The impact of pedestrian countdown signals on pedestrian-motor vehicle collisions: a reanalysis of data from a quasi-experimental study.
ABSTRACT To perform a more sophisticated analysis of previously published data that advances the understanding of the efficacy of pedestrian countdown signal (PCS) installation on pedestrian-motor vehicle collisions (PMVCs), in the city of Toronto, Canada.
This is an updated analysis of the same dataset from Camden et al. A quasi-experimental design was used to evaluate the effect of PCS on PMVC. A Poisson regression analysis, using a one-group comparison of PMVC, pre-PCS installation to post-PCS installation was used, controlling for season and temporal effects. The outcome was the frequency of reported PMVC (January 2000-December 2009). Similar models were used to analyse specific types of collisions defined by age of pedestrian, injury severity, and pedestrian and vehicle action. Incidence rate ratios with 95% CI are presented.
This analysis included 9262 PMVC, 2760 during or after PCS installation, at 1965 intersections. There was a 26% increase in the rate of collisions, pre to post-PCS installation (incidence rate ratio=1.26, 95% CI 1.11 to 1.42).
The installation of PCS at 1965 signalised intersections in the city of Toronto resulted in an increase in PMVC rates post-PCS installation. PCSs may have an unintended consequence of increasing pedestrian-motor vehicle collisions in some settings.
- SourceAvailable from: Ian Janssen[show abstract] [hide abstract]
ABSTRACT: The influence of the built environment on health is of contemporary societal interest. The design of streets in neighbourhood settings may contribute positively to the health of populations through increased physical activity, but it may also have injury consequences. We conducted a national cross-sectional study to describe the injury experiences of 9021 students from 180 Canadian schools that participated in the 2006 Health Behaviour in School-Aged Children survey. Street designs surrounding each school (5 km circular buffer) were estimated via geographic information systems for three established measures of connectivity (intersection density, average block length and connected node ratio). A composite scale of connectivity was derived using factor analysis. Multilevel logistic regression analyses were used to examine the associations between the composite connectivity measure and students' reports of physical activity injuries occurring in the street (street injuries). Students living in neighbourhoods with low versus high street connectivity reported possible increases in the occurrence of street injuries (OR, 1.38; 95% CI, 0.84 to 2.26). This relationship was mainly attributable to the occurrence of bicycle injuries (52% of all street injuries; OR, 2.33; 95% CI, 1.28 to 4.25). The population attributable risk was 20% for street injuries potentially caused by living in an area with low connectivity. The design of streets, as a measure of the built environment, is related to the occurrence of youth injury. Positive effects of poorly connected street designs that are likely in terms of physical activity were offset by negative injury outcomes, although the injuries observed were mostly minor in nature.Injury Prevention 07/2011; 18(2):81-7. · 1.76 Impact Factor
- Injury Prevention 03/2006; 12(1):1-2. · 1.76 Impact Factor
- American Journal of Public Health 10/2003; 93(9):1416-9. · 3.93 Impact Factor
The impact of pedestrian countdown signals on
pedestrianemotor vehicle collisions:
a quasi-experimental study
Andi Camden,1,2Ron Buliung,3Linda Rothman,1,4Colin Macarthur,1,5
Objective To determine whether pedestrian countdown
signals (PCS) reduce pedestrianemotor vehicle collisions
in the city of Toronto, Canada.
Methods A quasi-experimental study design was used
to evaluate the effect of PCS on the number of
pedestrianemotor vehicle collisions in the city of
Toronto, from January 2000 to December 2009. Each
intersection acted as its own control. We compared the
number of pedestrianemotor vehicle collisions per
intersection-month before and after the intervention.
Stratified models were used to evaluate effect
modification by pedestrian age, injury severity and
location (urban vs inner suburbs). Poisson regression
analysis with repeated measures (generalised estimating
equations) was used to estimate the RR and 95% CI.
Results The analysis included 9262 pedestrianemotor
vehicle collisions at 1965 intersections. The RR of
collisions after PCS installation was 1.014 (95% CI 0.958
to 1.073), indicating no statistically significant effect of
PCS on collisions. There was no evidence to suggest
effect modification between PCS and collisions by age,
injury severity or location.
Conclusion The installation of PCS at 1965 signalised
intersections in Toronto did not reduce the number of
pedestrianemotor vehicle collisions at these
Road traffic injuries are a major public health
concern, and account for approximately 1.2 million
deaths worldwide.1Pedestrianemotor vehicle colli-
sion injuries and fatalities disproportionately affect
the young (0e14 years of age) and elderly (over
65 years of age).2Pedestrian countdown signals
(PCS) are a relatively new intervention designed to
provide pedestrians with a numerical display indi-
cating the number of seconds remaining to cross
a street. Hypothetically, this information should
enable better decision making and safer road crossing
behaviour for pedestrians. In other words, PCS may
represent a cost-effective modification to the built
environment to create pedestrian-friendly spaces.
Proposed alternatives, such as healthy community
design and engineering countermeasures designed to
separate pedestrians from motor vehicles, reduce
speed and increase visibility, may be costly and time
consuming to implement.3e9The city of Toronto,
Canada, installed PCS at all intersections in the
city over the time period of November 2006 to
January 2011. The installation of PCS in Toronto
therefore created a unique opportunity for a ‘natural
experiment’ to evaluate PCS effectiveness.
Several studies have examined the effect of PCS
on driver behaviour10e16and pedestrian attitudes
and behaviour.10e13 17 18Only three studies have
included collision data, with mixed findings.10 11 19
Botha et al10reported no PCS effect; however, their
study was limited to a small number of intersec-
tions, few collisions and a short observation period.
Markowitz et al11reported a 52% reduction in the
number of pedestrian collisions post-PCS; however,
they also noted a similar decline in pedestrian
collisions at ‘control’ intersections. Pulugurtha
et al19reported a significant decline in the mean
number of collisions (car and pedestrian) post-PCS,
with the largest benefits noted at high crash and
high volume intersections.
This study examined the frequency of pedes-
trianemotor vehicles collisions before and after
the installation of PCS in the city of Toronto, over
a 10-year period. The main objective was to
determine whether PCS were associated with
a reduction in pedestrianemotor vehicle collisions.
The study took place in the city of Toronto,
Canada. The city of Toronto is Canada’s largest
city, with a population of 2503281.20The city of
Toronto was formed in 1998 through an amal-
gamation of metropolitan Toronto and six munic-
suburban part of a much larger urbanised region
called the greater Toronto area.21
A quasi-experimental study design was used. The
study design included a one-group (internal)
comparison in which each intersection acted as its
own control through comparison of the number of
pedestrianemotor vehicle collisions (per intersec-
tion-month) before and after PCS installation.
Ethics approval for the study was provided by
the ethics review board of the Hospital for Sick
PCS installation occurred citywide. Intersections
with controlled traffic signals where PCS were
introduced during the study period (January 2000
to December 2009) were eligible for inclusion.
Intersections were excluded from the analysis if
there was less than 6 months between the instal-
lation of a traditional traffic signal (controlled
traffic signal with a walk phase, flashing don’t walk
phase and a solid don’t walk phase) and installation
of a PCS. This exclusion reduced the likelihood of
measuring novel effects associated with the instal-
lation of a traffic signal. Exposure time was calcu-
lated using the number of months each intersection
1Child Health Evaluative
Sciences, The Hospital for Sick
Children, Toronto, Canada
2Division of Epidemiology, Dalla
Lana School of Public Health,
University of Toronto, Toronto,
3Department of Geography,
University of Toronto, Toronto,
4Institute of Medical Science,
University of Toronto, Toronto,
5Department of Paediatrics,
University of Toronto, Toronto,
6Division of Orthopaedic
Surgery, The Hospital for Sick
Children, Toronto, Canada
7Department of Surgery,
University of Toronto, Toronto,
8Department of Health Policy,
Management, and Evaluation,
University of Toronto, Toronto,
Dr Andrew Howard, The
Hospital for Sick Children, 555
University Avenue, Room S-107,
Toronto, ON M5G 1X8, Canada;
Accepted 7 November 2011
Published Online First
10 December 2011
This paper is freely available
online under the BMJ Journals
unlocked scheme, see http://
210Injury Prevention 2012;18:210e215. doi:10.1136/injuryprev-2011-040173
contributed to the study period before and after the installation
The outcome of interest was the frequency of reported
pedestrianemotor vehicle collisions between January 2000 and
December 2009. Data were extracted from motor vehicle colli-
sion reports filed by the Toronto police service and were obtained
from the city of Toronto, Transportation Services Division.
Pedestrianemotor vehicle collision records were excluded from
the analysis if: (1) the location code (eg, intersection/mid-block)
was missing; (2) the collision occurred on private property or in
a parking lot; (3) the collision occurred before a traditional signal
was installed at the respective intersection; (4) the collision
occurred outside a 30-m radius of the intersection where a PCS
was installed; (5) the collision occurred on the same day the PCS
was installed; and (6) the collision occurred at an intersection
where there was less than 6 months between the installation of
a traditional traffic signal and the installation of a PCS.
Age was included as a potential effect modifier to determine if
PCS were equally effective for all pedestrians. The following age
classes were used to conduct stratified analysis: 0e15, 16e59
and over 60 years.22Records with missing data for age were
excluded from the age-stratified analysis.
Injury severity was also gathered. Toronto police services
categorise injuries sustained from a collision into five types: no
injury; minimal (no medical attention required); minor (emer-
gency department treatment only); major (hospital admission
required); and fatal. Previous research has reported police
misclassification of injury is most likely to be associated with
minor injury.23e25In this study, minimal and minor injury
categories were combined.
Location was included to determine effectiveness by region.
Census geography from 1996 was used to geocode collisions as
occurring in either the urban or inner suburban parts of what is
now the city of Toronto. This stratification acknowledges
differences in urban design across strata. The inner suburbs are
a more recently constructed part of the city region, where auto
mode share for work and other activities is typically higher than
elsewhere in Toronto.26
Collisions and intersections with PCS were mapped onto the
city of Toronto centerline shapefile using latitude and longitude
coordinates using ArcGIS, ArcMap version 10. ArcGIS was used
to create a PCS intersection dataset and to attach collision data
to the set of intersections where PCS installation occurred.
All statistical analyses were conducted using SAS software,
version 9.1. Crude incidence rates per 1000 intersection-months
were calculated for all collisions and by strata, pre and post-PCS
installation. Poisson regression analysis with repeated measures
(generalised estimating equations) was used to estimate the RR
and 95% CI of collisions adjusted for clustering, as predicted by
PCS status (pre-PCS/post-PCS). To look for effect modification,
separate models were fit for total, each age group (0e15, 16e59
and over 60 years), each injury severity classification (no
injury, minor/minimal, major and fatal) and each location
(pre-amalgamated Toronto and inner suburbs). In all models, the
pre-PCS installation time period was specified as the reference
group. PCS, as a predictor of collision counts, was considered
statistically significant at p#0.05.
Intersections with PCS
From 20 November 2006 to 31 December 2009, 2078 PCS were
installed. Intersections where there was less than 6 months
between the installation of a traditional traffic signal and the
installation of a PCS (n¼113) were excluded from the analysis. A
total of 1965 intersections were included in the analysis.
Pedestrianemotor vehicle collisions
From 1 January 2000 to 31 December 2009, there were 23428
pedestrianemotor vehicle collisions in Toronto. Collision records
missing data for location (n¼2984), and collisions that occurred
in a parking lot or on private property (n¼289), outside a 30-m
radius from an intersection where a PCS was (eventually)
installed (n¼10486), before the installation of a traditional
traffic signal (n¼385), on the same day as the PCS installation
(n¼3) and at intersections where there was less than 6 months
between the installation of a traditional traffic signal and the
installation of a PCS (n¼19) were excluded, producing a final
sample of 9262 collisions. Table 1 provides a breakdown of
collisions by age, injury severity and location. There were 226
records with missing data for age; these were excluded from the
age-stratified analysis. The number of collisions per year was
plotted and no significant secular trend could be identified, either
in collisions occurring at intersections with PCS, or in total
pedestrianemotor vehicle collisions (figure 1).
Crude incidence and RR
Overall, the crude incidence rates (per 1000 intersection-months)
remained fairly stable pre and post-PCS installation, 40.73 and
41.30, respectively (table 1). When modelled, a RR of 1.014 (95%
CI 0.958 to 1.073) indicated no significant relationship between
PCS and collisions, after adjusting for clustering.
The stratified analysis by age revealed that the majority of the
collisions (n¼6482, 72%) occurred among adults 16e59 years of
age. Pre and post-crude incidence rates (per 1000 intersection-
months) were similar for this age group, 28.30 and 29.79,
respectively. When modelled, the RR (RR 1.038, 95% CI 0.972 to
1.108) revealed no effect of PCS on collisions among this age
group. Among the most vulnerable road users, children
(0e15 years of age) and older people (over 60 years of age), there
was a slight decrease in crude incidence rates; however, the RR
for children (0e15: RR 0.941, 95% CI 0.792 to 1.119) and older
people (over 60 years: RR 0.967, 95% CI 0.844 to 1.108) indicated
no significant effect of PCS on collisions.
The majority of collisions resulted in minor/minimal injury
(n¼7949, 86%). Crude incidence rates remained similar among
the pre and post-PCS periods for all types of injury severity.
When modelled, no significant effect of PCS was seen on colli-
sions of differing severity (no injury: RR 0.838, 95% CI 0.626 to
1.121; minor/minimal: RR 1.026, 95% CI 0.965 to 1.090; major:
RR 0.984, 95% CI 0.826 to 1.173; fatal: RR 0.968, 95% CI 0.594
The crude incidence rates by location revealed higher rates of
compared with the inner suburbs for both time periods;
however, when modelled, there was no effect of PCS on either
location (Toronto: RR 0.943, 95% CI 0.866 to 1.027; inner
suburbs: RR 1.042, 95% CI 0.967 to 1.122).
This study found no difference in pedestrianemotor vehicle
collision rates before and after the installation of PCS. Rates
were also similar pre and post-PCS installation when collisions
were stratified by age, injury severity and location.
Similar to Pulugurtha et al,19this study was restricted to
collisions that occurred at signalised intersections where a PCS
was eventually installed. Therefore, a reasonable explanation for
Injury Prevention 2012;18:210e215. doi:10.1136/injuryprev-2011-040173211
the findings is that while controlled traffic signals in general
(regardless of the signal head) are highly effective in reducing the
number of pedestrianemotor vehicle collisions,27the addition of
a PCS does not change the overall impact.
This study also found no negative impact of PCS on collision
counts, which is similar to the findings of Botha et al.10Where
the work of Botha et al10was limited by a small sample size, low
statistical power and a short post-PCS installation period, the
sample size of this study addressed these concerns.
The results reported contrast with findings from the work of
Markowitz et al.11Markowitz et al11reported a 52% reduction in
collisions post-PCS installation; however, this finding was based
on the analysis of nine high collision intersections; effect size
may have been partly due to regression to the mean. Collision
history was not a factor in the present study; in fact, some of
the intersections included did not experience collisions during
the study period. Pulugurtha et al19found PCS are most effective
at high crash and high volume intersections. This may explain
the differences in the effect size of PCS between studies. While
PCS appears to have no impact overall in the city of Toronto, it
is possible that the effect of PCS is different in some places than
Elements of the built environment are important to consider
when analysing the effectiveness of PCS. Location was included
in this analysis to understand the impact of PCS by urban
design. Many aspects of urban design affect pedestrian safety,
notably vehicle speed, which is further determined by driver
behaviour. Higher vehicle speeds are associated with an increased
risk of pedestrianemotor vehicle collisions and the severity of
pedestrian injury.28e31An 80% risk of pedestrian death has been
estimated when a vehicle is travelling at 50 km/h.28While it was
not possible to include reliable data on vehicle speed in this
analysis, we can comment on road density by type within each
location. Posted road speed and vehicle volumes increase up the
road type hierarchy, from local roads to expressways.32
We divided Toronto into two locations: pre-amalgamated
Toronto and the inner suburbs. Pre-amalgamated Toronto
includes the commercial downtown as well as higher density
residential neighbourhoods and pre-second world war traditional
neighbourhoods, whereas the inner suburbs include mainly
lower residential and commercial densities. Neighbourhoods in
the inner suburbs are closer in design to the post-war, car-
oriented suburban aesthetic; however, there are some high
locations were noteworthy for two road types.
Collector roads are designed to provide access to property and
move traffic (2500 to 8000 vehicles per day), have posted speed
limits of 40e50 km/h (40 km/h is more common in inner
suburbs)33and pavements on both sides of the road.32The most
notable difference in road density by type is for collector roads;
34.55 km/100000 population in the inner suburbs compared
with 19.82 km/100000 population in pre-amalgamated Toronto.
Major arterial roads are primarily designed for traffic move-
ment (>20000 vehicles per day), have posted speed limits of
50e60 km/h and pavements on both sides of the road (side-
walks).32There are slightly more major arterial roads in the
inner suburbs comparedwith
29.56 km/100000 population versus 24.28 km/100000 popula-
tion, respectively. Pedestrians in the pre-amalgamated (down-
town) region were exposed to a different built environment
compared with pedestrians in the suburbs. Without detailed
area-wide measures of walking we cannot comment on exposure
per se, but differences in road design, control and potentially
driver behaviour indicate that both PCS and other interventions
PCS analysis of pedestrianemotor vehicle collisions, Toronto, Canada, 2000e9
Total no of
incidence rate/1000 intersection-months
No of collisions
incidence rate/1000 intersection-months
40.73 (39.62, 41.66)
41.30 (39.40, 43.29)
0.958 to 1.073
4.87 (4.56, 5.20)
4.51 (3.91, 5.20)
0.792 to 1.119
28.30 (27.55, 29.08)
29.79 (28.18, 31.48)
0.972 to 1.108
Older people, 60+
6.51 (6.16, 6.89)
6.22 (5.51, 7.02)
0.844 to 1.108
1.81 (1.63, 2.02)
1.52 (1.19, 1.94)
0.626 to 1.121
34.87 (34.03, 35.73)
35.82 (34.05, 37.67)
0.965 to 1.090
3.58 (3.32, 3.86)
3.51 (2.99, 4.13)
0.826 to 1.173
0.47 (0.38, 0.58)
0.45 (0.29, 0.71)
0.594 to 1.578
53.03 (51.15, 54.98)
49.52 (46.21, 53.07)
0.866 to 1.027
35.46 (34.44, 36.50)
36.18 (33.94, 38.57)
0.967 to 1.122
*Reference group is pre-pedestrian countdown signal (PCS) intersections.
y95% Poisson CI.
zBase: n¼1965 intersections; location: pre-amalgamated Toronto (n¼622 intersections); inner suburbs (n¼1343 intersections).
212Injury Prevention 2012;18:210e215. doi:10.1136/injuryprev-2011-040173
could work differently here compared with the inner suburbs.
Although PCS was not an effective intervention in either loca-
tion, it is likely that any combined intervention (including PCS)
will be different in different traffic environments.
This study has several limitations. It is an exploratory
research study on the relationship between PCS and collisions in
Toronto. A parsimonious approach was taken to develop a model
to understand the RR between PCS and collisions. These models
do not adjust for potential confounders, such as changes in
pedestrian exposure. The use of collision rates per intersection-
month did not account for population differences between the
urban and inner suburban areas of the city of Toronto.
This study was not able to account for exposure measures
related to pedestrian and vehicle volumes. A strength of the
study conducted by Pulugurtha et al19was the inclusion of
vehicle volume data. These data would have been useful to
understand secular trends associated with walking and driving
behaviours or practices, but were not available in sufficient detail
for our analysis.
Data quality concerns have been documented for police
reported collision data.23 24 34 35Motor vehicle collision reports
are completed when there is an injury; therefore, it is likely that
collisions between pedestrians and motor vehicles not involving
injury are underrepresented in the data.35In addition, this data
source is limited to collisions reported to the police. Previous
studies conducted in the USA have estimated that police
reported motor vehicle collision data underestimate the number
of injured pedestrians involved in motor vehicle collisions by
approximately 21%.23This number remains similar among
paediatric populations in the USA (under 15 years of age), where
it is estimated that pedestrianemotor vehicle collisions are
underreported by 20%.25
Behavioural aspects, including changes in pedestrian and
driver actions and conditions, could not be captured by collision
pedestrianemotor vehicle collisions by
year, Toronto, 2000e9. PCS, pedestrian
Injury Prevention 2012;18:210e215. doi:10.1136/injuryprev-2011-040173 213
data. This means the frequency of ‘near misses’ was not
measured in this analysis. Observational studies examining
behavioural changes in response to PCS are important to provide
insight into how PCS are being used in the road traffic envi-
ronment, and how to make changes to improve PCS as an
intervention. The city of Toronto transportation services are
currently conducting an observational study of pedestrian
behaviour pre and post-PCS installation.
The results are based on controlled intersections where a PCS
was installed over the study period, and does not examine colli-
sions that occurred at adjacent, uncontrolled, unchanged inter-
sections or road segments in Toronto. To understand fully the
public health impact of PCS as a modification to the built
environment, all collisions in Toronto would need to be analysed.
Study strengths included the use of population-based data on
collisions. Outcome measure data over a 10-year period provided
adequate statistical power and permitted stratified analyses. The
use of secondary data, collected independently of the interven-
tion, prevented bias that may have influenced previous obser-
vational studies through observer effects and a lack of blinding.
The intersections included in the analysis comprised 95% of the
total eligible intersections, and are representative of Toronto.
The repeated measures design and one-group comparison
provided control over extraneous variables associated with the
geographical location of the intersection, including factors such
as posted road speed, land use mix, road type and other
The installation of PCS at 1965 signalised intersections in
Toronto did not reduce the frequency of pedestrianemotor
vehicle collisions at these intersections.
Reducing pedestrianemotor vehicle collisions at intersections
requires more than simply installing PCS. Other changes to
lights or their use might have safety benefits, for example,
increasing walking times, prohibiting drivers from turning on
red lights or allowing ‘pedestrian scrum’ crossings in all direc-
tions with no cars in the intersection.
More fundamental changes to cities, which intentionally
build pedestrian safety advantages into the environment, might
increase both the popularity and safety of walking. PCS may be
an important component of future strategies to make pedes-
trians safer in cities, but the Toronto experience does not suggest
that widespread installation of PCS alone will have important
benefits for pedestrian safety. Future evaluations of PCS
should incorporate elements of the built environment into their
Acknowledgements The authors would like to thank Michael P Brady (city of
Toronto) for providing the data, in addition to his assistance.
Competing interests None.
Ethics approval Ethics approval for the study was provided by the ethics review
board of the Hospital for Sick Children.
Contributors AC: Data assembly, model specification, data analysis, writing and
editing of the manuscript, final approval of the version to be published. RB: Data
assembly, contributed GIS expertise, model specification and interpretation, writing
and editing of the manuscript, final approval of the version to be published. LR: Study
design, data assembly, model specification, data analysis, writing and editing of the
manuscript, final approval of the version to be published. CM: Interpretation of models,
writing and editing of the manuscript, final approval of the version to be published. AH:
Study design, data assembly, model specification and interpretation, data analysis,
writing and editing of the manuscript, final approval of the version to be published.
Provenance and peer review Not commissioned; externally peer reviewed.
World Health Organization. Global Status Report on Road Safety: Time for Action.
Geneva: WHO, 2009.
Transport Canada. Pedestrian Fatalities and Injuries, 1992 to 2001. Fact Sheet TP
2436E. Ottawa, ON: Transport Canada, 2004.
Mecredy G, Janssen I, Pickett W. Neighbourhood street connectivity and injury in
youth: a national study of built environments in Canada. Inj Prev. Published Online
First: 1 July 2011. doi:10.1136/injuryprev-2011-040011
Stevenson M. Building safer environments: injury, safety, and our surroundings. Inj
Wilson RJ. Centering suburbia: how one developer’s vision sharpened the focus of
a community. Am J Public Health 2003;93:1416e19.
Retting RA, Ferguson SA, McCartt AT. A review of evidence-based traffic
engineering measures designed to reduce pedestrianemotor vehicle crashes. Am J
Public Health 2003;93:1456e63.
Constant A, Lagarde E. Protecting vulnerable road users from injury. PLoS Med
Tester JM, Rutherford GW, Wald Z, et al. A matched case-control study evaluating
the effectiveness of speed humps in reducing child pedestrian injuries. Am J Public
Pucher J, Dijkstra L. Promoting safe walking and cycling to improve public health:
lessons from the Netherlands and Germany. Am J Public Health 2003;93:1509e16.
Botha JL, Zabyshny AA, Day JE. Pedestrian Countdown Signals: An Experimental
Evaluation. Vol. 1, San Jose, CA; 2002.
Markowitz F, Sciortino S, Fleck JL, et al. Pedestrian countdown signals: experience
with an extensive pilot installation. ITE Journal 2006;76:46e8.
Schattler KL, Wakim JG, Datta TK, et al. Evaluation of pedestrian and driver
behaviors at countdown pedestrian signals in Peoria, Illinois. Transp Res Rec
Eccles KA, Tao R, Mangum BC. Evaluation of pedestrian countdown signals in
Montgomery County, Maryland. Transp Res Rec 2004;1878:36e41.
Nambisan SS, Karkee GJ. Do pedestrian countdown signals influence vehicle
speeds? Transp Res Rec 2010;2149:70e6
Huey SB, Ragland D. Changes in driver behavior resulting from pedestrian
countdown signals. 86th Annual Meeting, Transportation Research Board. Safe
Transport Education and Research Centre. 2007. http://escholarship.org/uc/item/
5g82b3r5 (accessed 27 Jul 2010).
Bundy B, Scrock SD. Modification of driver behavior based on information from
pedestrian countdown timers. Masters Abstracts International. Iowa State University;
Ames, Iowa, USA; 2008.
Noel EC, Arhin S. Evaluation of Countdown Pedestrian Signals in the District of
Columbia. Washington DC;2006.
Huang H, Zeeger C. The Effects of Pedestrian Countdown Signals in Lake Buena
Vista. Florida Department of Transportation;2000.
Pulugurtha SS, Desai A, Pulugurtha NM. Are pedestrian countdown signals
effective in reducing crashes? Traffic Inj Prev 2010;11:632e41.
What is already known on the subject
< Few studies have examined the relationship between PCS and
pedestrianemotor vehicle collisions.
< Previous research has produced mixed findings regarding the
effectiveness of PCS on pedestrian safety.
What this study adds
< This is the first population-based study to describe the
effectiveness of PCS at reducing pedestrianemotor vehicle
collisions, over a 10-year period.
< This study provides evidence that the installation of PCS in
Toronto, Canada, was not associated with a reduction in
pedestrianemotor vehicle collisions.
< Public health interventions designed to create pedestrian-
friendly cities should include additional components, as PCS
alone are not likely to have important safety benefits.
214Injury Prevention 2012;18:210e215. doi:10.1136/injuryprev-2011-040173
City of Toronto. Population & Dwelling Counts. 2006. http://www.toronto.ca/invest-
in-toronto/pop_dwell.htm (accessed 16 Feb 2011).
City of Toronto. Building the New City of Toronto: Three Year Status Report on
Amalgamation e January 1998eDecember 2000. Toronto, ON, Canada; 2011.
World Health Organization. World Report on Road Traffic Injury Prevention.
Geneva: World Health Organization, 2004.
Sciortino S, Vassar M, Radetsky M, et al. San Francisco pedestrian injury
surveillance: mapping, under-reporting, and injury severity in police and hospital
records. Accid Anal Prev 2005;37:1102e13.
Rosman D, Knuiman M. A comparison of hospital and police road injury data. Accid
Anal Prev 1994;26:215e22.
Agran PF, Castillo DN, Winn DG. Limitations of data compiled from police reports on
pediatric pedestrian and bicycle motor vehicle events. Accid Anal Prev
Data Management Group e University of Toronto. 2006, 2001, 1996 and 1986
Travel Survey Summaries for the City of Toronto. Toronto, ON: University of Toronto;
Garder P. Pedestrian safety at traffic signals: a study carried out with the help of
a traffic conflicts technique. Accid Anal Prev 1989;21:435e44.
Speed Management: A Road Safety Manual for Decision-Makers and Practitioners.
Geneva: Global Road Safety Partnership, 2008.
Anderson RW, McLean AJ, Farmer MJ, et al. Vehicle travel speeds and the
incidence of fatal pedestrian crashes. Accid Anal Prev 1997;29:667e74.
Garder PE. The impact of speed and other variables on pedestrian safety in Maine.
Accid Anal Prev 2004;36:533e42.
Roberts I, Norton R, Jackson R, et al. Effect of environmental factors on risk of injury
of child pedestrians by motor vehicles: a case-control study. BMJ 1995;310:91e4.
City of Toronto. Road Classification System. http://www.toronto.ca/transportation/
road_class/index.htm (accessed 30 Sep 2011).
City of Toronto. Toronto Bike Plan. Toronto, ON, Canada; 2001.
Farmer CM. Reliability of police-reported information for determining crash and
injury severity. Traffic Inj Prev 2003;4:38e44.
City of Toronto. Transportation Services, Traffic Data Centre and Safety Bureau.
Pedestrian Collision Study. Toronto, ON, Canada; 2007.
Fake pedestrian lane
Last April 1 (April Fool’s Day), a fake pedestrian lane was painted on sidewalks in Philadelphia to
persuade texting and distracted pedestrians to use it. Apparently, many believed it. The lane
marked off with paint included an icon of a pedestrian walking, holding a small glowing device.
The ‘E-Lane’ was intended to last during National Public Health Week.
Deaths of high-risk skiiers
I reported previously on the deaths of two leading Canadian skiiers, one of whom was killed
following a ski-cross jump. Many thought the death was preventable. Now a lawyer for the
family of one skier is alleging that there was ‘egregious negligence’ with respect to the design of
the landing area of the Swiss race course where he was killed. The lawyer characterised the
finish line as ‘a death trap’.
Canadian military suicides increase
The number of suicides in the Canadian military rose last year as soldiers returned from
Afghanistan. Although the actual numbers are not proof of an upward trend, and the rates are
lower than in the general population, the results are, nevertheless, disturbing. The total since
1996, 187, is more than the number killed in combat during the 10 years Canadians have been in
Injury Prevention 2012;18:210e215. doi:10.1136/injuryprev-2011-040173215