PEDESTRIAN CROSSING BEHAVIOUR AT SIGNALISED CROSSINGS
Xu Hao
Sonal Ahuja
Majid Adeeb
Tom van Vuren
Mott MacDonald Ltd, UK
Michael G. H. Bell
Imperial College London, UK
Suku Phull
Department for Transport, UK
1. INTRODUCTION
This paper presents the findings from the stated preference survey section of
the research project that tested the propensity of pedestrians to comply with
signals when crossing signalised crossing at intersections and stand-alone
crossings(away from an intersection). The study was sponsored by the
Department for Transport UK and involved a research team that includes
academics from the Transport Research Unit at Imperial College and
transport planners and researchers from Mott MacDonald Ltd.
The survey also investigated pedestrian perceptions of safety, level of signal
clarity and confusion and other factors that influence levels of compliance at
signalised crossings. The survey also observed and recorded the actual
behavior of pedestrians who took part in the interviews. The crossing behavior
of the general total pedestrian population at each site was observed by a
video recording. The video survey data has yet to be analysed and it is
anticipated that the results will be utilised for further research and a potential
microsimulation model.
At this stage pedestrian behaviour and preferences have not yet been
evaluated to explain particular different pedestrian crossing situations (e.g.:
pelican and puffin crossings). User preference under hypothetical crossing
situations/scenarios has been identified through the application of a Stated
Preference survey technique.
2. BACKGROUND
Most town centres have heavy pedestrian flows. Traffic signal control has
sought to minimise vehicular delays, sometimes with priority for public
transport, while pedestrian flows have been fitted around the vehicular flow
demands. A typical example of this is the staggered pedestrian crossing,
© Association for European Transport and contributors 2008
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which splits the pedestrian crossing movement into two, allowing the green
time lost to vehicles to be minimised at the expense of delay incurred by
pedestrians who (if they comply) wait on a traffic island. This raises two issues:
•
Significant numbers of pedestrians fail to comply with the detour and delay
involved in a staggered pedestrian crossing, leading to unsafe crossing
behaviour.
•
Where the primary function of a junction is to allow pedestrians to cross,
local authorities may well wish to shift priority from vehicles to pedestrians.
In order to identify junctions where priority should be shifted to pedestrians it
is necessary to understand the crossing behaviour of pedestrians and their
perceptions of different crossing types as well as the effect of age, gender,
crossing with/without luggage or children. The use of particular pedestrian
crossing facilities could then be estimated. This requires the development of a
conceptual model before optimising parameters. However, there is at present
an inadequate understanding of pedestrian crossing behaviour, and in
particular how attractive or otherwise different types of pedestrian crossing are
perceived to be. This in turn depends on how different types of crossing are
used, and in particular how compliant pedestrians are to the intended crossing
routes and signals.
Recently the DfT has promoted the installation of ‘Puffin’ pedestrian crossings
which aim to decrease ambiguity for pedestrians and vehicles. This research
looks at how people respond to different types of signalised pedestrian
crossings such as pelican, puffin and toucan which take varying degrees of
pedestrian priority into account.
The paper is structured as follows: In Section 2, a literature review is
presented. The implications from the literature review help to determine the
influential factors which would affect pedestrian crossing behaviour and their
compliance with signals.
Following the literature review, the research objectives and methodology are
described in Section 3. In Section 4, the data collection is discussed, including
survey site selection, description and the summary of the field work. The
discrete choice modelling analysis and crossing behaviour forecast is
discussed in Section 5. Finally, Section 6 summarises the conclusions and
suggested future work intrinsic to this study.
3. LITERATURE REVIEW
The use of a stated preference technique in estimating pedestrian compliance
probabilities is a novel approach to this particular area of study but the survey
design has explored and benefited from intensive literature research
Considerable research has been undertaken in the very recent years
addressing the problem of pedestrian crossing behaviour, such as Hamed
© Association for European Transport and contributors 2008
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(2001); Sisiopiku and Akin (2003); Zeeduk and Kelly (2003); Keegan and
O’Mahony (2003); Ahuja (2007), etc. Beyond the pedestrian crossing
behavioural problem, studies on pedestrian perceptions and attitudes towards
facilities for pedestrians are reported in the literature. Among them recent
studies by Hine (1996), Hine and Russell (1996) and Russell and Hine (1996)
published the impact of traffic on behaviour and perceptions of safety by
pedestrians. Another study by Tanaboriboon and Jing (1994) reported the
attitudes of pedestrians in Beijing, China, towards the sufficiency of crossing
facilities and the willingness of pedestrians to use them. The study compared
signalised intersection pedestrian crossings to overpass and underpass
counterparts and concluded that users preferred the signalised crossings to
the overpass or underpass crossings.
The authors also reported that the levels of compliance with pedestrian
signals at two study locations were 70% and 57%. Rouphail (1984) performed
a user compliance and preference study on marked stand-alone crossings in
downtown Columbus, Ohio. The preference study indicated that users
perceived the un-signalised marked stand-alone crossings to be unsafe.
However, the same crossings were rated highest with respect to crossing
convenience. Pedestrian crossing compliance rates at the signalised and unsignalised stand-alone crossings were about 85%.
Similar crossing compliance studies were carried out in Europe. Pedestrian
push buttons at signalised crossings are commonly used to regulate
pedestrian crossing demand and to decrease conflicts between pedestrians
crossing and vehicles passing through designated crossings; hence, to
increase safety. Pedestrians are supposed to register their demand manually
by activating the push-button when they wish to cross a street in a conflictfree phase; however, they frequently do not do so (Carsten et al., 1998).
Davies (1992) observed pedestrian compliance with the pushbutton installed
at signalised crossings in the UK and presented that more than half of the
pedestrians did not activate the push button to cross. The compliance with the
device was 49% in a small town, while in London the rate was 27%. In
another location in Toulouse, push button compliance was as low as 18%
(Levelt, 1992). Jacobs, Sayer, and Downing (1981) compared road user
behaviour at traffic signals, uncontrolled pedestrian crossings and priority
junctions in a number of cities in developing countries with similar
observations in Great Britain.
Ahuja et al (2007) made a comparison study between two cities (Birmingham,
UK and San Francisco, USA) to identify any differences in pedestrian
behaviour and perceptions around signalised traffic intersections. Sisiopiku
and Akin (2003) present findings from an observational study of pedestrian
behaviour at various urban crosswalks and a pedestrian user survey which
sought pedestrian perceptions of various pedestrian facilities in a divided
urban boulevard located next to a large university campus, Michigan State
University. It was found that un-signalised stand-alone crossings were
preferred by pedestrians (83% reported a preference to cross) and also
showed high crossing compliance rate of pedestrians (71.2%).
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Besides previous studies covering general pedestrian crossing behaviour,
some studies focused on the crossing behaviour of particular. Bernhoft (2003)
carried out a risk perception and behaviour study of elderly pedestrians in
Denmark. The analysis indicates that the elderly observe and comply with
pedestrian crossings, signalised intersections and cycle paths significantly
more than do other groups. They are more likely to feel that it is dangerous to
cross the road where these facilities are missing. Furthermore, elderly
pedestrians find the presence of a sidewalk very important on their route
whereas the control group more often chooses the fastest route.
Zeedyk and Kelly (2003) intended to observe unobtrusively the behaviours of
adult-child pairs as they crossed at pedestrian crossings with signal control.
Results showed that the adults observed provided reasonably good models of
pedestrian behaviour, but that they rarely treated the crossing events as an
opportunity to teach children explicitly about road safety. The only gender
difference to emerge revealed that adults were more likely to hold girls’ hands
than boys’ hands. No differences were observed in relation to the (estimated)
age of child.
To sum up, through the literature review the following influential factors which
can affect pedestrian compliance with pedestrian signals are revealed:
•
•
•
•
•
•
Infrastructure of the pedestrian crossing facilities (physical layout; such
as refuge island, guard rail, etc.);
Age ;
Crossing status (unaccompanied or accompanied): if crossing with
children or with heavy luggage, pedestrians may show different
crossing behaviour;
Travel purpose (destination): shopping, home-to-work, school, etc;
Traffic conditions;
Wait time for “Green”
All these factors were considered in the study to understand pedestrian
crossing behaviour at signalised pedestrian crossings.
One of the biggest lacunae in past work has been that none of the studies
have been able to determine what causes people to take risk and not comply
with the red signal and their perception towards risk taking tendencies at
signals. In addition most traffic models assume total compliance at signals
which is unrealistic and leads to incorrect evaluation of pedestrian transport
schemes.
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4. RESEARCH OBJECTIVES AND METHODOLOGY
3.1 Research Objectives
The objectives of this study are to improve the understanding of pedestrian
crossing behaviour at microscopic level. Such work will feed into the
development of traffic signal control mechanisms which can account for
pedestrian crossing preference in determining optimum settings. It should also
help in modelling pedestrians more accurately at signalised crossing points
while taking their behaviour and traffic conditions into account.
3.2 Methodology
3.2.1 Survey Approach
In order to collect a comprehensive set of data the study was divided into two
main parts; Part One a programme of face-to face interviews and Part Two
comprising video surveys, running concurrently. Part one, the main part of the
study, concentrated on non-compliance such as pedestrian behaviour during
“red” or “blackout/flashing green” phases. For the “Pedestrian Green“signal
phase the survey measured the incidence of: pedestrians crossing within the
designated crossing area, running across the intersection and walking
diagonally across the intersection. The response of pedestrians to the green
signal going out was captured by video camera to facilitate detailed
observation and analysis in due course.
The face-to-face interviews investigated perceptions, reasons behind
compliance and non-compliance and also trade offs between crossing
scenarios and the perception of safety using a stated preference game. The
last section of the main survey also recorded respondents’ actual behaviour
and their observed profile.
The second part consisted of video surveys of each location on the survey day.
The purpose of this survey was to collect revealed preference data as input
for a future forecast model.
The face-to-face survey form is contained in Appendix 1. The questions
investigated various elements of behaviour: first of all, questions about actual
pedestrian crossing behaviour were presented to respondents, including
“where did they cross?”, “did they observe pedestrian signals?” etc. Some
attitudinal questions were also asked, such as perceived maximum waiting
time, attitudes to different pedestrian signal sequences, etc.
The results from the first part of the survey are beyond the scope of this paper
and can be found in the project report submitted to the DfT.
The second part of the questionnaire was the stated preference survey. The
results from this study are the main focus of this paper and have been
presented in detail.
© Association for European Transport and contributors 2008
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To investigate choice behaviour under hypothetical situations (particularly
when introducing some new attributes or new applications of pedestrian
crossings), a stated preference (SP) approach has proven to be successful in
transportation studies (Louviere et al., 2000). The advantages of the SP
methods for this project are:
It allows the modelling of new alternatives, attributes or variations in the
attributes of existing ones;
The degree of correlation and variation between attributes of different
signal control applications (or pedestrian facilities) may be controlled.
The stated preference games presented in the survey are binary choice in
nature.
Stated Preference Survey
The purpose of using a stated preference technique in this study was to
investigate pedestrian compliance in different pedestrian crossing situations
(such as signal settings, traffic volume, crossing alone/with luggage/with
children, etc.). When comparing with the revealed preference data obtained
from the first part of the survey, the SP data captures a broader array of
preference-driven behaviours in different pedestrian crossing situations.
Variations of attributes of a given pedestrian crossing facility are quite limited
in current applications, while SP data are particularly rich in their attribute
trade-offs accommodating a wider range of attributes that can be built into the
experiment, allowing model estimates from SP data to be more robust than
based on the RP data solely(Swait, Louviere and Williams, 1994).
At this stage pedestrian behaviour and preferences have not yet been
evaluated to explain particular different pedestrian crossing situations (e.g.,
pelican and puffin crossings). By, introducing the SP survey tool in this study,
user preferences under hypothetical crossing situations have been identified.
In the literature review of this project, some key factors that may influence
pedestrian crossing behaviour have been discussed. In previous studies,
characteristics of pedestrian (e.g., whether the pedestrian is accompanied by
child/children or with luggage when he/she crosses), was regarded as the
most important factor. Therefore, in the SP survey, the attribute of
“characteristics of pedestrian” was selected as the primary factor. The relevant
level for this attribute include, alone, with luggage and with child/children.
Secondly, because in reality some sites’ signals are set at “blackout” or
“flashing green” stage, and in order to identify pedestrian crossing behaviour
under different pedestrian signal settings, “signal status” was used as a
variable in the SP survey. Two levels were allocated for this attribute: “Red”
and “Blackout/Flashing Green”.
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The third attribute in the SP survey is “traffic volume”. Since the study has
focused on the combination of vehicular traffic and pedestrians at and around
signalised intersections, “traffic volume” may influence pedestrian behaviour
(compliance) and perceptions. Previous studies also focused on this factor
when looking at pedestrian compliance around the signalised intersection. In
most cases it has been observed that when traffic volume is low, most people
tend to cross on ‘red’. This behaviour however, may not be true if pedestrian
mobility is impaired or children are accompanied. For a broader
comprehension of this particular factor three levels of traffic volumes and
conditions - vehicles stopped, low traffic and high traffic - were identified and
offered as an attribute.
Finally, a factor related to the layout of the pedestrian crossing, the “refuge
island” was introduced in the stated preference game. Here the term “refuge
island” does not distinguish between the types - those which are part of a
staggered and those where pedestrians are not required to stop. Pedestrian
behaviour and compliance may be different where a refuge island is provided.
Two levels, “Yes”(refuge island exists) and “No”(refuge island does not exist),
were designed for this attribute (See Table 1).
Table 1 Attributes and Levels of SP Survey
Levels
0
Attributes
Characteristics of Alone
Pedestrians
Signal status
Red man
With luggage
With child/children
Blackout/flashing green
--
Traffic volume
Vehicles stop
Low
High
Refuge island
No
Yes
1
2
--
The SP games are binary-choice experiments. Each respondent was
presented with 9 binary choice situations by show cards. The respondent was
asked to choose one alternative he/she preferred (i.e., the situation in which
they would be more likely to cross) in each situation. An example of a SP
show card is presented below (Table 2).
Table 2 Example of SP Show Card
Situation A
Status of Signal:
Red
Traffic Volume:
Low
Refuge Island:
No
Situation B
Status of Signal:
Blackout/flashing
green
Refuge Island:
Yes
O I would more likely cross in situation A O I would more likely cross in situation B
All the surveys were administered by face-to-face interview that helped
maintaining the accuracy of the data and monitoring the response rate.
© Association for European Transport and contributors 2008
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Revealed Preference Survey
Besides the stated preference surveys employed in this study, actual
pedestrian crossing behaviour was also investigated during the interviews.
This enabled to compare the crossing behaviour and pedestrian compliance
with different crossing situations between reality and the hypothetical
scenarios presented in the SP game.
3.2.2 Discrete Choice Modelling
A widely adopted approach for discrete choice analysis is the logit model
(Ben-Akiva and Lerman, 1985), which is used for modelling a choice from a
set of mutually exclusive and exhaustive alternatives. It is based on the
Random Utility Theory (RUT) by McFadden (1981), which assumes that the
decision-maker chooses the alternative with the highest utility among the set
of alternatives. The utility of an alternative is determined by a utility function,
consisting of independent attributes of the alternative concerned and relevant
parameters. RUT considers that the analyst does not include the whole range
of factors influencing the choice and introduces a random error to account for
them. The random aspect is represented by decomposing the utility into two
components: a systematic term and an error term. The former can be
observed but the later one indicates all unknown factors that could influence
decision makers’ choices. Therefore the individual relative utility function can
be written as:
U in = Vin + ε in = ∑ β imn X imn +ε in
(1)
where: Uin: the utility of alternative i for individual n;
Vin: systematic term of attributes related to alternative i for individual n;
βimn: coefficients to be estimated;
Ximn: deterministic variables (attributes);
εin: a random disturbance term.
‘m’ is used to distinguish different variables “X”, say X1 and X2
The multinomial logit (MNL) model can be written as:
β X
expV
exp ∑
= j
Pin = j
β X
V
∑ exp ∑ exp ∑
imn
in
imn
jn
1
imn
(2)
imn
1
Because Equation (2) represents the individual choice probability, it can be
aggregated and used to forecast market share of different alternatives, for
example by using sample enumeration (DfT, 2004).
The advantages of the MNL are that it is relatively easy to estimate, the
coefficients are easy to interpret and the forecasts are generally quite robust.
© Association for European Transport and contributors 2008
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5. DATA COLLECTION
4.1 Site Selection and Description
The site selection process considered certain key objectives of the study.
Intensive desk research and discussions within the survey team determined
the final survey locations.
In the last decade emphasis has been placed on pedestrian priority over
vehicles at signalised junctions in the main town centres and pedestrian
intensive areas. Pelican crossing have become the main pedestrian crossing
application across the UK. In recent years, an improvement to the pelican
crossing has been introduced by puffin crossing.
The puffin crossing provides positive signalling - i.e. there is no “blackout” or
“flashing sequence” and drivers are held on a red signal while pedestrians are
crossing. The puffin crossing also has the ability to extend the crossing period
and cancel unwanted demands (request to cross). Another difference
between puffin crossing and pelican crossing is the location of the pedestrian
signal. Pedestrian signals are placed on the nearside to encourage pedestrian
to look at the approaching traffic to their nearest point of conflict whilst having
the signal in their field of view. Whereas Pelican pedestrian signals are placed
on the opposite side of the road.
The location of the pedestrian crossing area is another consideration in the
site selection. We looked at two different situations: pedestrian crossings at
junctions and stand-alone pedestrian crossings.
Factors of pedestrian crossing layout and infrastructure (such as “refuge
island”, guard rail, stagger, etc.) also influence pedestrian crossing behaviour
and preferences. Therefore, all sites also included differences in these
features.
In addition to the factors above, and in order to encompass pedestrian
behavioural heterogeneity and homogeneity, different cities were considered,
selecting three locations: Birmingham, Leamington Spa and London. The
details for each selected site are described in the following section:
Birmingham
The study area in Birmingham consisted of two sub-locations in the city centre.
One is situated around the Bullring Shopping Centre/Moor Street Train
Station/New Street Station. Around Bullring and Moor Street, pedestrian
crossings and pelican crossings. The pedestrian crossings close to New
Street Station generally have near side pedestrian signals.
The second location in Birmingham is situated at the junction of Great Charles
Queensway/Newhall Street featuring a signalised intersection with a far side
pedestrian
signal.
© Association for European Transport and contributors 2008
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Leamington Spa
The selected sites are located in the town centre, alongside the Parade. In
total four pedestrian crossings were studied; a mixture of puffin crossings,
signals with near side facility and detection.
Wandsworth Road London
In addition to Birmingham and Leamington Spa, two locations were selected
in London; one in Wandsworth Road, located south of the River Thames,
which is close to Vauxhall mainline and underground stations. Among the
eight selected pedestrian crossings, seven are puffin crossings and nearside
facilities at junctions including one toucan crossing.
Camden Town London
The second location in London was Camden Town; located alongside
Camden High Street, from Camden Town underground station to Mornington
Crescent underground station. The famous Camden Market is situated in this
area; hence there are intense pedestrian flows and demands at signalised
crossing, particularly on weekends. Most of the signals here are either puffin
crossings or signals with pedestrian facilities (far side pedestrian signals).
4.2 Fieldwork
After a set of consecutive and successful pilots the main fieldwork started on
28th February 2008 and ended on 22nd March 2008. The survey period
spanned a 7 hour period from 9:30 till 17:00. Based on experience of the
initial pilot the expected response rate was about 4-5 interviews per
enumerator per hour.
During this survey period, a sample of 899 completed surveys was achieved.
The sample size by site is illustrated in Table 3 below. The continuous adverse
weather conditions hindered the targeted sample size in Camden Town.
Table 3 Summary of the Field Work
Location
Sub Location
Sample
Birmingham
Bullring/New Street
Station
Great Charles Street
350
Leamington
Spa
Town Centre
198
Wandsworth Road
208
Camden Town
74
London
Survey Dates
Weekdays
Weekends
th
Feb
Sat
1st Mar
Thu 28
2008
2008
Wed 5th Mar Sat 8th Mar
2008
2008
th
Thu 13 Mar Sat 15th Mar
2008
2008
Thu 20th Mar Sat 22nd Mar
2008
2008
© Association for European Transport and contributors 2008
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6. MODELLING PEDESTRIAN CROSSING BEHAVIOUR
5.1 Model Estimations
The MNL model was estimated by ALOGIT 4.0. First of all, estimated
coefficients of the full data set were obtained. As illustrated in Table 4, in
general the sign and size of estimates are statistically valid and sensible. Two
dummy variables “characteristics of pedestrians” are negative, which means
that compared with the base (“cross alone”), the presence of these two
dummies decreased the individual utility of crossing the road. Therefore the
individual probability also decreased. Dummy variable 2 (“with child/children”)
is more negative than dummy variable 1 (“with luggage”), indicating that when
pedestrians are accompanied by child/children, they are less likely to cross.
Variables
Table 4 Model Estimation of Full Data
Estimation Parameters
1. Characteristics of Pedestrians
Dummy variable 1: with luggage
Dummy variable 2: with child/children
Base: cross alone
2. Status of Ped Signal (Dummy variable)
– Blackout/flashing green:
Base: Red
3. Refuge island (Dummy variable):
Base: No
Likelihood with cons:
Rho-squared w.r.t. cons:
No. of observations:
t-ratios
-0.4867
-1.6753
-10.41
-23.47
0.891
20.49
0.3403
10.94
-3108.4591
0.1898
5883
The estimation for status of pedestrian signal is positive when compared with
the base (“Red”), means that pedestrians are more likely to cross when the
pedestrian signal is “Blackout/flashing green”. Similarly, the estimation for
“refuge island” also can reflect pedestrian perception toward different crossing
layouts. When a refuge island existed, pedestrians are found more likely to
cross the road, since they perceive that the presence of a “refuge island” (a
mid way shelter) enhances safety.
All estimations are statistically significant when checking t-statistics of the
estimated coefficients. The Rho-squared value is used to check the goodness
of model fit. From the SP results a value of 0.1898 shows a reasonable model
fit (normally rho squared values ranging between 0.2 and 0.4 are viewed as
good model fits).
5.2 Measuring Pedestrian Crossing Behaviour
Pedestrian crossing behaviour has been measured by hypothecated attributes
such as various signal statuses with and without refuge islands. Pedestrian
crossing probabilities are calculated based on the estimated coefficients in
Section 5.1 and are presented at aggregate level.
© Association for European Transport and contributors 2008
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Tables 5 to 8 list pedestrian crossing probabilities by categorising different
crossing facilities. Four classifications of pedestrian crossings are applied in
the crossing behaviour forecast, according to the various pedestrian crossings:
•
•
•
•
Signalised junction with far side pedestrian signal
Stand-alone pelican crossing
Signalised junction with near side pedestrian signal(puffin)
Stand-alone puffin crossing
In addition, the following factors were considered in the crossing behaviour
forecast analysis so as to check the heterogeneity and homogeneity of
different groups of respondents.
•
•
•
•
Infrastructure of pedestrian crossing;(crossing with or without a ”refuge
island”)
Number of Pedestrian(single or two or more adults)
Signal status;(“Red” or “Blackout/Flashing Green”)
Pedestrian characteristics.(with or without luggage/children)
As illustrated in the Tables 5 and 6, pedestrian crossing probability is lower
under “Red” than under “Blackout/flashing green” signal status. When a
refuge island is introduced, pedestrian compliance with “Red” is estimated to
be slightly lower than without “refuge island”.
For a puffin crossing application, the pedestrian signal is designed differently
from a pelican crossing. Only two signal stages for pedestrians are applied:
“Red” and “Green” for a puffin crossing. Therefore, in Tables 7 and 8, only the
signal status of “Red” was taken into account when the crossing behaviour
was predicted. For pelicans, pedestrian propensity to cross on “Red” is higher
with a “refuge island” than without.
The results across the four tables indicate that the provision of a refuge island
is an important factor for pedestrian safety, as it influences pedestrian
crossing decisions and their compliance.
The analysis has also taken into account of other key factors influencing
pedestrian propensity of compliance at pedestrian signals. These factors
relate to the characteristics of pedestrians (e.g. whether a pedestrian is
accompanied by children, laden with heavy luggage, is with a group or alone).
The compliance of two or more adults without luggage/children is the lowest,
while pedestrians crossing alone with children are likely to be more compliant
compared with all other pedestrian categories.
In the following section the tables 7 and 8 present results from aggregated
data but by location rather than by type of signals. As certain features such as
“refuge island”, “blackout/flashing green” do not apply to every type of signals
but are experienced by pedestrian on daily basis. Hence these features were
presented to the respondents as hypothetical attributes.
© Association for European Transport and contributors 2008
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Table 5 Pedestrian Crossing Probabilities estimated from surveys conducted at Signalised Junctions with Far Side Pedestrian
Signals
Signalised Crossings
with far side Pedestrian
Signals
Refuge
Island
Yes
Signal Status
Red
Blackout
No
Red
Blackout
%
34.8%
65.2%
33.1%
66.9%
No of
Pedestrians
Single adult
20%
Two or more
adults
14.8%
Single adult
30%
Two or more
adults
35.2%
Single adult
17.9%
Two or more
adults
15.2%
Single adult
32.3%
Two or more
adults
© Association for European Transport and contributors 2008
%
34.6%
Characteristics of Pedestrians
%
Without luggage/children
With luggage
With children
Without luggage/children
With luggage
With children
Without luggage/children
With luggage
With children
Without luggage/children
With luggage
With children
Without luggage/children
With luggage
With children
Without luggage/children
With luggage
With children
Without luggage/children
With luggage
7.4%
7.2%
5.4%
5.5%
5.1%
4.2%
12%
10.5%
7.5%
14.1%
12%
9.1%
7.2%
6.5%
4.2%
4.9%
4.5%
5.8%
12.8%
10.7%
With children
Without luggage/children
With luggage
With children
8.8%
13.8%
11.9%
8.9%
13
Table 6 Pedestrian Crossing Probabilities estimated from survey conducted at Stand-alone Pelican Crossings
Stand-alone
Pelican
Refuge Island
Signal Status
Yes
Red
%
36.2%
No of Pedestrians
%
Single adult
20.6%
Two or more adults
15.6%
Flashing green
64.8%
Single adult
28.5%
Two or more adults
36.3%
No
Red
34.7%
Single adult
14.9%
Two or more adults
19.8%
Flashing green
65.3%
Single adult
31.1%
Two or more adults
34.2%
© Association for European Transport and contributors 2008
Characteristics
of
Pedestrians
Without luggage/children
With luggage
With children
Without luggage/children
With luggage
With children
Without luggage/children
With luggage
With children
Without luggage/children
With luggage
With children
Without luggage/children
With luggage
With children
Without luggage/children
With luggage
With children
Without luggage/children
With luggage
With children
Without luggage/children
With luggage
With children
%
7.5%
7.3%
5.8%
5.6%
5.2%
4.8%
11.4%
10%
7.1%
14.5%
11.5%
10.3%
5.6%
5.1%
4.2%
6.6%
5.9%
7.3%
12.3%
10.2%
8.6%
13.6%
12%
8.6%
14
Table 7 Pedestrian Crossing Probabilities estimated from surveys conducted at Signalised Junctions with Nearside Pedestrian
Signals (Puffin facilities)
Signalised junctions with
nearside pedestrian
signals
Refuge
Island
Yes
No
Signal
Status
Red
%
36.8%
Blackout/
flashing
green
63.2%
Red
33.2%
Blackout/
flashing
green
66.8%
NB: The features “refuge island” and Blackout/Flashing Green” have
tabulated results by location rather than signal type per se.
© Association for European Transport and contributors 2008
No of Pedestrians
Characteristics of
%
Pedestrians
Single adult
14.1% Without luggage/children
5.1%
With luggage
4.9 %
With children
4.1%
Two or more adults
22.7% Without luggage/children
8.8%
With luggage
8.1%
With children
5.8%
Single adult
27.1% Without luggage/children
10.6%
With luggage
10.1%
With children
7%
14.9%
Two or more adults
36.1% Without luggage/children
With luggage
10.6%
With children
10.6%
Single adult
13.2% Without luggage/children
4.8%
With luggage
4.6%
With children
3.8%
Two or more adults
20%
Without luggage/children
8%
With luggage
7.2%
With children
4.8%
Single adult
32.3% Without luggage/children
11.8%
With luggage
10%
With children
10.5%
13.9%
Two or more adults
34.5% Without luggage/children
With luggage
11.7%
With children
8.9%
been presented as hypothetical attributes and estimates represent cross
%
15
Table 8 Pedestrian Crossing Probabilities estimated from surveys conducted at Stand-alone Puffin Crossings
Stand-alone Puffin
Refuge
Island
Yes
Signal
Status
Red
%
36.6%
No of Pedestrians
%
Single adult
14.2%
Two or more adults
22.4%
Characteristics
of
Pedestrians
Without luggage/children
With luggage
With children
Without luggage/children
With luggage
%
4.9%
4.8%
4.5%
9%
8.3%
With children
5.1%
Without luggage/children
10.9%
With luggage
9.8%
With children
7.7%
Without luggage/children
14.7%
Two or more adults
35%
With luggage
11%
With children
9.3%
No
Red
33.4%
Single adult
13.1% Without luggage/children
4.7%
With luggage
4.7%
With children
3.7%
7.9%
Two or more adults
20.3% Without luggage/children
With luggage
7.1%
With children
5.3%
Single adult
34.5% Without luggage/children
12.4%
Blackout 66.6%
/flashing
With luggage
10.6%
green
With children
11.5%
Two or more adults
32.1% Without luggage/children
12.6%
With luggage
10.8%
With children
8.7%
NB: The features “refuge island” and Blackout/Flashing Green” have been presented as hypothetical attributes and estimates represent cross
tabulated results by location rather than signal type per se.
Blackout
/flashing
green
63.4%
© Association for European Transport and contributors 2008
Single adult
28.4%
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7. CONCLUSIONS
This study focused on pedestrian crossing behaviour to examine the
pedestrian compliance with signals under different crossing scenarios. The
key findings can be summarised as follows:
•
The estimated probabilities show a comparatively higher propensity to
start crossing during the “blackout/flashing green” phase than during “red”.
•
The provision of a “refuge island” gives a perception of safety but at the
same time increases the tendency to take a risk by crossing on red.
•
There is a higher propensity to cross on red in the following conditions:
o
o
o
o
•
At non-designated areas
In the age group under 30 years old
If there are two or more adults in the group
When without luggage/children
Pedestrian compliance is slightly lower at puffin crossing facilities than at
any of the other situations considered. This may be due the fact that puffin
crossing a relatively recent development compared with existing farside
pedestrian facilities, suggesting the need for publicity to raise awareness.
7. AREAS FOR FURTHER WORK
Our work leads to a number of recommendations for further study.
First of all, the analyses resulting from this work can feed into the
development of a traffic signal control mechanism which takes into account
pedestrian crossing behaviour when determining optimum settings.
Some further suggested areas for future work include:
•
Further detailed analysis of the video (CCTV) RP data and correlating
SP responses to RP data.
•
Investigation of the extent to which pedestrians understand the
operation/ sequence of different traffic signal types.
•
Advice on an education programme on safer crossing and the use of
roadside traffic signal infrastructure.
•
An awareness programme in schools educating children on puffin
crossings.
•
Exploration of incentives for compliance.
© Association for European Transport and contributors 2008
17
REFERENCES
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© Association for European Transport and contributors 2008
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ACKNOWLEDGEMENTS
This project was sponsored by the Department for Transport. The data
analyses were carried out by Xu Hao and Kokil Gupta, both at Mott
MacDonald. The opinions in the paper are those of the authors alone, and
may not be attributed to their respective employers.
© Association for European Transport and contributors 2008
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Appendix 1
Survey Questionnaire
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© Association for European Transport and contributors 2008
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© Association for European Transport and contributors 2008
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