Illegal pedestrian crossing at signalised junctions in urban areas: The impact of spatial factors

Mayeux, Lovreglio, Saleh, and Fonzone

Illegal pedestrian crossing at signalised junctions in urban areas: The

impact of spatial factors

Mayeux Alexandre,

Transport Research Institute, Edinburgh Napier University

10 Colinton Road, Edinburgh, EH10 5DT, United Kingdom

e-mail: [email protected]

Phone: +33 (0)667791712

Lovreglio Ruggiero

Department of Civil, Environmental, Planning, Building and Chemistry, Politecnico di Bari,

Via Edoardo Orabona, 4, 70126 Bari, Italy




Phone: +44 (0)770 779 6624

Saleh Wafaa




Phone: +44 (0)131 455 2371

Fonzone Achille




Phone: +44 (0)131 455 2898

Total words: 5590

Diagrams/Figures/Tables 5 x 250 words 1250

Equivalent word count: 6840

Re-submission Date: 15/11/2014

mailto:[email protected]


ABSTRACT

Much has still to be done to increase pedestrian safety. Illegal crossing at signalised junctions

may increase the risk of accidents at the intersections where it takes place and induce

dangerous habits. Therefore efforts to prevent such a harmful behaviour must be put in place,

be based on an accurate understanding of the phenomenon.

The existing literature neglects the impact of spatial factors such as the position of the

pedestrians at the sidewalk or the possibility of having two-stage crossings. The paper

presents the result of a study developed to confirm the importance of the spatial factors and to

derive initial insights on their influence. To this aim pedestrian behaviour at a central

signalised junction in Edinburgh (UK) has been video recorded and the decisions of the

transgressors are studied through a logit model.

The findings show that the probability of illegal crossing is higher on the side of the

designated pedestrian crossing area. The decision to cross during the red time is influenced

by the width of the intersection and the presence of a refuge island. Recommendations on

further studies are developed based on the results of the research.

Keywords:

Pedestrian behaviour, illegal crossing, signalised junctions, spatial factors, herding behaviour


INTRODUCTION

Pedestrians are the most vulnerable road users because they lack the protection granted to

other types of road users by vehicles or specific equipment like helmets. In the UK 429

pedestrian fatalities occurred in 2010 (1). Although a positive trend can be observed – the

number of fatalities has halved from 858 in 2001 – this is still an unacceptably high cost of

the road mobility.

To increase pedestrian safety, marked crosswalks are commonly used to identify

preferred locations for pedestrian crossings. However, in many cases, particularly on multi-

lane roads with high vehicle speeds and large traffic volumes, marked crosswalks alone are

not sufficient to ensure pedestrian safety. In these cases zebra crossings are often associated

with other safety measures such as traffic signals. Although crosswalks and traffic signals

surely create safer environments for pedestrians, the number of pedestrian accidents at

crossing facilities is still large, and 30% of these accidents occur at signalised junctions.

Therefore, the safety level of pedestrian crossing in the presence of traffic signals must be

improved. This requires a better understanding of pedestrian behaviour.

Signalised crossings aim to reducing conflicts between pedestrians and vehicles.

Traffic signal plans are normally set so as to optimize the travel times of all or of specific

classes of road users and to ensure smooth traffic conditions. Safety is normally pursued by

including in the plans clearance times which let users complete their movements safely

before the movements of conflicting streams are allowed. However, often signal plans do not

meet users’ willingness to wait and this induces dangerous behaviours such as pedestrian

illegal crossing during red time. Illegal crossing can dramatically increase the probability of

accidents involving pedestrians as well as the severity of their outcomes.

Clearly illegal crossing generates safety problems only when it takes place in the

presence of conflicting vehicular streams. Nevertheless it should be discouraged in any case

to avoid that it becomes a habit automatically followed by pedestrians also in very dangerous

situations. It is sometimes anecdotally argued that pedestrians are more vigilant during illegal

crossing. Although this might be the case, pedestrians’ attention is not enough to guarantee

their safety, which depends also on other characteristics of the pedestrians themselves (such

their ability to react in case of danger) and on the interaction with vehicles (which in the

presence of traffic signals do not expect pedestrians when they are shown green signals).

Therefore understanding illegal crossing behaviour as a tool to develop recommendations for

junction and signal plan design is a priority for transport research.

The present work is a first attempt to investigate the influence on illegal crossing of

spatial factors such as the position of the pedestrian at the intersection. The study is based on

the observation of pedestrian behaviour at an important junction in Edinburgh city centre. To

single out the specific contribution of the spatial factors, other factors (wait time, traffic,

presence and behaviour of other pedestrians) are included in the analysis that have been

found relevant in the existing literature.

The paper starts with a brief review of relevant studies of pedestrian behaviour. Then

the case study is described, providing information on the junction and the collected data. The

paper continues with the description of the methodology used for the analysis. The result of

the proposed model are then presented. The paper ends with the discussion of the main

findings of the study.

LITERATURE ON ILLEGAL CROSSING BEHAVIOUR

Illegal behaviour of pedestrians at signalised junctions is a relatively new topic for transport

research. Previous studies have investigated the influence of several factors. Some studies

deal with the socio-demographic characteristics of the pedestrians. Using the theory of


planned behaviour, it has been proven that young pedestrians have a higher attitude to

commit violations than adult (2, 3, 4). Perception of and approach to risk have also been

explored. Sueur et al (5) study how culture influences the perception of risk of pedestrians.

They find out that the Japanese pedestrians are more cautious than the French ones. Levy (6)

observes that young pedestrians accept greater risks than the older ones. Similar findings are

found by Holland and Hill (7), who report that older pedestrians seem to be more careful

before attempting to cross. A possible explanation of the greater attentiveness shown by older

pedestrians is that they cannot always assess gaps correctly (8). Several studies have been

carried out to understand the influence of gender. Most of them conclude that males are more

likely to cross illegally (3, 6, 7, 9, 10). Men seem to be more influenced by the traffic flow

conditions, while the behaviour of other pedestrians at the junction has a greater effect on

women (11, 12).

Some researchers examine the role of distraction on the behaviour of pedestrians

while crossing (e.g. 13, 14, 15). Bunghum et al (13) argue that pedestrians who perform

other activities while crossing – such as eating, drinking or talking – tend to be less vigilant

increasing their exposure to accidents.

Rosenbloom (9) and Faria et al (10) study the effect of social influences on crossing

behaviour. They show that the probability of deciding to cross illegally decreases as the

number of pedestrian waiting at the sidewalk increases. However, the two studies provide

different results concerning the importance of the influence of the pedestrians who are

already crossing. Moreover, according to Faria et al (10) pedestrian are more likely to cross if

their close neighbour tries to do it.

The influence of signal plans is studied by different authors. For instance, some

studies prove that long red cycles increase the occurrences of illegal crossing (16, 17). Many

studies agree on the positive effect of countdown displays since they contribute to improve

the pedestrian experience of waiting (17, 18, 19).

The brief literature review provided above points out that most of the research

focusing on the effects of junction setting and layout on illegal crossing deal with temporal

factors (i.e. red cycle and remaining waiting time provided by countdown displays) rather

than spatial factors (i.e. location of the pedestrian, width of the carriageway etc.).

CASE STUDY

Junction

For this study, the pedestrian behaviour has been observed at the signalised T-junction

between Princes Street and St David Street in Edinburgh, UK (Figure 1. Note that being the

junction in the UK, the driving is on the left). The intersection is located in the city centre,

close to the main train station. The land use of the area is typical of CBD areas, characterised

by the presence of numerous commercial activities. There is no official statistic regarding

flows at the junction. The annual average daily flow in Princes Street is 6,591 motorised

vehicles east (where private cars are not allowed) and 5,211 west of the junction (20). Given

the configuration of the junction, pedestrians waiting at the sidewalk cannot see the aspect of

the traffic signals for vehicles in Princes Street, so they cannot easily predict whether and

from what direction vehicles turning in St David Street will arrive when the pedestrian signal

is red.

The study concerns the pedestrian flows along the West-East direction, parallel to

Princes Street. These flows are regulated by traffic signals. The length of the normal traffic

signal cycle is 112 seconds; the green for the considered pedestrian crossings lasts 10

seconds. Between the green and the red light for pedestrians, no information is shown for 3

seconds. A tram line has recently become operative along Princes Street. The signal plan is

modified when the tram passes and red is shown to all other users. At the end of the red, a


new cycle starts with green to vehicles, so that pedestrians have to wait more than three

minutes before start crossing the junction. This waiting time is very long, largely exceeding

the 60-90 seconds recommended by NACTO (21), and it could strongly influence the number

of occurrences of illegal crossings.

FIGURE 1 - Studied junction (Carriageway width for Zone 0 and 1 ~ 14.6m for Zone 2 ~

16.6m)

Observations

Crossings have been recorded by a video camera set either on the second or on the third floor

of the Scott Monument that surmounts the intersection. Recording has taken place between

10:00 and 14:00 in four days from Monday May 12th 2014. The weather during the

observation period was good.

Videos have been analysed off-line. Data has been extracted concerning only

pedestrians who cross illegally (transgressors). One log is introduced in the dataset every time

that a transgressor or a group of transgressors (transgressors are considered as a single

decision maker when they evidently walk together) is involved in a “relevant event”.

Relevant events are 1) arriving at the sidewalk, 2) observing other pedestrians crossing, 3)

deciding to cross. For each relevant event, the decision regarding crossing is recorded. For

instance, if a pedestrian arrives at the sidewalk and sees three pedestrians crossing at three

different times before he eventually decides to cross, 5 logs are added to the dataset:

One record when (s)he arrives

Three records capturing the reaction to other people’s transgressions (i.e. one for each

observed illegal crossing)

One record when (s)he decides to cross


Clearly the decision is “not crossing” in the first four logs and “crossing” in the last

one. As shown in Table 1, in the analysed dataset there are 471 transgressors and 2,455

relevant events. Three spatial factors have been considered: the position of the transgressors

at the junction, the direction of crossing, and the occurrence of two-stage crossings i.e. the

cases in which a pedestrian stops in the mid of the carriageway while crossing.

TABLE 1 Variables in the Dataset (m: mean value; sd: standard deviation)

Variables Descriptive

statistics

overall number of pedestrians 1145

number of transgressors 471 (41.1%)

number of logs in the dataset (i.e. observations of transgressor

decisions) 2455

personal

characteristics

gender - women 246 (52.2%)

men 225 (47.8%)

luggage whether the

pedestrian carries a large bag

no luggage 459 (97.5%)

luggage 12 (2.5%)

group whether the pedestrian walks alone

or together with known persons

single 339 (72.0%)

in a group 132 (28.0%)

waiting

time

interval between the arrival of the

pedestrian at the curb and the moment of decision

mb: 26,6 s

sdb: 32,9 s

spatial factors

location on

the

sidewalka

position of transgressors at the junction

(see Figure 1)

zone 0 292 (62.0%)

Zone 1 130 (27.6%)

Zone 2 49 (10.4%)

sidea direction of crossing from west 228 (48.4%)

from east 243 (51.6)

crossing in

two times

whether the pedestrian stops in the mid of

the carriageway while crossing

no 420 (89.2%)

yes 51 (10.8%)

traffic

interaction

traffic in

Lanes 1

and 2

no vehicle is present in Lane 1 or 2a no vehicle 93 (19.7%)

vehicles are present in Lane 1 or 2 and

stopped at the traffic signala vehicle stopped 356 (75.6%)

vehicles flow in Lane 1 or 2a vehicle driving 22 (4.7%)

vehicle

gap

time between the moment of the

decision and the time at which the

first conflicting vehicle crosses the junction

mb: 23,7 s

sdb: 22,7 s

vehicle

type

type of the first conflicting vehicle

crossing the junction after the moment of

the decision

car 1167c

small truck 33c

bus 562c

motorcycle 235c

vehicle

lane

position of the first vehicle crossing the

junction after the moment of the decision

Lane 1 (l1) 780c

lane 2 (l2) 669c

lane 3 (l3) 251c

lane 4 (l4) 297c

social

interaction

pedestrians

currently

crossing

number of crossing pedestriansa

same direction mb: 3.3 sdb: 3.7

opposite dir. mb: 2.9 sdb: 4.2

both directions mb: 6.2 sdb: 6.3

total of

crossings

seen by the

pedestrian

cumulative number of pedestrians who

have crossed illegally the junction since

the arrival of the decision makera

same direction mb: 5. sdb: 5.1

opposite dir. mb: 5.4 sdb: 4.4

both directions mb: 9. sdb: 9.0


pedestrians

at the

sidewalk

number of pedestrians waiting

on the sidewalka

same side mb: 10, sdb: 8,8

opposite side mb: 10,4 sdb: 7,9

a: at the moment of the decision

b: mean and standard deviation of the variable measured when the decision maker starts his/her crossing

c: number of interactions between pedestrians and vehicles

ILLEGAL CROSSING MODEL

Methodology

The behaviour of transgressors has been modelled as a binary decision using the random

utility framework. Random utility models (RUM) postulate that the decision makers facing

the problem of choosing among a discrete set of alternatives select the one with the maximum

utility. However, utilities are not exactly the same for all the decision makers and/or they are

not perfectly known to the modeller. Variations among subjects and non-perfect knowledge

are taken into account in RUM by assuming that the utility of the alternative i for the decision

maker q is equal to

𝑈𝑖𝑞 = 𝑉𝑖𝑞 + 𝜀𝑖 (1)

where 𝑉𝑖𝑞 is the deterministic (or systematic) component of the utility and εi its random

component. The systematic component is a deterministic function of the observable attributes

of the option i and of the decision maker q. The random component is specific of the option i

and cannot be observed. When the random components are assumed to be independently and

identically distributed following the Gumbel distribution, equation (1) generates a family of

RUM known as Logit models. Readers are referred to the large body of literature on RUM

for more information on the characteristics, the validity and the estimation of logit models

(e.g. Hosmer and Lemeshow (22) who provide a thorough derivation of the models).

Transgressors’ behaviour is modelled through a binary logit model in which the

dependent variable is equal to 1 when the decision-maker actually crosses, whereas it is 0

when s/he decides not to cross. The factors determining the systematic component of the

utility of the decision to cross illegally are found by testing the influence of the variables

presented in Table 1.

Model

Table 2 shows the model which best explains the observations. All the variables included in

the model have a p-value smaller than 0.05 and their presence increases the adjusted

McFadden R-squared statistic (adj-R2). Table 2 shows three statistics which allow evaluating

the goodness-of-fit of the proposed model: the likelihood L(0) of the simplest model

interpreting the observations, i.e. of the model including only a constant; the likelihood L(M)

of the proposed model; and adj-R2. The difference between L(0) and L(M), and the value of

adj-R2 show that the variables included in the model give a significant improvement over the

intercept model (23).

Given the definition of the dependent variable, in Table 2 positive coefficients

indicate that a factor increases the probability of deciding to cross illegally and vice versa.

Such a probability increases with the number of pedestrians illegally crossing from the same

sidewalk of the decision-maker (CGS), whereas it diminishes for higher numbers of

pedestrians waiting at the same sidewalk of the decision-maker (SWGS). The result

highlights that the decision to cross illegally is influenced by herding behaviour. This

behaviour, well known in literature, is more frequent in situations characterised by

uncertainty and lack of information about the choice (24). The influence of SWGS may also


be partly explained by the fact that the presence of other pedestrians create a physical

obstacle to walk and therefore it reduces the chances of illegal crossing.

TABLE 2: Coefficient estimates and statistics

Observations: 2455

L(0) = -1701.676

L(M) = -1124.420

adj-R2 = .337

Coeff. Std.Err. t-ratio P-value

CGS Number of pedestrians crossing in the same

direction as the decision-maker 0.0860 0.0160 5.3629 0.0000

SWGS Number of pedestrians who are waiting at the

decision-maker’s sidewalk -0.0528 0.0084 -6.3240 0.0000

GR

Dummy variable equal to 1 when the

decision-maker crosses with a group of

acquaintances

-0.4004 0.1237 -3.2368 0.0012

Z1 Dummy variable equal to 1 when the illegal

crossing starts in Zone 1 0.7677 0.1561 4.9183 0.0000

Z1F

Dummy variable equal to 1 when the illegal

crossing starts in Zone 1 and the decision

maker is female

-0.6993 0.2135 -3.2748 0.0011

STC1W

Dummy variable equal to 1 when at least one

vehicle is stopped at the light of Lane 1 and 2

and the pedestrian crosses from the West

-0.6934 0.1459 -4.7511 0.0000

STC1E


vehicle is stopped at the light of the first and

second lane (Lane 1 and 2) and the

pedestrian crosses either from East

-0.4779 0.1466 -3.2602 0.0011

STC2E


vehicle is crossing the junction in the first or

second lane and the pedestrian crosses from

the East sidewalk

-2.3903 0.7309 -3.2703 0.0011

WTSS Squared waiting time in seconds 0.0003 0.0001 3.9256 0.0001

CONST Constant -0.6212 0.1631 -3.8099 0.0001

The model shows that the probability of deciding to make an illegal crossing

decreases if the decision-maker is part of a group (GR). This could mean that the attitude to

illegal behaviour of a group depends on the attitude of the member of the group who is least

prone to cross during red time.

All other variables being the same, pedestrians show a greater inclination to illegal

crossing in Zone 1 (Z1). This disposition decreases if the pedestrian is a woman (Z1F<0).

Note that all the interactions between the gender and the other variables were tested but only

the interaction with Z1 is statistically significant. The role of side zones (Zones 1 and 2) is

discussed in more detail below.

As expected, the presence of vehicles waiting at the traffic signal in St David Street

(STC1) or crossing the junction in the first and second lane (STC2) decreases the inclination

to cross. The impact of these variables depends on the direction of the pedestrians. Vehicles

waiting at the traffic signal significantly influence pedestrians walking in either direction.

However, those crossing from West are more influenced as it is shown by the fact that the

absolute value of the coefficient of STC1W is greater than that of STC1E. This is because

pedestrians crossing from West cannot predict exactly whether the vehicles stopped in Lane 1

and 2 will be moving when they reach those lanes. This uncertainty reduces their tendency to


cross. The presence of vehicles in motion in Lane 1 and 2 is a deterrent only to decision-

makers coming from East. This might be explained by the fact that in most cases west-bound

pedestrians cannot avoid crossing the trajectories of the vehicles travelling in Lane 1 and 2.

Instead the pedestrians going towards East can stop halfway if there are still vehicles in

motion in Lane 1 and 2 when they reach the middle of the junction. In addition, the vehicles

travelling when the east-bound decision-makers start crossing may have cleared the junction

when the pedestrians reach Lane 1 and 2. The value of the coefficients of STC2E and STC1E

shows that the presence of vehicles in motion discourages west-bound pedestrians from

crossing more than the presence of stopped vehicles. The influence of the vehicles coming

from Princes Street have been studied by considering several potential critical values of gaps.

No significant result has been detected.

The last variable included in the model is the squared waiting time (WTSS). The sign

of the coefficient confirms that the longer the decision-makers wait at the sidewalk, the

higher is their propensity to cross illegally.

The model includes also a constant which is the value of the utility function when all

the aforementioned variables are 0. In this particular case (the decision-maker walks alone,

has just arrived at the junction, and starts crossing from Zone 0 or 2; there is nobody else

crossing or waiting; there is no vehicle waiting or crossing the junction in Lane 1 and 2), the

probability of deciding an illegal crossing is equal to 0.30.

Sensitivity analysis

A sensitivity analysis of the proposed model is provided in Figure 2. Figure 2(a) illustrates

the influence of other transgressors (CGS) and waiting pedestrians (SWGS) on the

probability of illegal crossing when all the remaining variables are set to 0. The probability

tends to 1 for high values of CGS (>45) and low values of SWGS (<15). In the opposite

situation (SWGS>45 and CGS<15) the probability is close to 0.

Figure 2(b) shows how the waiting time influences the probability of observing an

illegal crossing. Six cases are analysed, defined by the value of the variables GR, Z1,

STC1W, STC1E, and STC2E. The solid line refers to the case in which all the variables are

0. In the other cases, one value is set to 1 and the others to 0. The predicted probability of

deciding to cross illegally is always higher than 0.5 for pedestrians crossing from Zone 1

(Z1=1). The probability remains relatively low when there are vehicles crossing the junction

in Lane 1 and 2 (STC2E=1) and when the pedestrians cross from East (STC1W=1). Figure

2(b) also shows that the probability can become very close to 1 for high value of waiting

time.

(a)

(b)

FIGURE 2: (a) Combined influence of CGS and SWGS on the probability of having

illegal crossing; (b) Influence of waiting time on the probability of illegal crossing

0

21

42

0

0.2

0.4

0.6

0.8

1

0 6 12 18 24 30 36 42 48 54 60

CG

S

Pro

bab

ility

of

illeg

al c

ross

ing

SWGS

0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 20 40 60 80 100 120

Pro

bab

ility

of

illeg

al c

ross

ing

Waiting Time [s]

all dummy variables are equal to 0

GR=1

Z1=1

STC1W=1

STC1E=1

STC2E=1


INFLUENCE OF THE CROSSING ZONE

As already mentioned, the probability of illegal crossing increases when the pedestrians are in

Zone 1 (Z1>0). This inclination decreases if the pedestrian is a woman (Z1F<0). This result

has two potential explanations. Firstly, it can be argued that it is due to the presence of the

pedestrian refuge between lanes 2 and 3 in Zone 1. In fact, the decision-makers might be

encouraged to cross with red by the fact that they can stop in the island if there are potentially

conflicting vehicles in Lane 1 and 2 while they cross. This hypothesis has been tested by

considering the occurrences of illegal crossings in two stages. Two-stage crossings from

Zone 1 (17 cases out of 130 crossings) have been compared to the analogous behaviour from

Zone 0 (33 cases out of 292 crossings) using a Chi-Square test. The obtained p-value of 0.602

shows that there is no significant association between crossing in two stages and the zones.

The lack of relationship can have two reasons: on the one hand, the presence of the refuge

island creates a safe enough area also in Zone 0. On the other hand, pedestrians that start

crossing from Zone 0 have always the chance to change zone while crossing to use the

pedestrian island.

The second possible explanation of the result concerning Z1 is related to the physical

interactions among pedestrians. As discussed above, pedestrians waiting at the decision-

maker’s sidewalk may constitute a physical obstacle to cross. Generally Zone 1 attracts fewer

pedestrians because it is out of the area designated for crossing. Therefore crossing from

Zone 1 could be a strategy to avoid being hindered by other pedestrians. To test this

hypothesis we have compared the distributions of the overall number of pedestrian waiting at

the junction (SWGS) at the moment of the decision to cross for crossings occurring in Zone 0

and crossings occurring in Zone 1. The Kolmogorov-Smirnov test shows that both the

distributions are significantly different from the normal (Figure 3(a)). Applying the Mann–

Whitney U test (a non-parametric test more efficient than the t-test when the distributions are

not normal) it has been found that the distributions of SWGS for crossings in Zone 1 and

Zone 0 are significantly different. In particular, Figure 3(a) shows that the mean is higher

when the crossing starts from Zone 1 (means: Zone 1 = 15.0; Zone 0 = 9.5). In other words,

crossings in Zone 1 occur when the junction is more crowded. This finding supports the

hypothesis that transgressors may prefer from Zone 1 to avoid the physical impediment

caused by other people. One might wonder whether the same holds for Zone 2. The

distribution of SWGS when the illegal crossings take place in Zone 2 is not significantly

different from that of the illegal crossings occurring in Zone 1 (p-value of U-test=0.624).

This means that Zone 2 and Zone 1 becomes attractive to transgressors at the same level of

junction crowdedness. Interestingly Z2 (the dummy variable taking value 1 when the

transgressor uses Zone 2) is not included in the model in Table 2 because its effect is

captured by Z1. The similarity of the two distributions in Figure 3(a), and the non-

significance of Z2 indicate that when the junction is busy, transgressors migrate from the

central Zone 0 to the side Zones 1 and 2, possibly to enjoy better freedom of movement.

However the two side zones are not equivalent. In fact crossing in Zone 2 entails a longer

path than crossing in Zone 1. Furthermore pedestrians in Zone 2 cannot rely on a safe area

between Lanes 2 and 3. The longer crossing time caused by the longer crossing path, and the

absence of a refuge island make illegal crossings in Zone 2 more dangerous than those in

Zone 1. This explains why the number of illegal crossings in Zone 2 is smaller than that in

Zone 1 (49 vs 130). The difference between Zone 1 and Zone 2 is not found significant by the

logit model probably because the number of observations is not large enough.

The use of Zones 1 and 2 as buffers when the junction is crowded is confirmed by

Figure 3(b) that shows the distributions of the number of people waiting at the sidewalk when

the decision-maker arrives at the curb in each of the three zones. The Mann-Whitney test

(Zone 0 vs Zone 1: p-value: 0.000; Zone 0 vs Zone 2: p-value: 0.000) confirms that the


differences shown by the box-plots are statistically significant, i.e. that new arrivals prefer

Zones 1 and 2 when there are higher number of pedestrians waiting at the sidewalk.

(a)

(b)

(c)

FIGURE 3: (a) Number of pedestrians waiting on the sidewalk at the beginning of the

crossing for zone; (b) Number of pedestrians waiting on the sidewalk at the arrival of

pedestrians for zone; (c) Waiting time for zone


From Figure 3(c) it can be seen that transgressors tend to wait less before crossing

when they use Zone 1 (the waiting time is 17.6 sec or less for 1 transgressor out of 2) than

when they cross in the other two zones (the 50th percentiles are 30.7 and 23.5 sec for Zone 0

and 2 respectively). This confirms that Zone 1 makes illegal crossing easier. Note that the

25th percentile is 0 for all the zones, meaning that in all cases at least 1 transgressor out of 4

crosses the junction without stopping at all at the traffic signal.

DISCUSSION AND CONCLUSIONS

Although junctions have become safer for pedestrians in the UK in the recent years, they still

claim a very high cost in terms of pedestrian fatalities. Research efforts are needed to

improve the safety of crossing at signalised and non-signalised junction. This research has

focused on illegal crossing at signalised junction. While illegal crossing might not be

immediately related to larger numbers of accidents involving pedestrians, it can induce a

wrong habit leading to more hazardous pedestrian behaviour in any circumstance. Therefore

understanding and finding ways to prevent illegal crossing is an important goal for transport

research. The relation between illegal crossing, and number and severity of accidents is an

important topic for future research in the field.

Past studies of crossing at signalised junctions have identified the influence of socio-

demographic factors, distractions, herding behaviour, and wait times. This study has looked

into the impact of spatial factors such as the position of the transgressors, their direction, and

the possibility of dividing the crossings into two stages given the presence of a refuge island

in the middle of the junction. The main aim is not to provide recommendations to

practitioners but to confirm the relevance of spatial factors and to draw initial insights on

their influence to inform further research. The tendency to cross illegally has been studied by

observing the behaviour of pedestrians at a busy signalised junction in Edinburgh city centre.

The data concerning transgressors has been used to fit a binary logit model, in which the

dependent variable is equal to 1 when the decision-maker crosses illegally.

Regarding the spatial factors, the model proves that the probability of illegal

behaviour is higher in the lateral zones of the designated pedestrian crossing area. The side

areas are used as buffers by transgressors when the junction is crowded. The reaction of the

pedestrians to the presence of vehicles depends on their location. Pedestrians far from the

vehicles are less deterred to behave illegally by stopped vehicles than those on the opposite

side. On the contrary the former group of decision-makers is strongly discouraged to cross

when the vehicles are in motion but no significant impact of moving traffic has been detected

on the former group. As explained above, this difference of behaviour may be explained by

the concurrence of two factors: 1) having to cross two lanes before reaching the middle of the

junction, further pedestrians are less certain of the situation of the conflicting vehicles when

they will reach them. Hence they are more prone to expect that the stopped vehicles will be

moving and the moving ones will be stopped. 2) The presence of the refuge island encourages

crossings from the far side, because pedestrians know that they can stop there in case of

danger. It has to be noted that no significant difference has been found between crossing in

one stage and crossing in two stages. Of course this may due to the size of the sample. A

limitation of the adopted methodology is that it has provided insights only on the impacts of

the traffic coming from the road perpendicular to the crossing direction (St David Street),

while it has not been possible to draw any conclusion concerning the influence of the vehicles

coming from the parallel road (Princes Street).

Social interactions are found important as well. The probability of illegal behaviour

tends to 1 when the decision-makers observe many illegal crossings and there are few persons

waiting at their sidewalk. While the former result is surely a case of herding behaviour, the

latter is probably to be ascribed partly to the herding behaviour and partly to the greater


freedom of movement when there are few persons at the junction. Finally the research

confirms the well-known increase in the attitude to illegal crossing due to long wait times.

The study has confirmed the relevance of spatial factors for illegal crossing behaviour

but of course a single case study is not enough to draw a complete picture of such an

influence and so to provide junction and signal plan design recommendations. Further work

should concern junctions with different layouts (number of arms, width, refuge islands,

railings), traffic signal organisations (cycle times, implementation of pedestrian actuated

signals), vehicle and pedestrian flows. The influence of whether conditions should be also

investigated. It is important to develop the methodology so that the impact of all traffic

streams conflicting with the pedestrian movements are captured. The role of two-stage

crossing should be understood better, as well as that of the attempt of avoiding physical

obstacles in crossing using side zones. Data collection techniques should be improved so that

data can be recorded about the age (although socio-demographic features of the pedestrians

might not be relevant for the definition of regular standards, their influence is important in

particular cases such as, for instance, when junctions close to schools are designed) and the

level of attention of the pedestrians while crossing.

REFERENCES

1. Brandstaetter, C. Annual Statistical Report. Deliverable D3.9 of the EC FP7 project

DaCoTA, 2012

2. Evans, D., and P. Norman. Understanding pedestrians’ road crossing decisions: an

application of the theory of planned behaviour. Health Education Research 13, 1998,

pp. 481–489.

3. Moyano Diaz, E. Theory of planned behavior and pedestrians’ intentions to violate

traffic regulations. Transportation Research Part F: Traffic. Psychology and Behaviour,

Vol. 5, 2002, pp. 169–175.

4. Zhou, R., W. J. Horrey, and R. Yu. The effect of conformity tendency on pedestrians’

road-crossing intentions in China: An application of the theory of planned behavior.

Accident Analysis and Prevention, Vol. 41, 2009, pp. 491–497.

5. Sueur, C., B. Class, C. Hamm, X. Meyer, and M. Pelé. Different risk thresholds in

pedestrian road crossing behaviour: A comparison of French and Japanese approaches.

Accident Analysis and Prevention, Vol. 58, 2013, pp. 59–63.

6. Levy, D. T. Youth and traffic safety: the effects of driving age, experience, and

education. Accident Analysis and Prevention, Vol. 22, 1990, pp. 327–334.

7. Holland, C., R. Hill. The effect of age, gender and driver status on pedestrians’

intentions to cross the road in risky situations. Accident Analysis and Prevention, Vol.

39, 2007, pp. 224–237.

8. Oxley, J., B. Fildes, E. Ihsen, J. Charlton, and R. Days, Differences in traffic

judgements between young and old adult pedestrians. Accident Analysis and Prevention,

Vol. 29, 1997, pp. 839–847.

9. Rosenbloom, T. Crossing at a red light: behaviour of individuals and groups.

Transportation Research Part F: Traffic. Psychology and Behaviour, Vol. 12, 2009, pp.

389–394.

10. Faria, J.J., S. Krause, and J. Krause. Collective behavior in road crossing pedestrians:

the role of social information. Behavioral Ecology, Vol. 21, 2010, pp. 1236–1242.

11. Yagil, D. Beliefs, motives and situational factors related to pedestrians’ self-reported

behavior at signal-controlled crossings. Transportation Research Part F, Vol. 3, 2000,

pp. 1–13.

12. Tom, A., and M. A. Granié. Gender differences in pedestrian rule compliance and visual

search at signalized and unsignalized crossroads. Accident Analysis and Prevention,


Vol. 43, 2011, pp. 1794–1801.

13. Bunghum, T. J., C. Day, and L. J. Henry. The association of distraction and caution

displayed by pedestrians at a lighted crosswalk. Journal of Community Health, Vol. 30,

2005, pp. 269–279.

14. Hatfield, J., and S. Murphy. The effects of mobile phone use on pedestrian crossing

behaviour at signalised and unsignalised intersections. Accident Analysis and

Prevention, Vol. 39, 2007, pp. 197–205.

15. Nasar, J., P. Hecht, and R. Wener. Mobile telephones, distracted attention, and

pedestrian safety. Accident Analysis and Prevention, Vol. 40, 2008, pp. 69–75.

16. Van Houten, R., R., Ellis, and J.K., Kim. Effects of Various Minimum Green Times on

Percentage of Pedestrians Waiting for Midblock “Walk” Signal. Transportation

Research Record, Volume 2002 / 2007 Pedestrians, 2007, pp. 78-83.

17. Brosseau, M., N. Saunier, K. L. Mouel, and L. Miranda-Moreno. The impact of traffic

signals on dangerous pedestrian crossings and violations: a case study in Montreal.

Presented at 91st Annual Meeting of the Transportation Research Board, Washington,

D.C., 2012.

18. Lipovac, K., M. Vujanic, B. Maric, and M. Nesic. Pedestrians’ Behavior at Signalized

Pedestrian Crossings. Journal of Transportation Engineering, Vol. 139, 2013, pp. 165–

172.

19. Keegan, O., and M. O’Mahony. Modifying pedestrian behavior. Transportation

Research Part A, Vol. 37, 2003, pp. 889–901.

20. Government of the United Kingdom, Department for Transport. Traffic counts. http://

dft.gov.uk/traffic-counts. Accesed July 28, 2014.

21. National Association of City Transportation Officials. Urban Street Design Guide.

http://nacto.org/. 2013

22. Hosmer, D., and S. Lemeshow. Applied Logistic Regression. Wiley-Interscience, New

York, 2000.

23. Hensher, D. A., J. M. Rose, and W. H. Greene. Applied Choice Analysis: A Primer.

Cambridge University Press, New York, 2005.

24. Lovreglio, R., A. Fonzone, L. dell’Olio, D. Borri, and A. Ibeas, The Role of Herdng

Behaviour in Exit Choice during Evacuation, Presented at XI Congreso de Ingeniería

del Transporte (CIT 2014), Santander, Spain, 2014.

http://nacto.org/

Illegal pedestrian crossing at signalised junctions in urban areas: The impact of spatial factors

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