Identification of road safety measures for elderly pedestrians based on K-means clustering and hierarchical cluster analysis

Abstract

Introduction: Pedestrians aged over 65 are known to be a critical group in terms of road safety because they represent the age group with the highest number of fatalities or injured people in road accidents. With a current ageing population throughout much of the developed world, there is an imminent need to understand the current transportation requirements of older adults, and to ensure sustained safe mobility and healthy. Objectives: The aim of this study is to capture and analyze the key components that influence the identification of design solutions and strategies aimed at improving the safety of pedestrian paths for elderly. Method: A survey was conducted in 5 different locations in Catania, Italy. The locations were specifically chosen near to attraction poles for elderly pedestrians (e.g. centers for the elderly, squares, churches). Participants were recruited in person, so as to select exclusively people over 70. The sample comprised 322 participants. Both Hierarchical and K-Means clustering were used in order to explore which solutions elderly pedestrian propose for improving the safety of pedestrian path. Results: The results show that the judgment expressed by the elderly on the solutions for improving pedestrian safety is linked to the gender, to the experience as road users, and to mobility and vision problems. All solutions proposed regard road infrastructure (improvement of pedestrian crossings and of sidewalks, implementation of traffic calming measures, improvement of lighting), except for police supervision. Conclusion: This study has identified the factors that influence the identification of the best solutions to increase the safety level of pedestrian paths for elderly people. The aspects related to human factors considered were the gender, the factors associated with the experience as road users and the factors related to age related problems (mobility, vision and hearing problems). The results of this research could support traffic engineers, planners, and decision-makers to consider the contributing factors in engineering measures to improve the safety of vulnerable users such as elderly pedestrians.

Full text

ARCHIVES OF TRANSPORT ISSN (print): 0866-9546
Volume 56, Issue 4, 2020 e-ISSN (online): 2300-8830
DOI: 10.5604/01.3001.0014.5630
Article is available in open access and licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0)
IDENTIFICATION OF ROAD SAFETY MEASURES BY
ELDERLY PEDESTRIANS BASED ON K-MEANS CLUSTERING
AND HIERARCHICAL CLUSTER ANALYSIS
Salvatore LEONARDI1, Natalia DISTEFANO2, Giulia PULVIRENTI3
1, 2, 3 University of Catania, Department of Civil Engineering and Architectural, Catania, Italy
Abstract:
Introduction: Pedestrians aged over 65 are known to be a critical group in terms of road safety because they represent the
age group with the highest number of fatalities or injured people in road accidents. With a current ageing population
throughout much of the developed world, there is an imminent need to understand the current transportation requirements
of older adults, and to ensure sustained safe mobility and healthy. Objectives: The aim of this study is to capture and
analyze the key components that influence the identification of design solutions and strategies aimed at improving the
safety of pedestrian paths for elderly. Method: A survey was conducted in 5 different locations in Catania, Italy. The
locations were specifically chosen near to attraction poles for elderly pedestrians (e.g. centers for the elderly, squares,
churches). Participants were recruited in person, so as to select exclusively people over 70. The sample comprised 322
participants. Both Hierarchical and K-Means clustering were used in order to explore which solutions elderly pedestrian
propose for improving the safety of pedestrian path. Results: The results show that the judgment expressed by the elderly
on the solutions for improving pedestrian safety is linked to the gender, to the experience as road users, and to mobility
and vision problems. All solutions proposed regard road infrastructure (improvement of pedestrian crossings and of
sidewalks, implementation of traffic calming measures, improvement of lighting), except for police supervision.
Conclusion: This study has identified the factors that influence the identification of the best solutions to increase the safety
level of pedestrian paths for elderly people. The aspects related to human factors considered were the gender, the factors
associated with the experience as road users and the factors related to age related problems (mobility, vision and hearing
problems). The results of this research could support traffic engineers, planners, and decision-makers to consider the
contributing factors in engineering measures to improve the safety of vulnerable users such as elderly pedestrians.
Keywords: cluster analysis, elderly pedestrians, road safety, vulnerable users, human factor.
To cite this article:
Leonardi, S., Distefano, N., Pulvirenti, G., 2020. Identification of road safety measures
by elderly pedestrians based on K-means clustering and hierarchical cluster analysis.
Archives of Transport, 56(4), 107-118. DOI: https://doi.org/10.5604/01.3001.0014.5630
Contact:
1) salvatore.leonardi@unict.it [https://orcid.org/0000-0001-8533-0444]; 2) natalia.distefano@unict.it [https://orcid.org/0000-0002-1190-
1555] 3) giulia.pulvirenti@phd.unict.it [https://orcid.org/0000-0003-3518-3196]

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Leonardi, S., Distefano, N., Pulvirenti, G.,
Archives of Transport, 56(4), 107-118, 2020
1. Introduction
According to the United Nations’ World Population
Ageing report (ONU, 2015), nearly every country is
experiencing growth in the elderly population that
will create challenges and transformations in various
socioeconomic domains. In transportation, rapid
transition to the aged or super-aged society gener-
ates significant challenges in road safety, especially
in regard to elderly road users. Although older peo-
ple tend to travel less, the ability to move around
their community is important to them. They have
less need for travel connected to work, but like other
members of the community, older people travel for
social contact and to access services (Road Safety
Research Report, 2004). Recent research suggests
that the current cohort of older adults is healthier,
more affluent, and more mobile than previous gen-
erations (Chen and Millar, 2000). As such, older
adults are generating a greater demand for travel,
particularly for social and leisure activities (Zhou
and Lyles, 1997).
Older people are at heightened risk of pedestrian ac-
cident involvement, and especially of serious injury
or fatality. People over 65 years old represent 20%
of the EU population but account for as many as
47% of all pedestrian deaths. As is the case with pe-
destrian deaths, a large proportion of seriously in-
jured pedestrians in the EU are people above 65
years old, accounting for 30% of all seriously in-
jured people (PIN Flash Report 38, 2020).
With a current ageing population throughout much
of the developed world, there is an imminent need to
understand the current transportation requirements
of older adults, and to ensure sustained safe mobility
and healthy and active lives. Especially in light of an
ageing population, making walking safer will have
to take account of the specific needs of older people.
However, public transportation and walking envi-
ronments are not always well-adapted to accommo-
date the elderly. In order for walking to become an
attractive, efficient, and safe mode of transportation
for the elderly, the way public spaces are designed
must be rethought/reconsidered in order to accom-
modate to their needs and preferences.
A specific peculiarity of elderly pedestrians are age-
related declines. Age-related declines in perceptual,
cognitive, and physical abilities have been shown to
contribute to the high rate of fatal or serious-injury
crashes found for elderly pedestrians (Dunbar et al.,
2004). However, few researches have focused on the
relationship among elderly’s age-related declines in
perceptual and physical abilities and their perception
and opinion of pedestrian paths. In order to help pre-
vent elderly pedestrian accidents, it is necessary to
answer questions about how they perceive pedes-
trian paths with respect to their age related declines
in perceptual and physical abilities and with respect
to their experiences as road users. This paper pre-
sents the result of an analysis developed considering
the case study of the urban area of Catania (Italy).
The final aim of this study is first of all to understand
which solutions elderly pedestrian identify as solu-
tions to the critical issues of the pedestrian paths
they usually walk. Moreover, this study seeks to an-
alyze how elderly pedestrians’ age related declines
in perceptual and physical abilities (vision, hearing
and mobility problems) and experiences as road user
(no driving license, no still driving, accidents driv-
ing, accident pedestrian) can affect their opinion on
the solutions to the critical issues of pedestrian
paths. The intent of this approach was to allow older
people’s voices to broaden our understanding of
neighbourhood walkability and to understand their
priorities. This is important to determine interven-
tions and could support traffic engineers, planners,
and decision-makers to consider the contributing
factors in engineering countermeasures.
2. Literature review
The rapid growth and development in urban areas
has resulted in a drastic increase in human popula-
tion as well as vehicular population in most of the
metropolitans across the globe. Due to this, there is
an unavoidable increase in conflicts between vehic-
ular traffic and pedestrians often sharing the same
road space (Thakur and Biswas, 2019). No matter if
the primary mode of transportation is the automo-
bile, bicycle, or public transit; people must walk as
a part of the trip, such as from their home to the store
or place of employment, and/or to the transit stop.
Pedestrians are generally vulnerable compared to
other road users (Vorko-Jović et al., 2006). A num-
ber of road safety researchers elsewhere have iden-
tified various roadway, traffic and environmental
factors influencing the injury severity of pedestrians
(Montella et al., 2011; Ghasemlou et al., 2015; Gru-
den et al., 2019; Campisi et al., 2018; Distefano and
Leonardi, 2019). In particular, the road intersections
are real “black spots” for pedestrian accidents
(Canale et al., 2015).

Leonardi, S., Distefano, N., Pulvirenti, G.,
Archives of Transport, 56(4), 107-118, 2020
109
It is well known that older road users, both drivers
and pedestrians, have a heightened accident risk
compared to younger road users (Staplin et al., 2001;
Tollazzi et al., 2010). Numerous pedestrian studies
identified old age as one of the major risk factors of
severe injury or fatality (Abay, 2013; Eluru et al.,
2008; Kim et al., 2017; Pour-Rouholamin and Zhou,
2016). Lot of studies investigated the issues of ac-
cessibility of urban areas for vulnerable road users
(Giuffrida et al., 2017a; Giuffrida et al., 2017b;
Benenson et al., 2011) and for people with motor
and visual disabilities (Mrak et al., 2019). Individu-
als with disabilities may include individuals with
mobility disabilities, using wheelchairs, walkers or
canes, individuals who are blind or who have im-
paired vision, individuals with cognitive impair-
ments from developmental disabilities, stroke or
brain injury, and others. Individuals with disabilities
may be the most vulnerable users of transportation
facilities. Many are unable to drive and are depend-
ent on public transit and pedestrian facilities to travel
to work and to family, shopping, medical, and recre-
ation destinations. The safety of persons with disa-
bilities as road users is often dependent on the design
of sidewalks and street crossings for usability and
safety. Many people change their routes, or use par-
atransit services, or do not travel at all in response to
poor roadway facilities. Safety of persons with disa-
bilities is an essential part of improving roadway
safety. Age related declines in perceptual, cognitive,
and physical abilities have been shown to contribute
to the high rate of fatal or serious-injury crashes
found for elderly pedestrians (Road Safety Research
Report, 2004). Because of age-related perceptual,
cognitive, and motor limitations elderly pedestrians
are expected to experience more difficulty than
young pedestrians (Oxley et al., 1997; Fontaine and
Gourlet, 1997).
Yee (2006) showed that the fatality rate of the pe-
destrians aged 65 and above was almost double that
of the younger group. Elsewhere (Sklar et al., 1989),
the fatality rate of elderly pedestrians was reported
to be 5 to 6 times higher than that of their younger
counterparts. There exist several recent studies with
a specific focus on elderly pedestrian risk factors.
Wang et al. (2017) studied elderly pedestrian injury
in Singapore and found that nighttime, high-speed
roads, 3-legged intersections, and unlawful cross-
ings increased elderly pedestrian injury severity. In-
adequate crossing time at intersections was
identified as a contributing factor to high suscepti-
bility of elderly pedestrians to serious injury or fa-
tality (Martin et al., 2010). In seeking a gap between
vehicular traffic while crossing a street, elderly peo-
ple were found to underestimate the crossing times,
endangering themselves while crossing (Zivotofsky
et al., 2012). Older people walk more slowly than
younger people, and take smaller steps (Ketcham
and Stelmach, 2001). Studies of people with physi-
cal mobility problems confirm that they typically
walk rather more slowly (Shumway-Cook and
Woollacott, 2001). Ketcham and Stelmach (2001)
reviewed current research on changes in motor con-
trol related to ageing. In general, older adults move
more slowly. When high accuracy is required, older
adults tend to decelerate more slowly as they ap-
proach the target in a movement task.
Attention is important to pedestrians in a number of
ways. Pedestrians need to be able to switch attention
between tasks, to focus attention in particular loca-
tions, and to carry out visual search. There is some
evidence linking attention to the pedestrian task and
to accidents. Most pedestrians who are struck by
cars do not see the vehicle that hits them at all, and
many report that they looked but did not see it (Wil-
son and Grayson, 1980). Some aspects of cognitive
performance decline with age. There are, for exam-
ple, age-related deficits in both speed and accuracy
for memory, spatial processing, planning, and atten-
tion. There are several types of theoretical explana-
tion for lower performance on specific tasks. The
theorists argue that the differences result from gen-
eral slowing, from some other general reduction in
resources, and from reduction in some specific ca-
pacity, such as attention (Road Safety Research
Report, 2004). It has also been suggested that older
people perform more poorly on cognitive tasks in
part because they more frequently produce very
slow responses, or lapses, and that this is true for ex-
ecutive tasks specifically (West, 2001).
Regarding the methods used for data analysis (see
Section 3.2), it is important to note that the applica-
tion of cluster analysis is widely used in the scien-
tific literature. Cluster analysis has been applied in a
wide variety of fields, ranging from engineering,
computer sciences, life and medical sciences, to
earth sciences and economics (Xu and Wunsch,
2005). As for transport engineering, there are several
studies that have applied cluster analysis in the most
varied sectors of interest, such as the optimization of

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Leonardi, S., Distefano, N., Pulvirenti, G.,
Archives of Transport, 56(4), 107-118, 2020
the traffic control systems (Lin and Xu, 2020) and
the analysis of both accidents data and survey data
(Depaire et al., 2008; Distefano et al., 2018; Sivasan-
karan and Balasubramanian, 2020; Choudhari and
Maji, 2019). Clustering is an unsupervised machine
learning method based on heuristics which maxim-
izes the similarity between in-cluster elements and
the dissimilarity between inter-cluster elements
(Fraley and Raftery, 2002).
3. Method
3.1. Participants and survey
A 22 items survey was used to collect the partici-
pants’ opinions. The investigation techniques based
on surveys represent a very effective tool for the
study of lot of issues of transport interest (Ignaccolo
et al., 2019; Leonardi et al., 2019; Leonardi et al.,
2020; Distefano et al., 2019a; Distefano et al.,
2019b). The survey was conducted in 5 different lo-
cations in Catania, Italy. The locations were specifi-
cally chosen near to attraction poles for elderly pe-
destrians (e.g. centers for the elderly, squares,
churches). Participants were recruited in person, so
as to select exclusively people over 70. Participants
were briefed of the nature and time required to par-
ticipate in the study prior to commencement. After
their consent was obtained, the questionnaire started.
It was decided to question directly the participants,
instead of leaving them alone with the questionnaire,
in order to provide visual aids and detailed explana-
tions and clarifications. Each survey lasted approxi-
matively 20 minutes. Participants were assured of
anonymity and confidentiality. The total sample
comprised 322 participants (164 men and 158
women). Participants who did not complete the
questionnaire or who gave uncertain answers were
excluded. The respondents excluded were about 5%
of the sample. The final sample was composed by
306 participants (156 men and 150 women). The
majority of respondents (50.33%) were aged be-
tween 70 and 75. 28.10% of respondents were aged
between 75 and 80 and 21.57% of respondents were
over 80.
The survey contained questions about socio-demo-
graphic characteristics, the experience as road-users
and age related declines of perceptual and physical
abilities. Finally, the survey contained two open-
ended questions related to the critical issues of pe-
destrian paths and the solutions for these issues. The
questionnaire was divided into the following 5 sec-
tions:
- Section 1: participants reported their age, their
gender and other basic socio-demographic char-
acteristics information in the first section;
- Section 2: this section included questions regard-
ing the experience as road users of participants.
Participants were asked if they ever had the driv-
ing license, if they still drove, if they ever had
accidents while driving and if they ever had ac-
cidents as pedestrians;
- Section 3: the third section contained questions
about the age related declines of perceptual and
physical abilities. Participants were asked if they
had vision problems, hearing problems and mo-
bility problems.
- Section 4: this section consisted of an open-
ended question related to the critical issues of pe-
destrian paths. Participants could express freely
their opinion related to the critical issues and the
problems they found in the pedestrian paths they
usually walked.
- Section 5: this section consisted of an open-
ended question related to the solutions for critical
issues of pedestrian paths. Participants could ex-
press freely their opinion related to the solutions
they thought could improve the safety of pedes-
trian paths they usually walked.
Since the aim of this study is to explore which solu-
tions elderly pedestrian propose for improving the
safety of pedestrian paths they usually walk, this
study focuses on Sections 1, 2, 3 and 5 of the ques-
tionnaire. A previous study analyzed the results of
Section 4 of the questionnaire in order to explore the
perception of elderly pedestrians of the critical is-
sues of pedestrian paths (Pulvirenti et al., 2020).
3.2. Model development
In order to analyze the data obtained from the sur-
vey, a Cluster Analysis was developed. Cluster anal-
ysis seeks to separate data elements into groups or
clusters such that both the homogeneity of elements
within the clusters and the heterogeneity between
clusters are maximized (Hair et al. 1998). Among
the similarity-based techniques, two major ap-
proaches can be discerned. One approach is a dis-
tance-based clustering algorithm to identify homog-
enous subsets (partitioning approach; e.g., k-means
clustering). Another approach is the hierarchical

Leonardi, S., Distefano, N., Pulvirenti, G.,
Archives of Transport, 56(4), 107-118, 2020
111
clustering (e.g., ward’s method, a single linkage
method).
K-Means clustering is the most commonly used un-
supervised machine learning algorithm for partition-
ing a given data set into a set of k groups (i.e. k clus-
ters), where k represents the number of groups pre-
specified by the analyst. It classifies objects in mul-
tiple groups (i.e., clusters), such that objects within
the same cluster are as similar as possible (i.e., high
intra-class similarity), whereas objects from differ-
ent clusters are as dissimilar as possible (i.e., low in-
ter-class similarity). The first step when using K-
Means clustering is to indicate the number of clus-
ters (k) that will be generated in the final solution.
The algorithm starts by randomly selecting k objects
from the data set to serve as the initial centers for the
clusters. The selected objects are also known as clus-
ter means or centroids. Next, each of the remaining
objects is assigned to its closest centroid, where
closest is defined using the Euclidean distance be-
tween the object and the cluster mean.
The optimal number of clusters with the Hierar-
chical method is determined by the minimum num-
ber of groups with the maximum amount of distance
between group means. Frequently, this is illustrated
with a dendrogram of the merging clusters. Using a
dendrogram, the ideal number of clusters is deter-
mined by the number of clusters intersected when
drawing a vertical line through the largest horizontal
distance between merging clusters.
In this study both hierarchical and k-means cluster-
ing were used in order to explore which solutions el-
derly pedestrian propose for improving the safety of
pedestrian path. Starting from the results of the sur-
vey, a cluster analysis was developed to answer the
following questions:
1. Can we group together elderly pedestrians with a
similar perception of the solutions for improving
pedestrian safety?
2. How can we interpret the groups obtained? What
do elderly pedestrians belonging to the same
group have in common?
3. Which variables do mostly affect the determina-
tion of the groups?
The nominal variable considered is solutions for im-
proving pedestrian safety, with the fourteen possible
items showed in Table 1. These items were deduced
from the open-ended question related to the solu-
tions for improving the safety of pedestrian paths of
Section 5 of the questionnaire.
Table 1. Nominal variable: solutions for improving
pedestrian safety
Solutions for improving pedestrian safety
1 - Realization of sidewalks
2 - Adaptation of sidewalks (width)
3 - Renovation of sidewalks surface
4 - Prevention of parking on sidewalks
5 - Realization of ADA ramps on sidewalks
6 - Realization of pedestrian crossings
7 - Improvement of pedestrian crossing conditions
8 - Realization of signalized pedestrian crossings
9 - Realization or improvement of street lighting system
10 - Realization of traffic calming measures
11 - Realization of pedestrian areas
12 - Repair of road pavement
13 - Increase of police supervision
14 - Other forms of adaptation of sidewalks (height,
removal of obstacles, etc.)
The variables considered for the cluster analysis
were chosen in order to be representative of the re-
spondents’ experience as road users and of the re-
spondents’ age related declines in perceptual and
physical abilities. Moreover, the gender was taken
into account. Overall, 8 variables were considered:
1. No driving license (Yes, No): this variable indi-
cates whether the respondents had not ever got the
driver license, that means whether the respond-
ents had not ever drove;
2. No still driving (Yes, No): this variable indicates
whether the respondents were not still driving
when they answered the questionnaire;
3. Accidents driving (Yes, No): this variable indi-
cates whether the respondents had ever had an ac-
cident when they were driving;
4. Accidents pedestrian (Yes, No): this variable indi-
cates whether the respondents were ever hit by a
car (or another vehicle) when they were walking;
5. Vision problems (Yes, No): this variable indicates
whether the respondents have vision problems;
6. Hearing problems (Yes, No): this variable indi-
cates whether the respondents have hearing prob-
lems;
7. Mobility problems (Yes, No): this variable indi-
cates whether the respondents have mobility
problems;
8. Gender (Female, Male)
Each of the 8 variables considered can range from 0
to 1, indicating the percentages of respondents who
answered Yes or No to each question. The value 0 is
associated to the answer No for each variable (except

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Leonardi, S., Distefano, N., Pulvirenti, G.,
Archives of Transport, 56(4), 107-118, 2020
for the variable Gender, for which 0 corresponds to
Male), while the value 1 is associated to the answer
Yes for each variable (except for the variable Gen-
der, for which 1 corresponds to Female). The final
cluster centers can range from 0 to 1. These condi-
tions are all representative of age related declines in
perceptual and physical abilities (vision, hearing and
mobility problems) or of experiences as road user
(no driving license, no still driving, accidents driv-
ing, accident pedestrian) which can affect the opin-
ion on the solutions for improving pedestrian safety.
4. Results
4.1. K-Means cluster analysis
As shown in Table 2, the solutions for improving pe-
destrian safety were grouped in clusters by using
SPSS software. To use K-Means clustering, the
number of clusters is arbitrarily determined, either
from existing knowledge of the data and the approx-
imate number of groups you want to divide the data
into. Of course, a good approach to K-Means is to
try several numbers of clusters and see which num-
ber best represents the data or produces any signifi-
cant differences in analysis. Different models of
clusters were therefore estimated, from one to seven,
for selecting the suitable number of clusters. For fur-
ther analysis, the solutions for improving pedestrian
safety were divided into five clusters. Table 2 shows
the clusters membership. The first cluster is com-
posed only by item 6, i.e. “construction of pedestrian
crossings”. This cluster can therefore be named Con-
struction of pedestrian crossings. Cluster 2 groups
together three items, i.e. item 2 (“adaptation of side-
walks (width)”), item 13 (“increased police supervi-
sion”) and item 14 (“other forms of adaptation of
sidewalks (height, obstacle removal, etc.)”). The
second cluster can therefore be named Adaptation of
sidewalks and police supervision. Cluster 3 groups
together three items, i.e. item 3 (“Renovation of the
sidewalks surface”), item 9 (“Installation of the
street lighting system/Adaptation of street lighting
system”) and item 12 (“Repair of road pavement”).
Cluster 3 can therefore be named Walking surfaces
and lighting. Cluster 4 groups together 5 items, i.e.
item 1 (“Construction of sidewalks”), item 4 (“Pre-
vention of parking on sidewalks”), item 7 (“Im-
provement of pedestrian crossing conditions”), item
8 (“Installation of signalized pedestrian crossings”)
and item 11 (“Implementation of pedestrian areas”).
The fourth cluster can therefore be named
Pedestrian areas, construction of sidewalks and im-
provement of pedestrian crossings. Finally, Cluster
5 is composed by two items, which are item 5 (“Con-
struction of ADA ramps on sidewalks”) and item 10
(“Installation of traffic calming measures”). Cluster
5 can therefore be named Traffic calming measures
and elimination of architectural barriers.
Table 2. Clusters membership
Solutions for improving pedestrian
safety
Cluster
Distance
1 - Realization of sidewalks
4
0.227
2 - Adaptation of sidewalks (width)
2
0.260
3 - Renovation of sidewalks surface
3
0.199
4 - Prevention of parking on sidewalks
4
0.288
5 - Realization of ADA ramps on side-
walks
5
0.171
6 - Realization of pedestrian crossings
1
0.000
7 - Improvement of pedestrian cross-
ing conditions
4
0.396
8 - Realization of signalized pedes-
trian crossings
4
0.310
9 - Realization or improvement of
street lighting system
3
0.265
10 - Realization of traffic calming
measures
5
0.171
11 - Realization of pedestrian areas
4
0.311
12 - Repair of road pavement
3
0.241
13 - Increase of police supervision
2
0.232
14 - Other forms of adaptation of side-
walks (height, removal of obsta-
cles, etc.)
2
0.299
Table 3 shows the ANOVA analysis results and al-
lows to understand which variables affect more the
identification of the clusters. The variables mostly
contributing to the identification of the clusters are
Accidents driving (Sig.=0.002), Gender
(Sig.=0.004), Mobility problems (Sig.=0.002), Acci-
dents pedestrian (Sig=0.014), Vision problems
(Sig=0.026) and No still driving (Sig=0.033). Hear-
ing problems (Sig=0.401) and No driving license
(Sig.=0.517) are instead the variables less affecting
the division into different clusters.
Table 4 shows the profiles of the clusters obtained
with the K-Means procedure. Each group is repre-
sented by a center which originate a vector (row)
whom components are the means of the values of the
variables that defines the coordinates of the objects
belonging to that group. The main and most interest-
ing characteristics of the respondents belonging to
the five clusters are given below.

Leonardi, S., Distefano, N., Pulvirenti, G.,
Archives of Transport, 56(4), 107-118, 2020
113
- Cluster 1 (Construction of pedestrian crossings):
The majority of respondents belonging to this
group are men and have mobility problems.
- Cluster 2 (Adaptation of sidewalks and police su-
pervision): This group is mainly composed by
men with vision problems.
- Cluster 3 (Walking surfaces and lighting): This
group is mainly composed by women who had ac-
cidents while driving in the past and who have vi-
sion problems.
- Cluster 4 (Pedestrian areas, construction of side-
walks and improvement of pedestrian crossings):
This cluster is mainly composed by women who
do not drive anymore.
- Cluster 5 (Traffic calming measures and elimina-
tion of architectural barriers): The majority of re-
spondents belonging to this group are men who
had accidents while driving and as pedestrian in
the past.
4.2. Hierarchical cluster analysis
Hierarchical clustering allows to confirm the num-
ber of clusters which was hypothesized with the K-
Means clustering. The optimal number of clusters
with the hierarchical method is determined by the
minimum number of groups with the maximum
amount of distance between group means. Fre-
quently, this is illustrated with a dendrogram of the
merging clusters.
Using a dendrogram, the ideal number of clusters is
determined by the number of clusters intersected
when drawing a horizontal line through the largest
vertical distance between merging clusters. Similar
to K-Means, the optimal value of clusters must be
chosen, but this method gives some perspective as to
what the ideal value may be.
The hierarchical clustering allowed to illustrate the
hierarchical organization of groups as shown in the
dendrogram of Figure 1. This visualization confirms
the previous result, but offers also a hierarchical
view of the clusters. By cutting the dendrogram at
height 6, corresponding to the highest jump between
levels of similarity, five clusters homogeneous as for
their level of perceived safety are obtained. These
clusters correspond to the five clusters resulting
from the K-Means cluster analysis. The hypothesis
made for K-Means cluster analysis was therefore
fully confirmed by hierarchical cluster analysis.
Table 3. ANOVA analysis results
Cluster
Error
Mean Square
df
Mean Square
df
F
Sig.
Gender
0.097
4
0.011
9
8.685
0.004
No driving license
0.009
4
0.010
9
0.873
0.517
No still driving
0.034
4
0.008
9
4.283
0.033
Accidents driving
0.050
4
0.005
9
10.141
0.002
Accidents pedestrian
0.056
4
0.010
9
5.721
0.014
Vision problems
0.072
4
0.015
9
4.665
0.026
Hearing problems
0.019
4
0.017
9
1.129
0.401
Mobility problems
0.166
4
0.027
9
6.202
0.011
Table 4. Final cluster centres
Cluster
1
2
3
4
5
Gender
0.36
0.34
0.58
0.60
0.15
No driving license
0.27
0.27
0.25
0.36
0.25
No still driving
0.27
0.50
0.34
0.54
0.35
Accidents driving
0.45
0.47
0.65
0.34
0.57
Accidents pedestrian
0.27
0.37
0.15
0.24
0.55
Vision problems
0.45
0.69
0.55
0.49
0.21
Hearing problems
0.18
0.34
0.16
0.32
0.25
Mobility problems
0.91
0.17
0.06
0.16
0.42

114
Leonardi, S., Distefano, N., Pulvirenti, G.,
Archives of Transport, 56(4), 107-118, 2020
Fig. 1. Hierarchical analysis dendrogram
5. Discussion
The results of cluster analysis show that that the
judgment expressed by the elderly on the solutions
for improving pedestrian safety seems to be signifi-
cantly linked to the gender, to the experience as road
users, and to mobility and vision problems that com-
promise the correct perception of the road environ-
ment. Previous study shows that the gender of pe-
destrian impacts on the injury severity of elderly pe-
destrian (Retting, 1993). Age-related declines in per-
ceptual, cognitive, and physical abilities have also
been shown to contribute to the high rate of fatal or
serious-injury crashes found for old pedestrians
(Dunbar et al., 2004). Therefore, as might be ex-
pected, the variables which were found to affect the
solutions for improving pedestrian safety proposed
by elderly in this study are variables which are
known to affect the injury severity of elderly pedes-
trian.
On the other hand, the least significant variables in
conditioning the judgment on the solutions for im-
proving pedestrian safety is the one related to the
driving license. Hearing problems, even if condi-
tions the perception of urban pedestrian paths, are
less significant than mobility and vision problems.
Basically, in identifying the solutions for improving
pedestrian safety, elderly pedestrians are mainly
conditioned by the difficulty of correctly seeing the
paths themselves. The physical disability that most
influence participants’ answers in this study is vision
problems. This is a confirmation of the findings of
Barnett et al. (2016).
The analysis of clusters (Table 4) highlights that
men and women perceive differently the two main
elements of pedestrian paths, i.e. pedestrian cross-
ings and sidewalks. Men are more sensible to the ab-
sence of pedestrian crossings and suggest the reali-
zation of new pedestrian crossings. As for side-
walks, men wish that sidewalks already existing are
improved and adapted. Women show instead oppo-
site perceptions. Women are more sensible to the ab-
sence of sidewalks and suggest the realization of
new sidewalks. As for pedestrian crossings, women
wish that pedestrian crossings already existing are
improved and adapted. The two age-related prob-
lems most influencing the solutions for improving
pedestrian safety, i.e. mobility problems and vision
problems, originates different proposals. Elderly

Leonardi, S., Distefano, N., Pulvirenti, G.,
Archives of Transport, 56(4), 107-118, 2020
115
with mobility problems wish for more pedestrian
crossings. Elderly with vision problems are more
sensible to the conditions of sidewalks, especially
the conditions of the sidewalks surface. Moreover,
they wish for more police supervision and for better
lighting systems for streets and intersections. The
experience as road users also affect the judgment ex-
pressed by the elderly on the solutions for improving
pedestrian safety. Elderly who had road accidents
while driving or while walking prefer measures such
as traffic calming measures and measures aimed at
the elimination of architectural barriers. Moreover,
elderly who had road accidents while driving are
particularly inclined to measures aimed at improving
the road pavement and the night visibility of pedes-
trian paths.
The cluster analysis developed in this study allowed
to investigate the key components that influence the
elderly pedestrians’ perception of pedestrian paths
and to identify how these perceptions change for dif-
ferent pedestrian "profiles" based on human factors.
In order to meet the needs of pedestrians, it is indeed
necessary to have a clear understanding of the wide
range of abilities that exists in the population. Like
roads, pedestrian paths should be designed to serve
all users. In the same way that a roadway is not de-
signed for a particular type of vehicle, the design of
a pedestrian path should not be restricted to a single
type of pedestrian.
6. Conclusion
Pedestrian safety policies and guidelines should al-
ways (a) recognize pedestrians as legitimate road us-
ers and promote this recognition among planners,
engineers and professionals who plan and manage
the road transport system; (b) set and enforce traffic
laws that ensure safety of pedestrians; (c) encourage
an inclusive approach in planning new roads and/or
retrofitting existing roads; (d) pay attention to the
specific needs of the most vulnerable users such as
people with disabilities, children and the elderly.
This research has identified the factors that influence
the identification of the best solutions to increase the
safety level of pedestrian paths for elderly people.
The aspects related to human factors considered
were the gender, the factors associated with the ex-
perience as road users and the factors related to age
related problems (mobility, vision and hearing prob-
lems).
The results show that the judgment expressed by the
elderly on the solutions for improving pedestrian
safety is significantly linked to the gender, to the ex-
perience as road users, and to mobility and vision
problems which compromise the correct perception
of the road environment. All solutions proposed by
elderly for improving pedestrian safety regards road
infrastructure (improvement of pedestrian crossings
and of sidewalks, implementation of traffic calming
measures, improvement of lighting), except for po-
lice supervision. The road infrastructure should con-
sider the need of elderly pedestrians, which are less
able than others to cope with risky situations.
Several aspects of road infrastructure engineering,
such as a lamp replacement program to improve
night visibility at residential areas, better regulation
of traffic lights to help elderly pedestrians in terms
of time allowed for their crossing, improvement of
pedestrian crossings and of sidewalks, may need to
be considered in order to improve the safety of pe-
destrian paths for elderly. It is necessary to create the
adequate environment to lower vehicle speeds in ur-
ban areas, with the development of traffic calming
measures for example, in order to facilitate move-
ments of less-able pedestrians.
The results of this study can be used to support traf-
fic engineers, planners, and decision-makers to con-
sider the contributing factors in engineering
measures for improving the safety of particularly
vulnerable users such as elderly pedestrians.
Acknowledgement
The article has been presented on 13th International
BRD GAMBIT 2020 Conference. Co-funded by the
Science Excellence programme of the Ministry of
Science and Higher Education.
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