Ingeniería 32(2): 111-128, Julio-diciembre, 2022. ISSN: 2215-2652. San José, Costa Rica
DOI 10.15517/ri.v32i2.50386
Esta obra está bajo una Licencia de Creative Commons. Reconocimiento - No Comercial - Compartir Igual 4.0 Internacional
Pedestrian crossing light violation in Costa Rica: exploring factors
affecting mid-block crossing behavior
Infracciones en los cruces peatonales en Costa Rica: explorando
factores que afectan el comportamiento
Enoc Araya-Porras
Project Engineer
SediCon A.S.
San Antonio, Alajuela, Alajuela, Costa Rica.
Email: earaya.ap@gmail.com
Andrey Mora-Calderón
Construction Engineer
Constructora Hernán Solís S.R.L
San Isidro, Pérez Zeledón, San José, Costa Rica.
Email: andreyr25@hotmail.com
Jonathan Aguero-Valverde
Professor
Programa de Investigación en Desarrollo Urbano Sostenible,
Escuela de Ingeniería Civil, Universidad de Costa Rica (ProDUS-UCR).
Barrio Profesores calle B#11, Mercedes, Montes de Oca, San José, Costa Rica.
Postal address: 11501-2060
Email: jonathan.aguero@ucr.ac.cr
Orcid: 0000-0002-9096-9274
Recibido: 11 de marzo de 2022 Aceptado: 27 de junio de 2022
Abstract
It is necessary to analyze pedestrian behavior at crossings to improve their safety and mobility. Mid-
block pedestrian crossings are structures that facilitate the mobility of pedestrians, safeguarding them from
vehicular trafc; however, illegal crossing by pedestrians is an everyday occurrence and represents a risk
to their safety. The purpose of this study is to evaluate the relationship between different human and road
factors and the decision to illegally cross signalized mid-block crossings. Several human factors such as
age, gender, waiting time in trafc light, use of the pushbutton and individual or group crossing, as well as
road characteristics such as the length of trafc light phase, length of crossing, and vehicular volume were
analyzed. To collect information about these variables, this study recorded a one-hour video in six selected
crosswalks within the Montes de Oca County in Costa Rica. A total of 1,707 crossings were recorded, 10.6
% of which corresponded to instances of illegal crossing. After applying a logit model, this research found
out that trafc volume, pedestrian red-light time, waiting time, vehicle illegal crossing and group crossings
reduced the probability of violations by pedestrians. On the other hand, minimum trafc light time and
crossing length increased the possibility of pedestrian illegal crossings. This study concluded that the trafc
Ingeniería 32(2): 111-128, Julio-diciembre, 2022. ISSN: 2215-2652. San José, Costa Rica DOI 10.15517/ri.v32i2.50386 112
light cycle is an important variable that must be rigorously analyzed to ensure pedestrian’s compliance with
trafc lights, which will improve the safety of the pedestrian mid-block crossings.
Keywords:
Pedestrian crossings, midblock crossings, pedestrian behavior, red-light running, illegal crossing.
Resumen
Es necesario analizar el comportamiento de los peatones en los cruces para mejorar su seguridad y
movilidad. El propósito de este estudio es evaluar la relación entre diferentes factores humanos y viales y la
decisión de cruzar ilegalmente cruces señalizados a mitad de cuadra. Se analizaron varios factores humanos
como la edad, el género, el tiempo de espera en el semáforo, el uso del pulsador y el cruce individual o
grupal, así como características de la vía como la duración de la fase del semáforo, la longitud del cruce
y el volumen vehicular. Para recopilar información sobre estas variables, se grabó video en seis pasos de
peatones seleccionados dentro del cantón de Montes de Oca en Costa Rica. Se registraron un total de 1 707
cruces, de los cuales el 10,6 % correspondieron a cruces ilegales. Después de aplicar un modelo logit, se
encontró que el volumen de tráco, el tiempo en rojo, el tiempo de espera, el cruce ilegal de vehículos y los
cruces grupales redujeron la probabilidad de infracciones. Por otro lado, el tiempo mínimo del semáforo y
la longitud del cruce aumentaron la posibilidad de cruces peatonales ilegales. Se concluyó que el ciclo del
semáforo es una variable importante que debe ser analizada rigurosamente para garantizar el respeto a los
semáforos por parte de los peatones, lo que mejorará la seguridad de los cruces peatonales a mitad de cuadra.
Palabras Clave:
Cruces peatonales, cruces de media cuadra, comportamiento peatonal, cruce ilegal
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1. INTRODUCTION
Pedestrians are the most vulnerable road users and important victims in road safety. According
to the WHO Global status report on road safety 2018, about 26 % of all road casualties are
pedestrians [1]. Furthermore, the percentage of pedestrian trafc deaths in Costa Rica was about
17 % between January 2021 and February 2022, down from about 25 % in 2012 [2].
Even though signalized crossings offer protection to pedestrians, most pedestrian crashes
occur at those facilities. For example, in Tokyo, a 60 % of pedestrian crashes occurred at signalized
crossings [3] and in China more than 50% [4]. This is expected since these crossings concentrate
signicant pedestrian and trafc volume which increases the chance of conicts. However, this
does not mean these numbers cannot be reduced by improving signalized pedestrian crossings.
Previous studies have reported that red-light violation by pedestrians (i.e. pedestrians
crossing the road illegally) is one of the main causes for pedestrian crashes at intersection areas
[5], [6] and [7]. Furthermore, King et al. [8] found that pedestrian illegal crossing was involved
in over 58 % of police-reported crashes at intersections in Queensland, Australia from 1996 to
2006 increasing the relative crash risk 8 times.
Several studies have analyzed pedestrian illegal crossings in countries like Germany [6],
United States [9], Turkey [10], India [11], and China [7]. However, it is well known that the
behavior of pedestrians is inuenced by several factors including environment, trafc, personal
and social characteristics [12]. Therefore, it is important to study pedestrian behavior at signalized
crossings in different regions and under different social environments; however, as far as the
authors know, there are not examples of such a study in Latin-American countries.
The purpose of this paper is to add to the growing body of literature on pedestrian behavior
by modeling pedestrian illegal crossing at signalized mid-block crossings in Costa Rica, using
logit models to evaluate different human and road factors. This paper is organized as follows: rst
the most relevant literature is reviewed, then the data and methodology are presented, next the
main results of the research are discussed and nally the main conclusions and recommendations
for future research are outlined.
2. LITERATURE REVIEW
Several studies have analyzed the variables affecting the decision process that encourage
pedestrians to violate the crossing signal. Illegal crossing decisions could be inuenced by several
elements such as trip purpose or time of the day, as found by Zhang et al. [7]. The authors also
found that some pedestrian attitudes such as the individual perception of defects on the road or
his or her urgency to cross, could affect pedestrian behavior at the intersection. Vehicular volume
affects decision making as well. For example, Onelcin and Alver [10] found that vehicle speed is
the most determinant variable in gap perception and crossing time. Lipovac et al. [13] observed
that the increase in trafc volume decreased the number of pedestrian offenders. Similarly,
Dommes et al. [14] noted that higher trafc density appears to inuence the way pedestrians
look toward the scene before and while crossing, but it may also have contradictory effects;
Ingeniería 32(2): 111-128, Julio-diciembre, 2022. ISSN: 2215-2652. San José, Costa Rica DOI 10.15517/ri.v32i2.50386 114
if waiting time increases, it may encourage pedestrians to violate the crossing light. However,
opportunities to cross between the trafc are rare, which can reduce the number of violations.
Characteristics in the surrounding environment and infrastructure also inuence pedestrian
behavior. Onelcin and Alver [10] showed a statistically signicant smaller number of offenders at
pedestrian crossings with countdown display than at pedestrian crossings without the countdown
display. The authors also found that there was a statistically signicant larger number of
pedestrian offenders at a signalized pedestrian crossing with the countdown display than a
pedestrian crossing without a countdown display during the red light’s 44–40 seconds interval.
Furthermore, Lipovac et al. [13] determined that the length of the pedestrian red light inuences
the distribution of offences, which is consistent with the results from Koh et al. [15], who found
that a person is more likely to violate the trafc signal when the crossing length decreases.
Brosseau et al. [16] suggested that the presence of a pedestrian signal of any type signicantly
decreases the probability of dangerous crossings and violations. They also found that a 10 %
increase in waiting time before light changes is associated with increases in the probability of
violation (no risk) and dangerous violation (risk related) in 7.9 % and 2.1 %, respectively. De
Lavalette et al. [17] suggested that the environment has to be interpreted in terms of its physical
characteristics (topographical features, infrastructure and control system) but also within the
context of the pedestrians’ primary task (i.e. going to school, going to work, leisure). Cinnamon
et al. [18] observed that the presence of a travel generator such as public transit hub, commercial
and residential areas, schools, and others, could encourage a pedestrian to commit a violation.
The effect that pedestrian individual characteristics have in the probability of illegal crossing
has been studied in several papers. Zhang et al. [7] did not nd statistical signicance for variables
such as gender, age, education, living, or income. On the other hand, Lipovac et al. [13] found
signicant differences in the pedestrians’ behavior at different age groups. Pedestrians between
18-40 years of age violated the red light more often than other ages. Furthermore, Koh et al.
[15] observed that male groups are more likely to violate the red light than female pedestrian
groups who have higher compliance to the red light according to Dommes et al. [14]. According
to Brosseau et al. [16] pedestrian behavior differs when pedestrians walk in groups. Group size
and pedestrian ow decrease the probability of violations being committed; reducing either
variable by 10 % separately will decrease the probability of violations by a maximum of 0.9
% and 0.6 %, respectively. Also, Koh et al. [15] observed that a person was found to be 246 %
less likely to violate the pedestrian light if he or she was with a companion compared to when
he or she was alone.
Ren et al. [4] analyzed pedestrian rate of compliance in terms of signalization and conditions
while crossing the street. They found that male pedestrians are more observant to trafc regulations
when crossing, elderly pedestrians are found to be the most abiding group but young pedestrians
are more likely to obey trafc law than middle-aged, and pedestrians in group tend to cross
on red more often than individual or paired pedestrians. The compliance rate was found lower
when the crosswalk was short. It also increases as the green light time increases or pedestrians’
volume increases. Finally, they found the main reason for crossing violations is “to save time and
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for convenience” while a small percentage of results attribute the violations to an unreasonable
conguration of pedestrian facilities.
3. DATA AND METHODS
There are 15 pedestrian signalized mid-block crossings in Montes de Oca, six of which
were selected for analysis due to time and budgetary constraints. This selection was based on
the observed pedestrian ow, crossing characteristics, environment, and vehicle volume. The
selected signalized mid-block crossings allow for a wide range of pedestrian and vehicular
volume while also providing different geometric and operating characteristics of interest in the
study. The location of the analyzed crossings is shown in TABLE I and Fig. 1.
TABLE I
DESCRIPTION OF SIGNALIZED MID-BLOCK CROSSINGS ANALYZED
ID County District Description
1 Montes de Oca Mercedes Sports Facilities University of Costa Rica, in front of Aqua Matic Laundry
2 Montes de Oca San Pedro Perimercado Supermarket at Vargas Araya, Chicago Bar, Pollos La Granja
Restaurant
3 Montes de Oca San Pedro Entrance to Agronomy Faculty University of Costa Rica
4 Montes de Oca San Pedro Faculty of Modern Languages University of Costa Rica
5 Montes de Oca San Pedro 07 Avenue - 61 y 63 Street (General Studies Faculty, Saprissa Building)
6 Montes de Oca Mercedes Distance State University (UNED), in front of Beta Plaza, Amanda’s Coffee
Fig. 1. Location of pedestrian crossings studied.
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3.1. Data Collection
Pedestrian and driver behavior were documented during one hour in each of the selected
locations, using a video camera. All videos were recorded during business hours (between 8 a.m.
and 5 p.m.). The order of the locations was randomly selected and recording in rainy days was
avoided to remove any possible bias related to the weather conditions. Fig. 1 shows the midblock
crossings modeled in this study. Fig. 2 presents a standard pedestrian crossing signal in Costa
Rica. The signal has two lights, a solid red light that indicates “do not cross” and a green “walking
person” that indicates “cross”.
The camera location was selected to allow the recording of pedestrian behavior on each side
of the road while also enabling the counting of vehicles. After the videos were recorded on each
location, analysis was performed to quantify all variables selected for the study. The same person
coded all the data from the videos to avoid inter-rater reliability issues.
For this study, a red-light violation is recorded when a pedestrian begins to cross the road after
the pedestrian light has changed to red. This denition is consistent with the legal denition of
pedestrian trafc violation in Costa Rica, but it might be different in other countries and regions.
Similarly, a driver red-light violation is recorded when the car crosses the pedestrian crossing zone
while the trafc light is red.
3.2. Selected Variables
The variables selected for the analysis are those that could be measured in the eld or obtained
through video observation. The study design was observational non-intrusive; hence, no questionnaire
was applied, and the location of the camera was selected to be as non-conspicuous as possible.
The purpose of this design was the capture a behavior of pedestrians as natural as possible. The
variables included in the analysis are:
Pedestrian gender: pedestrians were classied by gender, according to the estimation of a single
video observer. There were 1707 observations in total, with 823 corresponding to male and 884 to
female pedestrians.
Vehicle volume: all video recordings were one-hour long. The hour was divided into 15 minutes
segments for vehicle volume estimation. The number of vehicles counted on each 15-minute
interval was multiplied by four to obtain the number of vehicles per hour, which was linked to all
the pedestrians that cross during those 15 minutes.
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1
3
4
2
5
6
Fig. 2. Pedestrian signalized mid-block crossings analyzed in the study*.
* The number corresponds to the location shown in TABLE I and Fig. 1
Fig. 2. Typical pedestrian trafc light.
Age: The population sample of this study was separated by age, which allowed analyzing
how likely they were to illegally cross according to variables such as maturity, walking speed and
perception of danger. Pedestrians were grouped into four age groups according to the estimation
of a single video observer:
• Less than or equal to 18 years old.
• Between 19 and 40 years old.
• Between 41 and 60 years old.
• More than 60 years old.
Ingeniería 32(2): 111-128, Julio-diciembre, 2022. ISSN: 2215-2652. San José, Costa Rica DOI 10.15517/ri.v32i2.50386 118
Younger pedestrians tend to walk faster and might not perceive danger properly. Older-age
pedestrians walk slower, and their perception of danger can be more realistic, which makes them
cautious users. Among the less than 18-year old group there are young children, who mainly walk
together with an adult. Children were considered an independent observation only when walking,
not when they were being carried.
Vehicular trafc-light violation: the number of vehicles that went through the pedestrian crossing
while the vehicular light was red. Every vehicle running a red light was considered an individual
instance of vehicular illegal crossing for each pedestrian crossing at the same time.
Push-button activated: If the user pressed the trafc light button, it is considered as push-button
activated. If the pedestrian did not push the button but crossed in green it is also considered a push-
button activated, since there was no need to push the button. If the person did not press the button
and crossed during pedestrian red light, it is counted as no push-button not activated.
Group Crossing: this variable indicates if the person crossed alone or accompanied. Group
Crossing was dened by two or more pedestrians crossing together.
Pedestrian crossing length: this is the specic marked length of the pedestrian crossing, from
curb to curb.
Pedestrian Green Time: It is the amount of time the pedestrian green light is on.
Pedestrian Red Time: this is the minimum time that trafc light stayed on red for pedestrians.
The pedestrian crossing lights were all actuated, meaning that the light stays green for the cars and
red for the pedestrians until it is activated or actuated by pushing the light button. All actuated trafc
lights have minimum cycle lengths determined by the minimum green light for the main ow, in
this case the main ow is the vehicular ow and the minimum green time for the vehicles is the
same as the minimum red time for pedestrians or “pedestrian red time”. This time was measured
by pressing the button immediately after it went from green to red light. The longer the pedestrian
red time, the longer pedestrians must wait.
Minimum Response Time: the time the pedestrian trafc light takes to change to green after
it has not been activated for a time equal or longer than a complete trafc light cycle (pedestrian
green + red time), in other words, it is the response time to change to green after a long period on
red (for pedestrians). The longer the minimum response time, the longer pedestrians must wait.
Waiting Time: this variable represents the time that pedestrians waited since arriving to the
crossing point. It is measured from the moment the pedestrians stop at the crossing to the moment
they begin to cross the street. This time was recorded for both legal and illegal crossing cases. In
group crossings, the time was measured from the moment the rst pedestrian of the group arrived
at the crossing point. Therefore, all group crossing observations had the same waiting time as the
rst pedestrian who arrived at the crossing point.
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3.3. Data Analysis
The previously discussed variables were included in the analysis either as categorical or as
continuous variables. Explanatory variables were used to statistically predict, using a logit model,
the probability of illegal crossing at mid-block
3.4. Statistical Method
Given the binary nature of the response variable, pedestrian illegal crossings, a Logit model
was proposed. For the model, legal crossings (pedestrians that begin crossing the road while the
pedestrian light was green) are coded as zeroes and illegal crossings (pedestrians that begin crossing
the road while the pedestrian light was red) are coded as ones; hence, the model results show the
actual probability of illegal crossing (Pi). In the logistic regression equation, the natural logarithm
of the odds represents a logit transformation, where the Logit is a function of the covariates [19]:
(1)
where Yi is equal to zero if the pedestrian crossed legally and one if the pedestrian crossed
illegally, Pi is the probability of illegal crossing, β0 is the model constant and the β1, … βk are the
unknown parameters corresponding with the explanatory variables Xi,k. The unknown parameters
were estimated using maximum likelihood methods through R statistical software [20].
4. RESULTS
4.1. Demographic Characteristics
TABLE II presents the demographic characteristics of the sample studied. Of the 1,707
observations collected, 48.2 % were men, mostly between 19 to 40 years. From the 181 pedestrian
crossing violations reported, 53.6 % corresponded to men, which shows that men are overrepresented
on crossing violations.
TABLE II
DEMOGRAPHIC CHARACTERISTICS
TOTAL Men Women
younger than 18 12 8
between 19 and 40 764 839
between 41 and 60 41 26
older than 61 6 11
Total 823 884
Crossing Violations
younger than 18 3 1
between 19 and 40 88 77
Ingeniería 32(2): 111-128, Julio-diciembre, 2022. ISSN: 2215-2652. San José, Costa Rica DOI 10.15517/ri.v32i2.50386 120
Crossing Violations
between 41 and 60 6 6
older than 61 0 0
Total 97 84
4.2. Characteristics of mid-block pedestrian crossings
The characteristics of the crossings analyzed in this study include data related to length,
pedestrian green time, pedestrian red-time and minimum response time to change phase. These
characteristics are summarized in TABLE III. All the crossings were located in two-lane roads, one
in each direction which present mostly passenger vehicles and buses.
TABLE III
DESCRIPTIVE STATISTICS
Continuous variables mean S.D. max min
Crossing length (m) 11.93 4.64 20 7
Pedestrian Green Time (s) 19.17 3.31 22 15
Pedestrian Red Time (s) 56.17 5.64 65 50
Minimum Response Time (s) 13.67 4.08 21 11
Waiting Time (s) 38.18 14.40 60 0
Vehicles/hour 1065.30 279.44 640 1620
Categorical variables percentage
Illegal crossings 10.60
Male 48.21
Female 51.79
Group crossing 95.14
Vehicle trafc light violation 3.69
Push-button activated 92.16
4.3. Pedestrian and Driver Behavior
TABLE III summarizes the data on pedestrians. As shown on the table, more than 10 % of
pedestrians illegally crossed the streets on the study. Also notable is the fact that a high percentage
of pedestrians crossed in groups and that the mean waiting time is about 38 s, which is high. Another
important result in the data is the percentage of vehicle trafc violations. In 3.7 % of legal crossings
by pedestrians, vehicles encroached or crossed the pedestrian crossing area when their light was
in red, and the pedestrian was crossing. This illegal crossing by vehicles clearly increases the risk
of a pedestrian crash.
Of the total of pedestrians who crossed illegally, 26 % pushed the light button before they
decided to cross illegally. Waiting time of pedestrians varied depending on whether they waited or
not for the green light.
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4.4. Statistical Model
Variables were analyzed using a logit model and considering a condence level of 95 %. After
removing the non-signicant variables, the nal model is shown in TABLE IV.
TABLE IV
LOGIT MODEL FOR ILLEGAL CROSSING
Variable Coefcient Std.
Error Z P>|Z|* dy/dx Odds
Ratio
(Condence
Interval 95 %)
Vehicles/hour -2.918 0.737 -3.96 0.000 -0,068 0.997 0.996 0.999
Vehicle trafc light violation -1.373 0.467 -2.94 0.003 -0.032 0.253 0.101 0.633
Group crossing -3.054 0.555 -5.51 0.000 -0.071 0.047 0.016 0.140
Length 0.091 0.040 2.29 0.022 0.002 1.095 1.013 1.184
Pedestrian Red Time -0.211 0.072 -2.92 0.003 -0.005 0.810 0.703 0.933
Minimum Response Time 0.457 0.062 7.40 0.000 0.010 1.580 1.400 1.783
Waiting Time -0.152 0.010 -15.17 0.000 -0.003 0.859 0.842 0.876
Constant 10.789 3.479 3.10 0.002
*P<0.05 to make The P variable signicant.
From the model based on the data collected, the variables that were signicantly correlated to
the probability of illegal crossing were: vehicular volume, crossing length, minimum time from red
to green light phase, red light phase time, waiting time, group crossings and vehicle illegal crossing.
The probability of pedestrians crossing in red decreases when vehicular volume increases. The
expected percentage of illegal crossings decreases 6.8 % for each increase of a thousand vehicles per
hour, as shown in the marginal effects (dy/dx) column. This behavior is probably due to the higher
number of vehicles travelling on the street and smaller trafc gaps available, which discourages
pedestrians from crossing on red. This is consistent with the results from Lipovac et al. [13], which
mentioned that pedestrian behavior is inuenced by the trafc volume and the environment in
which the crossing is located. In cases where pedestrian crossings are located in zones with low
trafc volume, much larger vehicle gaps were experienced, so a larger number of pedestrians are
expected to cross in the red phase.
Fig. 3 presents the change on probability of illegal crossing versus vehicle volume, all the
other things being equal. From the gure, the effect of trafc volume is evident. The probability
illegal crossing decreased signicantly, even with modest increases in trafc ow. With volumes
of about 1300 vehicles per hour, the probability of pedestrians violating the red light decreased to
lower than 1 %. The darker line represents the expected probability of illegal crossing while the
light lines represent the 95 % condence interval.
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0,0%
1,0%
2,0%
3,0%
4,0%
5,0%
6,0%
7,0%
8,0%
9,0%
10,0%
600 700 800 900 1000 1100 1200 1300 1400 1500 1600
ytilibaborPgnissorClagellI
Vehicles / hour
Fig. 3. Illegal crossing probability versus vehicle volume.
The age variable is found to be non-statistically signicant to describe pedestrian compliance
in the dataset. As the analyzed data was collected mostly around the University of Costa Rica, most
pedestrians were between 19 and 40 years old (94 % of the sample). This data showed that only 6
% of the sample can be classied into different age ranges, therefore this variable does not have
enough variability to be of statistical signicance. According to [21], variables such as age and gender
introduced signicant variations to pedestrian behavior, particularly for young pedestrians from ages
17 to 25, who consistently appeared to violate trafc lights more often than adult pedestrians. This
idea is supported by Echeverry et al. [22], who determined in their analysis that the younger group
of the study (ages between 10 to 19) presented the most violations. In addition, the group older
than 59 years old constituted the group with less risk of suffering an accident caused by pedestrian
behavior. The study developed by Mohammed [23] showed that younger people tended to interrupt
their waiting time sooner than older users, which is consistent with the aforementioned studies.
Despite of the non-signicance of this variable, Fig. 4 shows how this sample behavior agrees with
the trend shown in those studies.
>60
41-60
19-40
Legal crossing
Illegal crossing
<18
0%
20%
40%
60%
80%
100%
Percentage of observations
Age Range (Years)
Fig. 4. Pedestrian behavior according to age.
According to statistical model, it is determined that gender has no inuence in pedestrian
behavior related to illegal crossing for the dataset analyzed, since the variable was non-statistically
signicant.
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Vehicles running the red light affected the probability of pedestrian illegal crossing signicantly.
The marginal effect shown in TABLE V demonstrated an absolute reduction of more than 3 % in the
probability of illegal crossing by pedestrians or a relative reduction of more than 75 % according to
the odds ratio. Pedestrians who witnessed a red light running by drivers or know that it is a recurrent
behavior in that crossing were more likely to wait for the pedestrian green light.
According to the model, as pedestrian crossing length increases, the number of pedestrian illegal
crossings increases too. This result was contradictory from behavior expected and from results
obtained in previous studies such as de Ren et al. [4] and Duduta et al. [9], since one might expect
that the shorter the crossing the faster the pedestrians can cross, and more trafc gaps could be
accepted. Fig. 5 shows how the probability of pedestrians violating the red light increases from about
2 % with 7 m crossings to more than 4 % with 20 m crossings. There may be some risk compensation
at play here since all the crossings were two-lane and therefore the wider road might have provided
space for vehicles to avoid pedestrians and pedestrians to avoid vehicles. Again, the darker line
represents the expected probability while the lighter lines represent the 95 % condence interval.
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
7 8 9 10 11 12 13 14 15 16 17 18 19 20
ytilibaborPgnissorClagellI
Crossing length (m)
Fig. 5. Illegal crossing probability versus crossing length.
Red light phase time or red time corresponds to the possible user waiting time for the next
green phase. Waiting interrupts both trafc and pedestrian ows, therefore the option of illegal
crossing under moderate risk became the appealing choice to not delay the trip. The fact that the
perception of time can vary among individuals, in addition to other factors such as low vehicular
volume and reduced risk perception cause some users to expose themselves to cross illegally. The
results show that in places with longer red pedestrian phase time there were fewer illegal crossings;
however, there could be correlation between vehicular volume and red time. Fig. 6 shows the effect
of red pedestrian phase time versus illegal crossing. Here the 95 % condence interval lines are not
presented since they are higher than 95 % percent and very close to zero percent due to an artifact
of the logit function.
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0,0%
1,0%
2,0%
3,0%
4,0%
5,0%
50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65
ytilibaborPgnissorClagellI
Red Phase Time (s)
Fig. 6. Illegal crossing probability versus red phase time.
Generally, short waiting times can be directly related to illegal crossing. A user who had waited
part of the red phase, had a greater probability to obey the trafc light than users just arrived at the
crossing and saw an opportunity to cross with conditions that seemed relatively safe. In this analysis,
the average waiting time for pedestrians who violate the red light is 17.4 s against 40.6 s for users
who waited for the green light. This result was consistent with other studies, as Brusseu et al. [16]
noted that users could be divided into two types, the ones who wait for the pedestrian green light
and the ones who cross right after they get to the crossing spot, even if the pedestrian light is red.
Previous studies noted that drivers who violate the red light are inuenced by time pressure and
social context [24]; one can expect this to be the case for pedestrians also.
Fig. 7 shows the effect of waiting time on the probability of illegal crossing, according to the
model. The gure indicates that many users who violate the trafc light usually make this decision
when they get to the crossing spot; therefore, they experience no waiting time.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 10 20 30 40 50 60
ytilibaborPgnissorClagellI
Waiting Time (s)
Fig. 7. Illegal crossing probability versus pedestrian waiting time
The minimum time the trafc lights take to change to green after long inactivity periods is
directly related to the probability of users to make the decision to cross before time. From the
analyzed locations, those that took more time to change to green light were the ones with the most
occurrences of illegal crossing. Fig. 6 shows that the probability of illegal crossings increased as
the minimum response time of trafc light became longer. As shown in the gure, the probability
ARAYA-PORRAS, MORA-CALDERÓN, AGUERO-VALVERDE : Pedestrian crossing light violation...
125
of illegal crossing increased rapidly with increases in the minimum response time. It went from
less than 1 % at 12 s to more than 12 % at 20 s.
0%
10%
20%
30%
40%
50%
60%
70%
12 13 14 15 16 17 18 19 20
ytilibaborPgnisssorClagellI
Minimun Response Time (s)
Fig. 8. Illegal crossing probability versus trafc light minimum response time.
It was also found that crossing as a group reduced the probability of illegal crossing. The
estimated odds ratio for group crossing was 0.047 which means that group crossing reduces the
probability of illegal crossing about 95 % compared to pedestrians crossing alone. Illegal group
crossing implies that all the members of the group simultaneously attempt to seize a trafc gap,
which is a collective behavior difcult to achieve and therefore unlikely. Considering the data
analyzed, only 27 % of illegal crossings were also group crossings.
Using the trafc light button to activate the pedestrian green light had not statistically signicant
effect on the probability of running the red light. Most of users that crossed during the pedestrian
red-light phase did not use the button; this behavior was related to the pedestrians’ perception of a
lower risk at the time of crossing likely connected to low vehicular volume. Out of 181 users who
illegally cross, 74 % did not press the button.
5. CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH
The analysis shows that the variables correlated to illegal pedestrian crossings at mid-block
crosswalks are: vehicular volume, crossing length, minimum time from red to green light phase,
pedestrian red phase time, waiting time, group crosses and drivers running the red light. This indicates
that the user behavior depends on both environmental and pedestrian characteristics.
Increases on vehicle volume, the number of vehicles running the trafc light on red and
pedestrian red light-phase time decrease the probability of illegal crossing. Increasing crossing
length and waiting time are correlated with increases on the probability of illegal crossings at the
analyzed locations.
Ingeniería 32(2): 111-128, Julio-diciembre, 2022. ISSN: 2215-2652. San José, Costa Rica DOI 10.15517/ri.v32i2.50386 126
The length of the red-light phase is statistically signicant, negatively correlated with illegal
pedestrian crossing. The fact that red pedestrian phase time disincentives illegal crossing seems
contradictory; nevertheless, it might be explained by the possible association to vehicular volume,
so this factor might not be attributed to patience from pedestrians.
Differences between the age and gender are not statistically signicant based on the analyzed
data. Similarly, the use of the trafc light button is a variable with non-statistical signicance.
Low vehicular volume induces a signicant reduction of the risk perceived by pedestrians,
possibly increasing the number of pedestrians violating the crossing light. In areas with low vehicular
volume, there are other options to be considered instead of trafc lights, such as speed reducers
with proper signalization, because existent infrastructure is underutilized.
Minimum response time is a very important variable in terms of possible engineering
improvements that can be applied to mid-block crosswalks. The probability of illegal crossing
increases rapidly when minimum response time increases. Hence, it is recommended to reduce the
minimum response time to the lowest safe value that includes yellow (for vehicles) and all-red phases.
Crossing length was also found to be directly correlated to illegal crossing. This result has
implications for engineering improvements. The crossing length can be shortened using curb
extensions which will both encourage pedestrians to comply to the trafc light and drivers to reduce
the speed. Also, curb extensions have the added benet of improving the visibility for pedestrians
and drivers.
It is recommended to perform similar studies considering more variability of pedestrian crossings
and larger sample sizes. This way, it may be possible to achieve more data representativeness
through different age groups, trafc volumes, crossing lengths and other variables that might be
correlated to the probability of illegal crossing. It is also important to consider the effect of time
that the intersection takes before crossing path is clearing and counting the number of pedestrians
who nish crossing right after the pedestrian trafc light changed to red.
Another variable that can be further explored is time of the day, since trafc and pedestrian
behavior can vary with it, especially considering peak and off-peak hours and night hours. In
addition, time of the day could be a surrogate for trip purpose in an observational design as the one
used in this research.
In future studies it would be important to consider the effect of refugee zones within crossing
structures in pedestrian behavior. There are different studies that indicate this characteristic of the
crossing structure as a modier of pedestrian behavior.
The observational non-intrusive design of this study has the advantages of being easy to
implement and reproduce but has the disadvantage of missing several important variables such as
trip purpose or familiarity with the crossing. To overcome some of these shortcomings a hybrid
ARAYA-PORRAS, MORA-CALDERÓN, AGUERO-VALVERDE : Pedestrian crossing light violation...
127
approach can be used where the pedestrian behavior is observed and a questionnaire is then applied.
However, this is a much more complex approach that requires signicantly more resources and time.
5. ROLES OF THE AUTHORS
Enoc Araya-Porras: Research, data curation, formal analysis, initial draft writing.
Andrey Mora-Calderón: Research, data curation, formal analysis, initial draft writing.Jonathan
Aguero-Valverde: Conceptualization, Formal analysis, initial draft writing, Writing - review
and editing.
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