sub-comparison group in the after period divided by the
number of crashes / injuries in the sub-comparison group in the before period. The individual
correction factor, e.g. CTrend,1, is then multiplied with the same sub-group of crashes, which
occurred on the treated road in the before period, ABefore,1, as part of Formula 1.
The correction factor CTraffic is based on changes in traffic volumes on the treated road
and in the general comparison group. The relationship between traffic flow and crashes /
injuries is non-linear. Danish crash prediction models for links (Formula 2) and intersections
(Formula 3) are most often of the following kinds:
,)()3(
,)()2(
21 sec
NNE
NE
pri
where E(μ) is the predicted number of crashes / injuries, N is the motor vehicle daily flow on
the link, Npri and Nsec are the incoming motor vehicle daily flow from primary and secondary
directions at intersections, and α, β, β1 and β2 are estimated parameters. β is often close to 0.7,
and β1 and β2 are often close to 0.5 in the many models that have been developed during the
Søren Underlien Jensen 6
last decades in Denmark, whereas α varies between the different types of roads and
intersections (9-16). Figures for α varies, because the level of safety depends on the type of
road and intersection. In this case, incoming bicycle / moped flow is also known, and here the
sparse number of crash prediction models indicate that bicycle / moped flow only influence
the number of crashes involving cyclists and moped riders. Formula 2 and 3 are then used to
set up formulas for CTraffic:
,)7(
,)6(
,)5(
,)4(
5.0
,
,
sec,
sec,
5.0
,
,
,
,
5.0
,
,
sec,
sec,
5.0
,
,
,
,
5.0
,
,
sec,
sec,
5.0
,
,
,
,
7.0
,
,
7.0
,
,
7.0
,
,
beforeCG
afterCG
before
after
beforeCG
afterCG
beforepri
afterpri
beforeCG
afterCG
before
after
beforeCG
afterCG
beforepri
afterpri
onintersectibike,Traffic,
beforeCG
afterCG
before
after
beforeCG
afterCG
beforepri
afterpri
onintersectipmv,Traffic,
beforeCG
afterCG
before
after
beforeCG
afterCG
before
after
linkbike,Traffic,
beforeCG
afterCG
before
after
linkpmv,Traffic,
BM
BM
BM
BM
BM
BM
BM
BM
MV
MV
MV
MV
MV
MV
MV
MV
C
MV
MV
MV
MV
MV
MV
MV
MV
C
BM
BM
BM
BM
MV
MV
MV
MV
C
MV
MV
MV
MV
C
where CTraffic, pmv is the traffic correction factor for pedestrian and motor vehicle crashes /
injuries (see Table 1), CTraffic, bike is the traffic correction factor for bicycle-moped crashes /
injuries, MV, MVpri and MVsec are motor vehicle daily flow at the treated site on link,
primary and secondary direction respectively, BM, BMpri and BMsec are bicycle-moped daily
flow at the treated site on link, primary and secondary direction respectively, and MVCG and
BMCG are motor vehicle flow and bicycle-moped flow in the comparison group respectively.
Flow data from before and after periods are used, hence, increases and decreases in
traffic volumes from before to after are accounted for. The change from before to after in
motor vehicle traffic varied from -26 percent to +29 percent, however, most treated roads
experienced a minor decrease. Similar the change in bicycle-moped traffic was between -28
percent and +90 percent, most treated roads experienced a larger increase. However, Formula
6 and 7 have been used for the intersections, where traffic volumes for side streets are known.
Søren Underlien Jensen 7
Traffic volumes are known for only about a tenth of the intersections. The rest of the
intersections (minor side streets) have been treated as links using Formula 4 and 5.
The analysis of long-term crash trends is made in order to check for abnormally high
crash counts, i.e. regression-to-the-mean, in the before period. The analysis is made using a
before-before period, which is a 5-year period 8 to 12 years before applying bicycle facilities.
The before-before period is chosen because it most often will be prior to an eventual black
spot identification period or other type of systematic crash investigation period that may have
lead to applying bicycle facilities. This before-before period is then used to calculate an
expected number of crashes and injuries in the before period of the treated roads by making
corrections for crash trends and traffic volumes:
TrafficTrendBeforeBeforeBeforeExpected CCAA ,
The CRTM correction factor is then calculated as the expected number of crashes in the before
period divided by the observed number of crashes in the before period, and likewise for
injuries. However, because not all treated roads can undergo this type of analysis, the CRTM is
set to be the same for all treated roads and is only used, if there are statistically significant
differences between the expected and observed numbers of crashes and injuries in the before
period.
Of the 23 roads, where bicycle tracks were constructed, it is possible to make this
calculation for 9 roads, and the calculation was possible for 5 of 10 roads, where bicycle
lanes were marked. Several roads have been excluded of this analysis because they have been
changed by other schemes in the period between 12 years before the bicycle facility was
applied and the before period. Some roads have been excluded of the analysis because crash
records only are available back to 1976.
TABLE 2 Expected and Observed Crashes and Injuries in the Before-Before and
Before Period, where Bicycle Tracks and Bicycle Lanes have been Applied
Observed
BEFORE-BEFORE
Expected
BEFORE
Observed
BEFORE
Change in safety (percent)
Best estimate
95% CI a
Bicycle tracks
All crashes
686
460
484
-3 b
-21 ; +20 b
All injuries
211
128
140
+10
-13 ; +38
Bicycle lanes
All crashes
411
333
337
+1
-12 ; +18
All injuries
111
89
84
-7
-31 ; +25
a 95% confidence interval, b inhomogeneous i.e. results of random effects model.
The results of the analyses of long-term accident trends, which are shown in Table 2,
indicate no general abnormally high or low crash counts, i.e. regression-to-the-mean effects,
in the before period. Meta-analyses have been used to calculate best estimates for safety
changes and related confidence intervals. Table 2 shows that the best estimate for the change
in safety, where bicycle lanes have been marked, is an increase of 1 percent (+1) in crashes.
This means that the observed number of crashes in the before period is 1 percent higher than
expected. The 95% confidence interval for bicycle lanes, all crashes, is between a fall of 12
percent (-12) and an increase of 18 percent (+18), meaning that the best estimate of a change
in safety is within this interval with 95% certainty. A glance on the confidence intervals in
Table 2 reveals that all intervals include 0 or no change, which means that none of the best
Søren Underlien Jensen 8
estimates are statistically significant different from 0. In other words, Table 2 indicate no
abnormally high or low crash counts in the before period. Results from breakdowns into
different accident / injury severities and transport modes do neither indicate abnormal crash
counts in the before period. The general correction factors for regression-to-the-mean effects
are then set to 1.
Due to major differences in correction factors for crash trends and traffic volumes and
that the bicycle facilities have been applied over a long time span it is founded reasonable to
use meta-analysis rather than simple sums of crashes and injuries in order to describe best
estimates for safety effects and the variance of these effects. The meta-analysis methodology
used is described by Elvik (17). Fixed effects models have been used for homogeneous mean
effects, i.e. only random variation in estimated effects. Random effects models are adopted to
heterogeneous mean effects.
Effects on traffic volumes are simply estimated by taking the traffic development in
the general comparison group into account. Hence, no traffic model has been used. Parallel
streets to the treated roads have been checked for major construction works in the before and
after periods, however, no such construction works have been identified.
RESULTS OF BEFORE-AFTER CRASH AND INJURY STUDY
Bicycle Tracks
The construction of bicycle tracks has resulted in a slight drop in the number of crashes and
injuries on road links between intersections of 10 and 4 percent respectively, see Table 3. The
two figures may be found in Table 3 in the “Links” rows and the “Best estimate” column. In
the confidence interval column it may be seen that none of these safety effects on the links
are statistically significant, because the intervals include 0 or no change. At intersections on
the other hand, the number of crashes and injuries has risen statistically significant by 18
percent. A decline in road safety at intersections has undoubtedly taken place after the
construction of bicycle tracks. If figures for links are combined with those for intersections,
an increase of about 10 percent in crashes and injuries has taken place.
The safety effects of the various bicycle track projects are statistically significant
different in some cases, hence heterogeneous safety effects. The safety effects mentioned
above are therefore not general. The reason for this is that the crash composition and road
design are different on those individual streets, where bicycle tracks have been constructed.
Some road designs with bicycle tracks are safer than others.
The decline in road safety arises, because more pedestrians and bicyclists / moped
riders are injured at intersections. There are statistically significant increases in injuries at
intersections of 30 and 24 percent respectively for these two road user groups. No major
changes in injuries have occurred to motorists.
The increase in injuries to women is 18 percent, whereas there is only a small rise in
injuries to men of just 1 percent. The increase in injuries is especially large among females
under 20 years of age on foot and bicycle, as well as female pedestrians over the age of 64.
On the other hand, there is a considerable fall in injuries among older bicyclists and children
in cars of both sexes. The figures for men and women and four age groups have been rescaled
in order to account for different trends in the general comparison group.
Søren Underlien Jensen 9
TABLE 3 Safety Effects of Bicycle Tracks
Observed
BEFORE
Expected
AFTER
Observed
AFTER
Safety effect (percent)
Best estimate
95% CI a
Crashes
All
2,987
2,663
2,911
+10 b
-2 ; +23 b
Injury
1,313
784
875
+12
+2 ; +23
Property damage only
1,674
1,879
2,036
+6 b
-8 ; +22 b
Injuries
All
1,476
857
937
+9
+0 ; +19
Fatal
25
19
22
+10
-1 ; +23
Severe
757
606
665
Minor
694
231
250
+8 b
-17 ; +40 b
Intersections
All crashes
2,010
1,840
2,171
+18 b
+6 ; +32 b
All injuries
938
541
636
+18
+6 ; +31
Links
All crashes
977
823
740
-10 b
-26 ; +10 b
All injuries
538
316
301
-4
-17 ; +12
Pedestrians,
all injuries
Total
469
271
315
+19
+2 ; +38
At intersections
267
154
197
+30
+7 ; +57
On links
202
117
118
+7
-16 ; +35
Bicyclists and
moped riders,
all injuries
Total
574
369
406
+10
-4 ; +26
At intersections
353
230
285
+24
+5 ; +46
On links
221
139
121
-13
-32 ; +10
Motorists,
all injuries
Total
433
217
216
+4 b
-24 ; +43 b
At intersections
318
157
154
-3 b
-32 ; +39 b
On links
115
60
62
-1 b
-28 ; +37 b
a 95% confidence interval, b inhomogeneous i.e. results of random effects model.
The crash composition has changed markedly after the construction of bicycle tracks.
Table 4 shows that the construction of bicycle tracks resulted in three statistically significant
gains in road safety. Rear-end crashes where motor vehicles hit bicycles / mopeds from
behind have fallen by 63 percent due to the traffic separation. Crashes with left-turning
bicycles / mopeds have fallen by 41 percent and crashes with bicycles / mopeds against
parked motor vehicles have decreased by 38 percent.
These safety gains were more than outweighed by new safety problems, where the
number of crashes has risen statistically significant. Rear-end crashes where a bicycle /
moped hit another bicycle / moped from behind has risen by 120 percent. Crashes with right-
turning vehicles have risen by 140 percent. All kinds of right-turn crashes have increased in
numbers. Crashes with left-turning motor vehicles against bicycles / mopeds have risen by 48
percent. Lastly, crashes between bicycles / mopeds and pedestrians or entering / exiting bus
passengers have also risen significantly.
Prohibiting parking is one reason why the construction of bicycle tracks brings about
more crashes and injuries. Prohibiting parking on a road with a bicycle track results in motor
vehicles being parked on minor side streets and consequently more turning traffic, especially
at right of way regulated intersections. The construction of bicycle tracks and prohibition of
parking resulted in an increase in crashes and injuries at intersections of 42 and 52 percent
respectively. The construction of bicycle tracks combined with permission to park also
resulted in an increase in crashes and injuries at intersections but of only 13 and 15 percent
Søren Underlien Jensen 10
respectively. On road links with parking ban, there was a 24 percent increase in crashes,
whereas on links with parking permitted crashes fell by 14 percent. When parking is
permitted, there are fewer parking crashes, rear-end crashes and pedestrian crashes. This
means that illegally parked motor vehicles causes more crashes than legally parked vehicles.
The total width of drive lanes is reduced when parking is permitted, resulting in increased
safety for pedestrians when they cross the road.
TABLE 4 Effects on Crashes of Bicycle Tracks Divided into 11 Crash Situations
Observed
BEFORE
Expected
AFTER
Observed
AFTER
Safety effect (percent)
Best estimate
95% CI a
Single
vehicle crash
All crashes
170
151
142
-3
-23 ; +22
MV c
134
127
111
-8
-29 ; +19
BM d
36
23
31
+16
-30 ; +91
Rear-end
crash
All crashes
718
674
584
-7 b
-22 ; +12 b
MV and MV
517
490
483
+1
-11 ; +15
MV and BM
173
164
57
-63
-73 ; -49
BM and BM
28
20
44
+120
+37 ; +253
Frontal crash
All crashes
77
71
92
+34
-2 ; +82
Right-turn
crash
All crashes
160
169
397
+140
+98 ; +190
MV and turning MV
47
41
73
+70
+15 ; +151
Turning MV and BM
81
104
282
+129 b
+57 ; +233 b
Turning MV and Ped e
25
20
32
+77
+4 ; +202
Turning BM
7
4
10
+135
-17 ; +561
Left-turn
crash
All crashes
614
548
589
+12 b
-7 ; +33 b
MV and turning MV
334
299
334
+9 b
-16 ; +40 b
Turning MV and BM
120
119
161
+48 b
+4 ; +110 b
Turning MV and Ped
65
45
47
+1
-33 ; +53
Turning BM
95
85
47
-41
-59 ; -15
Right-angle
crash
All crashes
575
536
522
-1
-13 ; +11
Crash with
parked MV
All crashes
217
182
142
-21
-36 ; -1
MV and parked MV
123
105
96
-8
-30 ; +22
BM and parked MV
94
78
46
-38
-57 ; -11
Crash with
pedestrian
from right
All crashes
296
220
244
+13
-5 ; +34
MV and Ped
228
162
140
-10
-28 ; +11
BM and Ped
68
58
104
+80
+30 ; +148
Crash with
pedestrian
from left
All crashes
123
83
85
+23 b
-25 ; +102 b
MV and Ped
111
75
68
+5 b
-38 ; +78 b
BM and Ped
12
9
17
+78
-15 ; +273
Crash with entering or exiting bus
passenger
5
4
73
+519
+157 ; +1390
Other pedestrian crashes
32
25
41
+66
+3 ; +167
a 95% confidence interval, b inhomogeneous i.e. results of random effects model, c motor vehicle, d bicycle
or moped, e pedestrian.
Søren Underlien Jensen 11
Several design features especially at intersections affect the safety effects. At
signalized intersections, it has been found that the number of crashes with traffic from entry
lanes with a shortened bicycle track ending before a right-turn lane, see Figure 2, fell
statistically significant by 30 percent, whereas the number of injuries increased by 19 percent.
Another design at signalized intersections is to end the bicycle track at the stop line, i.e.
advanced bicycle tracks. This resulted in a statistically significant increase of 25 percent in
crashes, whereas injuries only increased by 9 percent. Entry lanes with an advanced bicycle
track and no turn lanes for motor vehicles resulted in statistically significant increases of 68
and 67 percent in crashes and injuries respectively. The figures for entry lanes with turn lanes
and advanced bicycle track showed a 15 percent increase in crashes and a fall of 5 percent in
injuries. A comparison shows that entry lanes with an advanced bicycle track without turn
lanes for motor vehicles is the design, which is most unsafe. Shortened bicycle tracks and
advanced bicycle tracks with turn lanes for motor vehicles are equally effective as far as
safety goes. There is a difference, however, advanced bicycle tracks are best for pedestrians
and bicyclists, whereas shortened bicycle tracks are best for motor vehicle occupants. Other
results for e.g. non-signalized intersections and bus stops also shows significantly different
safety effects for the various designs.
FIGURE 2 Photos of shortened bicycle track (left) and advanced bicycle track (right).
Bicycle Lanes
The marking of bicycle lanes resulted in an increase in crashes of 5 percent and 15 percent
more injuries, see Table 5. These increases are not statistically significant. The decline in
road safety can be seen both at intersections and on links. The worsening safety occurred
especially amongst bicyclists and moped riders, where the increase in injuries is 49 percent.
In line with the study of bicycle tracks, there is a larger increase in injuries among
women of 22 percent with the marking of bicycle lanes, whereas the figure for men was only
7 percent. There is a fall in injuries among children under 20 years of age and an increase
among those aged 20-34.
Søren Underlien Jensen 12
TABLE 5 Safety Effects of Bicycle Lanes
Observed
BEFORE
Expected
AFTER
Observed
AFTER
Safety effect (percent)
Best estimate
95% CI a
Crashes
All
389
295
311
+5
-10 ; +23
Injury
95
90
102
+14
-15 ; +52
Property damage only
294
205
209
+1
-16 ; +21
Injuries
All
106
98
113
+15
-13 ; +52
Fatal
3
3
0
+22
-15 ; +73
Severe
72
48
59
Minor
31
47
54
+5
-36 ; +73
Intersections
All crashes
327
249
247
0
-16 ; +18
All injuries
87
82
93
+14
-16 ; +54
Links
All crashes
62
47
64
+30
-9 ; +87
All injuries
19
16
20
+27
-38 ; +160
Pedestrians,
all injuries
Total
29
24
19
-17
-54 ; +49
At intersections
23
20
18
-8
-51 ; +74
On links
6
4
1
-53
-91 ; +154
Bicyclists and
moped riders,
all injuries
Total
41
39
60
+49
-1 ; +126
At intersections
33
30
47
+57
-1 ; +150
On links
8
9
13
+27
-48 ; +207
Motorists,
all injuries
Total
36
35
34
+12
-34 ; +89
At intersections
31
32
28
+1
-43 ; +79
On links
5
3
6
+39 b
-98 ; +10753 b
a 95% confidence interval, b inhomogeneous i.e. results of random effects model.
The marking of bicycle lanes has a markedly different effect on the crash composition
compared to the construction of bicycle tracks. Bicycle lanes did not apparently lead to an
appreciable fall in rear-end crashes between motor vehicle and bicycle / moped or crashes
involving left-turning bicycle / moped. Conversely, the marking of bicycle lanes did not
apparently lead to an increase in crashes between bicycle/moped and pedestrians or crashes
between left-turning motor vehicle and bicycle / moped.
There are however similarities. The number of crashes involving right-turning motor
vehicles increased statistically significant by 73 percent with the marking of bicycle lanes.
There was also a considerable increase in rear-end crashes between two bicycles / mopeds.
RESULTS OF BEFORE-AFTER TRAFFIC STUDY
The construction of bicycle tracks resulted in a 20 percent increase in bicycle/moped traffic
mileage and a decrease of 10 percent in motor vehicle traffic mileage on those roads, where
bicycle tracks have been constructed, see Table 6. These effects are statistically significant. A
considerable amount of these effects were already visible during the construction period,
although the effects increased after road works were completed.
The marking of bicycle lanes resulted in a 5 percent increase in bicycle / moped
traffic mileage and a decrease of 1 percent in motor vehicle traffic mileage on those roads,
where bicycle lanes have been marked. These effects are not statistically significant.
Søren Underlien Jensen 13
TABLE 6 Effects on Traffic of Construction of Bicycle Tracks and Marking Bicycle
Lanes
Traffic effect (percent)
Best estimate
95% CI a
Bicycle tracks
Bicycle / moped traffic mileage
+20
+11 ; +29
Motor vehicle traffic mileage
-10
-14 ; -6
Bicycle lanes
Bicycle / moped traffic mileage
+5
-4 ; +14
Motor vehicle traffic mileage
-1
-10 ; +8
a 95% confidence interval.
Bicycles comprise over 95 percent of bicycle / moped traffic. The effects are valid for
bicycle traffic, but it is not known whether they are valid for moped traffic on its own.
DISCUSSION
The study is based on a second-best methodology. Corrections for changes in traffic volumes
and road safety trends have been made. Despite methodological shortcomings, study results
show systematic patterns. Several safety and traffic effects are statistically significant. The
analyses point towards specific safety gains and flaws for different road user groups, crash
situations and road and intersection designs. Overall, there is internal consistency in the
changes of safety and traffic volumes, which indicate causality, and the causal direction
seems clear.
The bicycle facilities effects on traffic volumes are rather large. We do not know for
sure whether these effects are a result of changes of route choice or transport mode choice or
both. The magnitude of the changes in traffic volumes on the reconstructed streets, and the
traffic volumes on parallel streets, however, do indicate that thousands of travelers in total
must have changed their choice of transport mode. We do not know who have shifted mode
children, middle-aged or elderly, women or men, beginners or experienced, etc. Another
point is that the reduced motor vehicle traffic volumes may have resulted in traffic operation
changes e.g. higher vehicular speed, increased crossing activity by pedestrians outside formal
crossings, etc. Due to dramatic shifts, the corrections for changes in traffic volumes in the
safety studies can be important to the safety effect findings.
If corrections for traffic volumes were not done at all, the expected number of crashes
and injuries in the after period on the roads, where bicycle tracks were constructed, would be
2,758 and 875, respectively. The comparable figures found when corrections for traffic
volumes were done, see Table 3, are 2-4 percent lower. This means that corrections for traffic
volumes result in a small worsening of the overall safety effect, i.e. the effect would be about
6 percent instead of about 10 percent as shown in Table 3. However if corrections for traffic
volumes were not done, the increase in bicycle-moped injuries would be 15 percent instead of
the 10 percent when these corrections were done. Here the corrections actually improve the
safety effect, because the bicycle traffic has increased. The difference in the safety effects
calculated respectively with and without corrections for traffic volumes are rather small.
Therefore, the results of the safety studies are not particular sensitive to the method for
making corrections for traffic volumes.
Bicycle tracks and bicycle lanes separate bicycle traffic from motor vehicle traffic on
links between intersections. Having these bicycle facilities is perceived to be safer and more
Søren Underlien Jensen 14
satisfying by bicyclists compared to a mixed traffic situation (18). Seen in this perspective,
the results of this study are somewhat controversial. Constructing bicycle tracks and marking
bicycle lanes in urban areas resulted in an increase in crashes and injuries of approximately
10 percent in Copenhagen, Denmark. Bicyclists’ safety has worsened due to these facilities.
On the other hand, making these bicycle facilities resulted in more cycling and less
motor vehicle traffic. This must have contributed to benefits due to more physical activity,
less air pollution, less traffic noise, less oil consumption, etc. A recent study shows that an
extra pedal cycled kilometer in Copenhagen gives an average gain in health and production
solely due to more physical activity of rather more than 5 DKK, which equals about 1 US$
(19). The positive benefits may well be much higher than the negative consequences caused
by new safety problems. It will be reasonable to sum up costs and benefits in order to identify
roadways that are relevant for implementing bicycle facilities.
Design of bicycle facilities clearly seems to have safety implications. The study has
revealed a few points in relation to this. However, it remains unclear whether it is possible to
design urban bicycle facilities so road safety is improved.
CONCLUSIONS
The main conclusions of the research reported in this paper can be summarized in the
following points:
1. A before-after traffic, crash and injury study of constructing bicycle tracks and
marking bicycle lanes has been completed taking into account changes in crash trends, traffic
volumes and regression-to-the-mean effects in the before period. Bicycle facilities are
predominantly made in order to provide bicyclists better travel conditions.
2. The weighted means or best estimates for safety effects of bicycle tracks in urban
areas are an increase of about 10 percent in crashes and injuries. This is due to a large
increase of 18 percent in intersections, which more than outweigh a small reduction on road
links between intersections. Pedestrians, bicyclists and moped riders safety at intersections
are significantly worsened. Results vary significantly from road to road.
3. One reason to this heterogeneity in safety effect between roads is that some bicycle
track designs are safer than others. Roads with bicycle tracks and parking permitted are safer
compared to roads with parking bans. Bicycle tracks than ends at the stop line at signalized
intersections with no turn lanes for motor vehicles should be avoided due to major safety
problems.
4. The best estimates for safety effects of bicycle lanes in urban areas are an increase of
5 percent in crashes and 15 percent in injuries. Safety is worsened both at intersections and
on links. Bicyclists safety has significantly worsened on the roads, where bicycle lanes have
been marked. More detailed traffic and design conditions were not studied in relation to
bicycle lanes.
5. The construction of bicycle tracks resulted in a 20 percent increase in bicycle/moped
traffic mileage and a decrease of 10 percent in motor vehicle traffic mileage on those roads,
where bicycle tracks have been constructed. The marking of bicycle lanes resulted in a 5
Søren Underlien Jensen 15
percent increase in bicycle/moped traffic mileage and a decrease of 1 percent in motor
vehicle traffic mileage on those roads, where bicycle lanes have been marked. This must have
contributed to benefits due to more physical activity, less air pollution, less traffic noise, less
oil consumption, etc.
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The Handbook of Road Safety Measures
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