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BULLETIN OF GEOGRAPHY. SOCIO–ECONOMIC SERIES
Bulletin of Geography. Socio-economic Series, No. 61 (2023 ): 135-157
http://doi.org/10.12775/bgss-2023-0030
What drives them to drive? Mode choice for holiday travel in Poland
and its determinants
Iwona Pielesiak1, CMR, Bartosz Bartosiewicz2, CDFM, Szymon Wójcik3, DFM
1,2University of Lodz, Faculty of Geographical Sciences, Poland; 1e-mail: iwona.pielesiak@geo.uni.lodz.pl,https://orcid.org/0000-
0002-8396-8230; 2e-mail: bartosz.bartosiewicz@geo.uni.lodz.pl (corresponding author), https://orcid.org/0000-0001-8745-5910;
3University of Lodz, Faculty of Economics and Sociology, Poland, e-mail: szymon.wojcik@uni.lodz.pl, https://orcid.org/0000-
0002-6796-5734
How to cite:
Pielesiak, I., Bartosiewicz, B. & Wójcik, S. (2023). What drives them to drive? Mode choice for holiday travel in Poland and its
determinants. Bulletin of Geography. Socio-economic Series, 61(61): 135-157. DOI: http://doi.org/10.12775/bgss-2023-0030
Abstract. e article presents insights into holiday travel and its determinants in
Poland. e purpose of the study was to analyze Polish citizens’ modal split and
its determinants. Raw data from a pilot survey conducted in 2015 were used as
the source material. To identify the determinants of travel mode choice for holiday
trips, a multilevel multinomial logit model was utilized. is approach made it
possible to include the hierarchical structure of the data, in which respondents
are clustered within municipalities. e results reveal that, in addition to the
decision-maker’s socio-economic characteristics and household attributes, trip
characteristics signi cantly determine Polish citizens’ choice of holiday travel
mode. Moreover, the inclusion of municipality-level predictors substantially
improved the accuracy of the model. e analysis revealed that the severity of the
environmental consequences of motorized transport as perceived by respondents
also signi cantly in uences their travel mode choice for holiday trips.
Contents:
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
2. Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
3. Research design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
3.1. Study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
3.2. Data and methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
4. Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
4.1. Holiday travel behavior: basic remarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
4.2. Determinants of holiday travel behavior: multivariate analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
5. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
5.1. Main ndings and limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
5.2. Policy implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
Key words:
holiday travel,
tourism geography,
multilevel multinomial logit
model,
modal split & determinants,
Poland
Article details:
Received: 17 January 2023
Revised: 21 July 2023
Accepted: 29 September 2023
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/ Bulletin of Geography. Socio-economic Series / 61 (2023): 135–157
136
1. Introduction
Cyclical everyday travel behavior, such as
commuting or shopping trips, or traveling in
general, has gained considerable recognition in the
literature so far. e purpose of and the demand
for travel are repeatedly subjected to analysis. e
same applies to travel patterns and directly related
issues, such as mode choice, frequency, time and
distance, cost or complexity. ese phenomena
are examined through the prism of a wide range
of factors, mainly of a socio-economic nature
– travelers’ individual features (i.e., age, gender,
education level, economic status, and psychological
factors – values, attitudes), their household features,
but also in relation to the surrounding environment,
location in transportation network, urban structure,
etc. e numerous publications on these subjects
include: Schwanen (2002), Lanzini and Khan (2017),
Schoenau and Müller (2017), Mirzaei et al. (2021),
and De Vos et al. (2022).
ere is, however, another unique kind of travel
behavior, and its possible negative outcomes are
observed from a divergent perspective. is is
holiday travel behavior, and it is covered mostly
in tourism studies (e.g., Hsieh et al., 1993; Mok
& Lam, 2000; Le-Klähn et al., 2014, 2015; rane,
2015; Gross & Grimm, 2018; Große et al. 2019).
Analysis of tourist behavior, as with other forms of
mobility, is limited by the unavailability of extensive,
credible, and precise source materials. While
new technologies, such as passive and active GPS
tracking, and big data from mobile phone, smart
card, and social media traces open new avenues
of research in holiday travel behavior (Ahas et al.,
2008; Birenboim & Shoval, 2016; Shoval & Ahas,
2016; Zhao et al., 2018; Gutiérrez et al., 2020; Xue
& Zhang, 2020; Xu et al., 2022), they come with
limitations regarding data privacy, high level of
aggregation, costs of commercial acquisition, or the
distinguishing of tourists from non-tourists (Reif &
Schmücker, 2020).
Holiday travel is a captivating research problem
because of its occasional nature and the less limited
choice of behavior (e.g., destination, mode) than
day-to-day travel oers. Furthermore, holiday travel
has adverse eects on the climate (Peeters et al.,
2007; Hares et al., 2010). Recognizing the features
of holiday travel and, in particular, its determinants,
allows for a better understanding of consumer
choices. On the one hand, such knowledge might be
utilized for commercial purposes, in the tourism or
transport sectors particularly. On the other, it oers
decision-makers information that allows for more
precise targeting and implementation of transport
and environmental policies. e additional benet
is that it informs people about the negative
consequences of their choices and thus may facilitate
the change toward sustainable behavior.
With economic development and improved
living standards, tourism is now within reach of
alarge part of the population who live in medium-
and highly developed countries. However, the
structure of holiday travels and their determinants
vary according to the region of the world. Central
and Eastern European (CEE) countries are certainly
an interesting “laboratory” for research in this eld.
For decades their development path was separated
and to some extent hidden from Western Europe.
Despite radical changes in political and economic
doctrines that nally opened them for scientic
exploration, still they constitute an area in which
there are unknowns that need clarifying. In terms
of socio-economic development, Müller (2020) calls
this part of the world the “Global East”, located
somewhere between the Global North and the
Global South. At the same time, this region largely
remains on the peripheries of the debate on spatial
processes such as urban development or transport
(Müller & Trubina, 2020).
Compared to Western countries, CEE still lacks
complete recognition and understanding of the
factors of change in transport behavior, especially
for holiday travel. erefore, an attempt was made
to reveal its patterns and drivers within this specic
geographic context, which appear to be a research
gap worthy of closer examination. While choosing
the research area we focused on the largest country
of the region, Poland. No thorough diagnosis has
been made for Poland in this respect so far, largely
due to the scarcity of source information. Only
fragmentary data are available for this region of
Europe (EUROSTAT; Frei et al., 2010), and they
focus more on leisure activities than on movement
patterns. Furthermore, they are usually explored
supercially. Apart from a few descriptive and
unrepresentative studies that tackle holiday travel
behavior in general, or patterns of tourists’ movement
only within selected areas (e.g., Zientara et al.,
2021), not much is known about its determinants
in Poland. What is clear, however, is that, for the
last 30 years, the domestic tourist market (measured
by numbers of tourists) has tripled (Czernicki et al.,
2020; Tourism in 2022, 2023).
Between 2011 and 2019 alone, the share of
Polish tourists increased by 30%, from 6.9 to 9
million people (Eurostat, 2022). at is an obvious
consequence of Poles’ growing income – between
2010 and 2020, the average salary almost doubled.
Iwona Pielesiak et al.
/ Bulletin of Geography. Socio-economic Series / 61 (2023): 135–157
137
e direct market share for goods and services
strictly related to travel and tourism in Polish
Gross Domestic Product was 1.7%. By contrast, the
combined share of those activities and cooperating
industries was 4.3% (Milczarek 2017). Those
numbers are clearly lower than for most Western
European countries but, over the years, they have
testied to the stability of the tourist sector as a
source of income.
Regarding the structure of Poles’ tourism,
domestic trips dominate, exceeding 82% of the total
number (Tourism in 2022, 2023). e development
of domestic tourism is aided by the dynamic
development of road infrastructure (the length of
motorways and expressways has increased vefold
since 2004) and the motorization rate (a threefold
increase to over 600 cars per 1,000 inhabitants in
the same period according to Statistics Poland).
is car dependence is certainly worth attention as,
according to the European Environmental Agency,
cars are older than in Western Europe and much
lower electromobility dynamics are observed here.
CEE countries are close to Western European
countries in a few ways, despite their turbulent
past. After World War Two, they belonged to
the communist bloc. However, at the end of the
20th century, they underwent a socio-economic
transition. Finally, in the 21st century, they acceded
to the European Union (EU). ey follow Western
European behavior and make similar decisions in
many respects, but at times, they do reveal their
dierent nature. erefore, other questions arise:
Does this observation also apply to travel behavior,
and to holiday travel behavior, in particular? Do the
gaps in living standards and economic development
in a broader sense (Večerník, 2012; Otrachshenko
& Popova, 2014) make a difference to those
phenomena if we compare them with Western
European countries? Are the dynamic economic
processes – and the tourism sector, in particular
– reected in other (dierent) determinants that
aect Poles’ holiday travel? Our hypothesis is that
patterns of behavior in this European region do not
dier dramatically, though the role of motorized
individual transport is denitely higher.
e main objective of this paper was to reveal
the determinants of mode choice for Polish citizens’
holiday travel, which haven’t been the subject of
representative studies so far. We were interested
in discovering socio-demographic, economic,
psychological, and spatial factors that affect
decisions whether to take a car or use another
means of transport while moving to and from a
holiday destination. e analysis is preceded by
an overview of the basic features of holiday travel
regarding destinations and mode choices in this
part of Europe.
In this paper, we refer to the results of a pilot
survey on travel behavior in Poland that was carried
out in 2015 and from which we extracted data
related to holidays. A multilevel multinomial logit
model was utilized in the empirical quantitative
analysis.
The above-mentioned assumptions and
objectives determined the following structure of the
paper. First, the main thrusts of research on holiday
travel behavior and its determinants are presented.
ey are followed by a description of the research
method and source material. In the next section, we
refer to the results, where holiday travel behavior
is characterized, and its determinants are identied
and discussed. e article nishes with conclusions.
2. Literature review
In this section of the article, previous research on
travel behavior – and holiday travel, in particular
– has been analyzed. Our intention was to collect
and organize already-published results in order to:
resolve some terminological confusion we have
come across, build a hypothesis, select the most
accurate variables and the method for their analysis,
and compare our conclusions with what other
researchers have already discovered. erefore, the
following content: (1) structures the denition of
holiday travel; (2) reveals the scope and perspectives
already adopted within this eld; (3) refers to general
determinants of travel behavior, and nally (4)
discusses those determinants divided into categories
– socio-economic, demographic, psychological
(with reference to travel characteristics), and
spatial factors aecting behavior. In addition, the
article was supplemented with a review table (see
Appendix) ordering the research chronologically.
It species research samples and areas, methods
of assessment and dependent variables, as well as
factors taken into account. Features that proved to
be statistically signicant have been highlighted in
the table.
In the scientic literature, the behavior that is
analyzed in this paper is referred to as “holiday
travel” (e.g., Böhler et al., 2006; Wang et al., 2015,
2017; Li et al., 2016), “vacation travel” (LaMondia,
2010; van Nostrand et al., 2013) or “tourist’s travel
behavior” (Hough & Hassanien, 2010; Masiero
& Zoltan, 2013; rane, 2015). In this context,
a tourist, in contrast to “a visitor”, is “any person
traveling to a place other than that of his/her usual
environment for less than 12 months and whose
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/ Bulletin of Geography. Socio-economic Series / 61 (2023): 135–157
138
main purpose of the trip is other than the exercise
of an activity remunerated from within the place
visited” (IRTS, UNWTO 2008). Böhler et al. (2006)
restrict that role more, suggesting stays of at least
four nights and traveling for private purposes. Wang
et al. (2015, 2017) explicitly attribute being a tourist
to a specic period in which one does not have to
go to work or school. ere is also a whole body
of literature on leisure and long-distance travel that
largely overlaps with the phenomenon tackled in
this paper. Limtanakool et al. (2007, p. 2129) make
the point that “holiday journeys are less frequent
and involve longer travel distances and time spent
at destinations than leisure journeys”. e dierence
between these two categories is also acknowledged
by Böhler et al. (2006). On the other hand, many
researchers (e.g., Woodside et al., 2004; Hong et
al., 2005; van Nostrand et al., 2013; Bieland et al.,
2017; Fox et al., 2017; Gössling et al., 2017; Kirillova
et al., 2018; Cole et al., 2019; Große et al., 2019;
Czepkiewicz et al., 2020) do not articulate that
divergence so clearly.
Similarly, the scope of long-distance travel is
approached from diverse points of view. One-way
distance, usually Euclidean or road distance, and
trip duration are the most frequently employed
descriptors. A threshold of 50 km is suggested as a
minimum value (e.g., Dargay & Clark, 2012; Arbués
et al., 2014, 2016), but more oen it ranges from
a 50-km to 100-km minimum (Van Goeverden et
al., 2015; Czepkiewicz et al., 2020) or even further
(100 miles by Georggi and Pendyala [2001] and
Van Nostrand et al. [2013]). e extent depends
a great deal on country size and the arbitrarily
collected format of survey data. Furthermore, long-
distance journeys might be associated with time
spent traveling. In such a case, a threshold of, for
instance, three hours of travel in one direction is
adopted (Zanni & Ryley, 2015). Adding an overnight
stay, distance, and motivation are also criteria that
are used (IRTS, UN WTO 2008). An in-depth
terminological consideration was given to this kind
of travel by Aultman-Hall et al. (2018). Referring to
the above-mentioned literature review, in our paper,
we adopted the notion of holiday travel as described
in section 3.2.
e holiday and leisure travel issues tackled,
include, for instance, basic matters such as the
desire to leave and the level of satisfaction that it
gives (Terkenli, 2002; Dekker et al., 2014). en
the motivation, purpose, and frequency of such
activities are examined (Wei & Conners, 2017;
Wong et al., 2018). Hough and Hassanien (2010), as
well as Mok and Lam (2000), expand this topic by
investigating choices of holiday destination and pre-
travel decisions on tourism travel organizers. e
choice of travel mode and complexity of tourists’
journeys are also referred to.
These matters usually concern movement
between the home and the destination; however,
travel behavior at the destination is also a subject
of interest (Masiero & Zoltan, 2013; Le-Klähn,
2014, 2015; Gross & Grimm, 2018; Nutsugbodo,
2018; Bursa et al., 2022a,b). Analysis of mode
choice may be accompanied by an examination
of travel distance, time, or expenditure (Becken &
Schi, 2011; Mabit et al., 2013). Moreover, in the
face of growing concerns about the negative impact
of human activity on the natural environment,
the specic impact of holiday travel behavior is
investigated (Van Goeverden et al., 2015; Gössling
et al., 2017). At the same time, data quality and
its methods of acquisition (Aultman-Hall et al.,
2018; Janzen et al., 2018) are regularly discussed
and improved in order to provide sucient input
information for the above-mentioned inquiries.
Factors that aect travel behavior in its broadest
sense may be examined from dierent points of
view and attributed to various categories (e.g., De
Witte et al., 2013; Sun et al., 2017). Usually, the roles
of socio-demographic and time-related factors are
recognized (e.g., Commins & Nolan, 2011; Metz,
2012; Santos et al., 2013). Moreover, psychological
issues such as values, attitudes and norms, and
beliefs and opinions relating to convenience, safety,
or environmental consciousness are acknowledged
(Buehler, 2011; Santos et al., 2013; Lanzin & Khan,
2017; Wójcik, 2019). Another popular research
topic in this field is the disruptive character
of natural weather phenomena and the role of
climate change (Helbich et al., 2014; Böcker et al.,
2016; Liu et al., 2017). And nally, the functional
structure and spatial conguration of the built and
natural environment are examined. at category
encompasses the location of a job and service
facilities relative to places of residence (densities,
physical and time distances), land-use structure,
public transport accessibility (access/egress distances,
service frequency, and necessary transfers), length/
density and conguration of roads, intersections,
and bicycle lanes, and the availability of parking
space, among others (Schoenau & Müller, 2017; Sun
et al., 2017; Wójcik, 2020).
Some of the above-mentioned factors have also
been recognized as inuencing holiday, leisure, and
long-distance travel behavior with reference to trip
generation in general, distance, and mode choice
(see Appendix). It seems that women depend on cars
less than do men (Mallett, 1999; Arbués et al., 2016;
Lee et al., 2016), as do elderly travelers and young
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/ Bulletin of Geography. Socio-economic Series / 61 (2023): 135–157
139
adults (Georggi & Pendyala, 2001; Limtanakool et
al., 2006; Arbués et al., 2016). According to Georggi
and Pendyala (2001), LaMondia et al. (2010), and
Dargay and Clark (2012), increased household size
decreases trip distance. Household structure also
matters. e presence of children in a holiday-
making group discourages long-distance travel
(LaMondia et al., 2010). Similarly, having elderly
members in such a group increases the probability
of the journey being made by car (Li et al., 2016).
is is similar to the eect of increasing the number
of travel companions (rane, 2015).
Furthermore, married, full-time employed, and
highly educated people tend to travel more (Georggi
& Pendyala, 2001). Böhler et al. (2006) conrmed
the signicance of higher education, although their
results regarding marital status diered from one
another’s. According to Limtanakool et al. (2006),
those in high school (ages 14–18) prefer trains for
their leisure trips.
At the border between social and economic
issues, there is professional status. Van Can (2013)
noted that people who are employed in the state
sector tend to travel by air and by train rather than
by coach. Limtanakool et al. (2006) added that
worker-families prefer trains. Jobseekers, trainees,
and students who are already on the spot choose
public transport more oen (Gross & Grimm, 2018).
Income is one of the most important factors that
determine the distance covered, trip generation in
general, and mode choice (Limtanakool et al., 2006;
Dargay & Clark, 2012; Arbués et al., 2014). e least
economically privileged groups usually choose the
bus (Georggi & Pendyala, 2001; Van Can, 2013),
but Limtanakool et al. (2006) noted their preference
for trains, while Gross and Grimm (2018) noted a
preference for public transport in general. High
disposable income increases the role of the car, even
compared to the train (Arbués et al., 2016; Li et al.,
2016), and the wealthiest travelers more frequently
choose the plane (Van Can, 2013; rane, 2015).
Furthermore, those who own a second home
are more inclined to undertake domestic travel
(Czepkiewicz et al., 2020). is factor also enhances
their preference for the car over public transport
(rane, 2015; Arbués et al., 2016). Finally, owning
a car and the increasing number of cars owned
means there is a preference for cars when they are
at the user’s disposal (Gross & Grimm, 2018).
According to the literature on holiday and leisure
travel, we also know that people sensitive to travel
cost would rather use a surface mode of transport,
and if it is important to get to a destination easily,
journeys are shorter and more probably made by
car (LaMondia et al., 2010). Böhler et al. (2006)
and Arbués et al. (2014, 2016) noted that the longer
a trip is, the higher the probability of choosing
train over bus, as well as plane and train over car.
rane (2015), however, observed that increasing
the number of countries visited within the same trip
made travelers more likely to use a car than a plane.
Unsurprisingly, travel time also aects tourists’
choices. But it is more the out-of-vehicle rather
than the in-vehicle travel time that matters (Van
Can, 2013). e longer the trip between home and
destination, the greater the propensity to use the
train (Limtanakool et al., 2006). e elasticity of
demand for car travel with respect to travel time
and costs is unclear according to the observations of
Rich and Mabit (2012), Li et al. (2016), and Arbués
et al. (2016).
Other psychological factors matter as well.
According to the theory of planned behavior,
intentions affect mode choice, although other
important predictors are traveler habits and past
behavior (Lanzini & Khan, 2017). Thus, it is
interesting that analyzing habits in relation to
holiday travel behavior allowed Bieland et al. (2016)
to nd that repeated use of public transport makes
it more likely that it will be used during short
holidays. Asimilar observation was made earlier by
Nordærn et al. (2015) regarding leisure travel. ey
additionally discovered that leisure travel was also
aected by safety and security factors (accidents,
offenses such as violence or theft) more than
work trips were. e psychological explanation of
holidaymakers’ behavior has developed considerably,
not only based on the above-mentioned theory of
planned behavior, but also value-belief-norm theory,
social comparison theory, attribution theory, and
others (see Juvan & Dolnicar, 2014).
As for space-related factors (Appendix, “Place
of residence” column), the type of settlement unit
in which the travelers live is usually signicant.
Limtanakool et al. (2006) and Arbués et al. (2016)
emphasize the roles of high population density
and more mixed land use, which encourage people
to choose public modes, as does living in a big
city in general (Gross & Grimm, 2018). On the
other hand, more rural destinations increase car
use (rane, 2015). Such observations were also
made by Czepkiewicz et al. (2018a). Regarding
destination, high population density, mixed land
use, and specialization in services also enhance the
use of the train (Limtanakool et al., 2006). ose
who stay longer at their tourist destination would
rather get there by plane or public transport than
go by car (rane, 2015). However, that observation
is not in line with Becken and Schi (2011), who
emphasized the role of cars in such cases.
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2008 2009 201 0 2011 201 2 2013 201 4 2015 201 6 2017 201 8
Lviv 0.679 0.676 0.733 0.751 0. 810 0.822 0.734 0.769 0. 853 0.899 0.691
Ivano-Frankivsk 0.852 0.732 0.819 0.858 0.957 0.992 0. 789 0.652 0.689 0.791 0.869
Zakarpattia 0.875 0.711 0.793 0.834 0. 903 0.957 0.758 0.636 0. 665 0.739 0.805
Chernivtsi 0.855 0.763 0.839 0.887 0. 955 0.992 0.801 0.683 0. 728 0.823 0.890
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
Oblasts:
2008 2009 201 0 2011 201 2 2013 201 4 2015 201 6 2017 2018
Lviv 0.663 0.640 0.723 0.797 0.911 0. 885 0.824 0.750 0.743 0.791 0. 873
Ivano-Frankivsk 0.715 0.601 0.631 0.620 0.780 0.778 0. 714 0.696 0.610 0.602 0.674
Zakarpattia 0.824 0.711 0.688 0.767 0.823 0.862 0.766 0.677 0.649 0. 704 0.731
Chernivtsi 0.729 0.722 0.634 0.667 0. 734 0.755 0.586 0.557 0.516 0. 526 0.563
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
0.90
0.95
Oblasts:
ya
ya
To conclude, there was a considerable range
of factors analyzed as potential determinants for
travel behavior. Still, not enough research has
tackled holiday travel directly and explicitly, as
most research refers more to broader categories of
leisure or long-distance travel. In addition, some
observations are contradictory, oen due to national
characteristics (e.g., the organization of the public
transport system). And nally, all the important
analyses of the determinants of holiday travel
behavior refer to the situation in the West, South
Asia, or Australasia. Central and Eastern Europe,
and Poland in particular, are a less recognized
research area in this respect.
3. Research design
3.1. Study area
With 38 million inhabitants, Poland is one of the
ten largest countries by population in Europe.
According to the World Bank and OECD, before
2020, its economy was also one of the fastest-
growing in the EU, although just a few decades ago
it was still going through a painful transition from
acentrallyplanned economy to a market economy.
As a result, there was considerable improvement
in the sectoral and ownership structure,
entrepreneurship, infrastructure, education, and
the natural environment, among other things.
Additionally, the character of tourism changed from
social and mainly domestic to internationally open.
However, income inequality also became more
evident. Poles work longer but for smaller wages
(Croes et al., 2021), which might aect their holiday
behavior.
With an index value of around 634 cars per
1000 inhabitants (in 2019, according to Statistics
Poland), the country has become one of the most
motorized in the EU (the EU average in 2018
was 531). For several years, a great improvement
has been noted in the road accessibility of Polish
regions and cities (Kowalski & Wiśniewski, 2019).
However, there are growing inequalities in public
transport accessibility, which are due to enhanced
motorization, the ownership and organizational
changes of the former national bus and rail carriers,
the emergence of commercial operators in urban
agglomerations (Taylor & Ciechański, 2017), and
the provision of bus services for school children,
which is limited to rural areas. A distinctive feature
is that, in regards to air travel, international trac
prevails. According to the Polish Civil Aviation
Authority, in 2019, the ratio of passengers carried
within the country to those going abroad was 1:10.
3.2. Data and methods
Our research is based on a representative survey
of travel behavior in Poland that was conducted
by Statistics Poland (2015). That is the first such
rich and reliable source of data on Pole’s travel
behavior. Surprisingly, despite the time that has
passed since the raw data was made public, it still
has not been completely and thoroughly analyzed
(Bartosiewicz & Pielesiak, 2019). That appears
in a sense as a waste of immense potential for
informing the society, as well as for providing
more accurate bases for political decision-
making. Since 2015, no other representative of
even a similarly substantive value database on
travel behavior has been developed. The survey
sample included 13,500 Polish households (0.1%
of the total number of Polish households). In
total, there were 25,500 interviewees aged 16 and
over (0.1% of the total population 16 and over)
(Note 1). The CAII (Computer Assisted Internet
Interviewing) and CAPI (Computer Assisted
Personal Interview) survey was conducted as
a one-off project. This allowed us to gather
information on journeys made by the respondents
from Monday to Friday and on weekends (for
one chosen week), including occasional trips over
100 km that had happened within the preceding
12 months (before the survey).
The database comprises all types of travel
activity, including journeys made every day
and those made occasionally. The questionnaire
included seven purposes for occasional trips:
business trips, spending free time/short holiday
(up to four days), shopping, accompanying
somebody, personal needs (e.g., medical
assistance), holiday trips (four and more days),
and others. Return trips were a separate category.
In each category, the respondent was asked to
provide the place of residence and the destination
(municipality), the time and distance of travel,
the number of people traveling, and the main
means of transport.
We included all data from the category
“holiday trips”, which consisted of trips lasting
four days or more. Taking note of the origin
and destination, we excluded travel within the
interviewee’s municipality of residence. That
allowed us to remove data that referred to
holidays spent with family in the same city, for
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141
2008 20 09 2010 20 11 2012 20 13 2014 20 15 2016 20 17 2018
Lviv 0. 2205 0. 274 7 0.410 2 0.599 3 0. 7088 0. 8525 0. 8411 1. 0891 1.214 5 1.477 8 1.478 5
Zakarpattia 0. 4816 0. 5039 0. 611 9 0.708 5 1.313 5 1. 0416 1. 0140 1. 2130 1. 572 2 1.802 6 2.148 6
Ivan o-Fra nkivs k 0.006 7 0.001 5 0.0002 0. 0109 0. 0003 0. 0001 0.000 2 0.000 5 0.006 0 0. 0080 0. 0073
Chern iv tsi 0. 2154 0. 2173 0.199 9 0.250 0 0.2973 0. 3476 0. 2811 0. 3988 0. 557 0 0.644 1 0.694 9
1.4785
2.1486
0.0073
0.6949
0.00 00
0.50 00
1.00 00
1.50 00
2.00 00
2.50 00
DEPARTURES PER CAPITA
ya
example. The second category, which we partly
combined with the first one, was “spending free
time/short holiday”, such as on concerts, hobbies,
or cultural events. It referred to shorter trips
of up to four days. We limited this category to
trips with a distance exceeding 100 km, which
allowed us to exclude trips related to spending
free time cyclically, at least to some extent.
This assumption is in line with observations
made by Frändberg and Vilhelmson (2003),
who analyzed trips in Sweden in terms of the
relationship between travel distance and purpose.
We are aware of the limitations of this approach,
however, and understand that our database may
have included some non-holiday trips. On the
other hand, we did not want to lose some of the
data on short holiday trips, which are popular in
Poland. Thus, it was possible to take a holistic
approach to the topic.
In Table 1, the characteristics of the final
sample are presented. Ultimately, 8,274 trips
were selected for analysis, among which there
were 3,682 occasional trips over 100 km. There
were 6,958 individual travelers in the sample,
which gives nearly 1.2 trips per person. The
respondents lived in 988 different municipalities.
As the range of statistical tools used in
modeling holiday travel behavior is wide (Baltas,
Table 1. Sample characteristics
* Respondents were asked to indicate the eects of motorized transport that they consider to be the most adverse. Here, the eect was used
as indicated or not indicated. × 1 PLN ≈ 0.24 € (in 2015).
Source: own elaboration.
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142
2007), the vast majority of studies utilize the well-
established random utility framework (Ben-Akiva
& Lerman, 1985) to identify the determinants
of travel behavior. If the dependent variable
has a discrete polychotomous distribution, it
is common to use the multinomial logit model
(e.g., LaMondia et al., 2010; Thrane, 2015). In the
dataset used in the study, trips were nested within
respondents, who were nested within households,
which were nested within municipalities.
This complex data structure required more
advanced methodological treatment than classic
multinomial logit modeling. A proper approach
is to consider multilevel models, which can
address unobserved heterogeneity across the
observations at particular levels (e.g., Hox et al.,
2018: 1–7; Wong, 2017). This framework also
makes it possible to relax the IIA (Independence
of Irrelevant Alternatives) assumption, which
often binds the classic (one-level) MNL model
and restricts its applicability in some choice
situations (Hausmann & McFadden, 1984; Grilli
& Rampichini, 2007).
Therefore, the multilevel multinomial logit
model (multilevel MNL) was utilized in the
empirical part of the study (Note 2). This
method is currently regarded as a state-of-the-
art approach to modeling cross-sectional data in
transportation as it can capture random intra-
agent taste heterogeneity (Hess et al., 2004;
Washington et al., 2011: 275–281; Ortúzar
& Willumsen, 2011: 250–252). Various level
structures of the model were considered, taking
into account the hierarchical nature of the data.
Unfortunately, attempts to estimate models
that account for the full hierarchical structure
were not successful. Three- and four-level
model estimations suffered from convergence
problems caused by an insufficient number of
observations to form the groups at the household
and individual levels (they were often just one
trip made by an individual or one household in
the sampling period) (e.g., Clarke & Wheaton,
2007; Łaszkiewicz, 2013). This resulted in the
final choice of the two-level MNL model with a
random intercept at the municipality level as the
most appropriate tool (e.g., Arbués et al., 2016;
Mercado & Páez, 2009; Hung et al., 2013).
A two-level MNL model with a random
intercept at the municipality level was
considered. It can be written as follows
(Goldstein, 2011: 119–121; Arbués et al., 2016):
ǡ
where s is the response category (mode of
transport chosen), t is the number of categories
of the dependent variable, and πij stands for the
expected value of the response for respondent i
living in municipality j. X consists of respondent
level predictors with β as the regressor’s
parameters, α stands for a fixed category-specific
intercept, and ξj denotes a random category-
specific intercept describing the differences in
choices due to the clustering of respondents
within the municipalities. Finally, εij is an error
term assumed to be Gumbel distributed and
independent across respondents, categories, and
municipalities (Skrondal & Rabe-Hesketh, 2003).
The two-level MNL model allowed the level
of correlation between respondents living in the
same municipality to be assessed with an intra-
class correlation coefficient (ICC), defined as
the ratio of between-municipality variance and
total variance (Snijders & Bosker 2012: 38–66):
This coefficient is calculated for each response
category (excluding the base category). The
statistical significance of the ICC also supports
the view that spatial heterogeneity should be
accounted for. When choosing the research
method, the potential correlation between choice
categories was taken into consideration. As the
above-mentioned IIA assumption was not violated
in the estimated models (the Small–Hsiao test of
IIA at the 5% level of significance), it was not
justified to change the methodological approach
to a category-clustered oriented one (i.e., Nested
Logit). On the other hand, according to Hess et
al. (2004), a multilevel approach that accounts
for random taste heterogeneity can capture the
effects of inter-alternative correlation presence
in the error term. This means that, even if a
significant correlation between alternatives were
present in the data, the multilevel MNL model
would capture it but it would be interpreted as
part of a random taste variation.
The final specification of the model was
developed based on a series of Likelihood Ratio
tests and the assessment of theoretical plausibility.
The selection of variables for the final model
was performed in accordance with the general-
to-specific modeling paradigm (Campos et al.,
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143
2005), which assures that none of the statistically
significant predictors will be omitted.
4. Results and discussion
is section presents basic information about Polish
travelers’ modal split, followed by a description of
the results of the multinomial analysis. Finally,
the results are contrasted with what the scientic
literature reveals regarding subsequent determining
factors.
4.1. Holiday travel behavior: basic remarks
According to the survey, the majority of Poles spend
their holiday without leaving the country. Of the
8,274 trips made, only 15% were foreign. As far as
domestic trips are concerned, one third went to the
seaside, while mountain resorts were the second
most popular destination. Another distinctive
feature was the large share of trips to the biggest
cities (Warsaw, Cracow, Wroclaw, and Gdansk).
ose cities both attract typical tourists and might
also reect the tendency of Poles to spend their
holiday with their families.
As for trips abroad, two categories may be
distinguished. e rst is related to visiting family,
which is a result of the massive migration of labor
that began in Poland aer it joined the EU in
2004 (Burrell, 2011). Accodingly, the joint share
of Germany and the United Kingdom reaches
20%. e other group consists of typically tourist
destinations, which is apparent as far as winter (ski)
and summer trips are concerned. Poles target the
Czech Republic, Austria, and Slovakia for the former
and Croatia and Italy for the latter, although Italy
is also a popular destination in the winter season.
e high motorization index for the whole
nation is visibly reected in the modal split of
holiday travel. Most Polish travelers use their own
cars, which are responsible for almost three quarters
of all domestic trips (Table 2).
One in ten citizens goes on holiday by bus and
one in twelve by train. Air travel was only declared
by those going to destinations abroad (about 40%
of international trips). Simply taking shares into
consideration suggests that choosing the car, which
was the expected mode, becomes more likely as
the number of household members increases.
Furthermore, such behavior is typical of half of the
interviewees who live on their own. In the case of
two-person households, the share is 72%, and for
large families (5+) with children younger than 16
years old, it was 81%. People living in rural areas
use cars more oen than those in urban areas, but
the dierence is not dramatic (83% vs. 75%). at
pattern is determined by three factors: a higher
motorization index and limited access to public
transport for domestic journeys in rural areas
(Bartosiewicz & Pielesiak, 2019), as well as low
accessibility of airports for international journeys
(Czepkiewicz et al., 2018).
4.2 Determinants of holiday travel behavior:
multivariate analysis
e transport mode chosen for holiday trips was
taken as the dependent variable in the two-level
MNL model. As the car was the most popular
mode chosen by respondents, it was used as the
base category. e estimated results for the choice
of bus, train, and plane are presented in Table 3.
Table 2. Modal split of Poles’ holiday trips*
* Holiday trips – trips of four days or more and a distance of more than 100 km
** D – domestic; I – international
Source: own elaboration.
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Table 3. One-level and two-level multinomial logit models for travel mode choice for holiday trips in Poland
Note: Car is the base category for the whole model. Z-scores were calculated using robust standard errors. *p < 0.1, **p < 0.05, ***p < 0.01. ♦ Base category: Male. † Base category: Lower. ‡ Base category:
Pensioner/jobless. ◊ Base category: City ≥ 100k inhabitants. # Base category: < 25k PLN/year (PLN refers to the national currency of Poland, the Polish zloty).
Source: own elaboration..
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Of the respondents’ socio-economic
characteristics, only the inuence of gender was
signicant for all modes of transport considered. In
each case, women were less likely to choose a car
than men, which is in line with observations made
for American, Dutch, Spanish, and Chinese citizens
by Mallett (1999), Limtanakool et al. (2006), Arbués
et al. (2016), and Li et al. (2016), respectively. e
nonlinear eect of age was signicant for trips
made by bus and airplane. e negative value of
the squared term suggests that, as the respondents
get older, the likelihood that they will choose a bus
or plane over a car increases but with a diminishing
rate (an inverted U-shaped parabolic relationship).
is conrms what Georggi and Pendyala (2001)
noted based on simple descriptive statistics and
Chi-squared testing. ey also noted that for, elderly
Americans, the role of bus transport also increases
with age. On the other hand, their results reveal no
visible preference for rail transport, in contrast to
Limtanakool et al. (2006).
According to our research, education level
signicantly determines the choice of the bus on
holiday trips. As the level of education increases,
the probability of choosing the bus over the car gets
smaller. is is a new insight and, again, is dierent
from the Dutch preference for trains among highly
educated travelers (Limtanakool et al., 2006). Highly
educated respondents tend to choose the plane
more oen than low-educated respondents, which is
in line with what Czepkiewicz et al. (2019) observed
regarding young Icelanders’ international travels.
Our results also add to the scientic knowledge
that self-employed people tend to choose cars more
oen than buses or trains compared to pensioners
and jobless respondents (base category). However,
for trips by air, this relationship is the opposite.
For respondents who are not self-employed, there
is a signicant preference for the car over the bus.
However, being a student or pupil sharply increases
the probability of choosing a bus or train over a car,
which can be explained by the discounts for train
and bus tickets available for this group. Moreover,
this group of respondents can face problems with
car accessibility due to driving license eligibility and
lower precedence of car use, especially if there is
only one car in the household. is nding is in line
with the relationship observed in the daily travel
activity of Polish students (Sokołowicz et al., 2011).
Students and high-school pupils also tend to choose
the plane more readily than the car. is eect is
less statistically signicant, but it can be explained
by higher international mobility among the youth
(observed among young Germans by Kuhnimhof
et al. 2012), their desire for short-term trips with
cheap ights (Mailer et al., 2019: 231) refer to less
frequent car use among the young vs. their “greater
desire to discover the world by plane”), and the
discrepancy between environmental behaviors at
home and while traveling, especially on holiday
(Barr et al., 2010).
Household attributes are essential predictors
of mode choice for almost all modes considered.
So far, household size has been analyzed in the
holiday travel context as a determinant of trip
length. Our research revealed that it also matters
for mode choice. In Poland, as the size of the
household increases, the probability of choosing
public transport over a car gets higher (the eect
is not signicant for trips by air). If we consider
the number of household members who are
younger than 16 years old, an inverse relationship
can be observed, which was also reported by Li
et al. (2016) in their analysis of Chinese domestic
tourism. Ownership of at least one car in the
household leads to a signicant decrease in the
probability of choosing any other mode of travel.
Such an observation regarding tourism mobility
in Austria was also recently made by Juschten and
Hössinger (2020). In our case, this predictor has
the most substantial inuence compared to any of
the other covariates in the model, which supports
similar ndings in other studies (e.g., Limtanakool
et al., 2006). It is also important to mention that the
car ownership variable can itself be related to other
factors (Van Acker & Witlox, 2010). erefore, the
conclusions should be treated with caution. We
argue that, in our study, the eect of car ownership
can be partially related to the missing information
on personal/household income.
One of the trip characteristics we considered
was the number of people traveling together. An
increase in the size of the travel party leads to an
increase in the probability of choosing the car over
alternative modes of travel, which is in line with
what Juschten and Hössinger (2020) observed for
Austrian tourists and their lower preference for
public transport. However, it contradicts rane’s
(2015) ndings on Austrian tourists’ preference for
air and public transport. Our study does not directly
measure the perceived comfort of traveling or the
per-capita cost of the trip. erefore, we suspect
that the size of the travel party might also partially
account for these factors.
e Polish study also controlled for the attributes
of the municipality. e inhabitants of larger cities
(over 100k citizens) have a higher propensity to
choose public transport modes than the residents
of smaller cities and rural areas. at was expected,
as it was previously suggested by Limtanakool et al.
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146
(2006) and Arbués et al. (2016). However, we add
that an increase in the density of railways raises
the probability of choosing train over car. We also
argue that the overall income status of respondents
who reside in a particular municipality can aect
modal choices. erefore, the average yearly income
in the municipality was included in the model. e
relationship between this variable and mode choice
is positive and signicant for the use of planes for
holiday travel, which conrms what Georggi and
Pendyala (2001), LaMondia et al. (2010), Van Can
(2013), and rane (2015) observed regarding the
use of the “household income” variable for air travel.
We also observed a negative relationship between
municipality income and the probability of choosing
bus over car, which is particularly signicant in the
wealthiest regions compared to the poor ones.
As for travelers’ opinions regarding the side eects
of transport, the multivariate analysis revealed that
respondents who perceive exhaust emissions as the
most adverse side eect of transport are more likely
to choose planes than cars for their holiday travel
(Note 3). us, it seems that emissions are attributed
more to road trac than to air travel. However,
this contradiction between the expectancy of more
awareness (for which higher education might be a
proxy) and choosing less environmentally friendly
modes of transport was also observed for long-haul
travelers by Böhler et al. (2006). Similarly, Davison
et al. (2014: 21) observed a “cognitive dissonance
between attitudes and behavior” in this respect.
is was later conrmed by McDonald et al. (2015),
Alcock et al. (2017), and Lanzini and Khan (2017),
among others. Hares et al. (2010) and Juven and
Dolnicar (2014) explained it through the prism of:
(1) unwillingness to change behavior as holidays
are prioritized more than environmental concerns;
(2) denial mechanisms (referring to responsibility,
external factors, e.g., nancial and time constraints,
or limited accessibility); (3) downward comparison
(worse behavior happens), an exceptional situation
(on holiday vs. at home), and covering harms with
the benets that tourism oers. Mailer et al. (2019)
found that tourists are still not ready to welcome
dramatic changes that limit their freedom, accepting
relatively easy or temporary compromises that
enhance sustainability.
On the other hand, Bruderer Enzler (2017)
observed that people who care more about the
environment choose the plane less frequently.
However, that study concerned air travel for private
purposes rather than explicitly for holiday travel.
erefore, the possibilities of comparison with this
case are limited. e respondents who selected
congestion as the most critical consequence of
transport tend to travel more by car than by bus
or train. One could expect an inverse relationship
here, but this eect can be explained by the fact
that frequent car users are primarily aected by
congestion daily.
e values of the intraclass correlation coecients
(ICC) for each mode are reported in Table 3. is
measure can be interpreted as the proportion of
variability explained by spatial dierentiation. For
bus and train travel, the ICC equaled around 16%
and 15%, respectively; for the choice of plane,
it was signicantly smaller (4.3%). ese results
mean that most of the mode choice determination
stems from the traveler’s individual characteristics,
but the between-municipality dierences are not
negligible. For the choice of train, similar results
were obtained by Arbués et al. (2016) for Spain.
On the other hand, their estimated ICC for the
choice of bus over car was signicantly lower. It is
hard to determine the exact factors responsible for
the spatial heterogeneity of choices. ey may be
related to local taste variation or the dierences in
the infrastructure between the regions and access to
a particular mode of transport.
e validity of the choice of the two-level MNL
model as a tool for researching holiday travel
behavior was conrmed by the signicant LR test
outcomes. e results of the two-level MNL model
were also compared with the classic (one-level) MNL
model. e outcomes of this comparison suggest
that the results are robust in terms of parameter
signicance and signs of coecients. e value
of the Akaike Information Criterion (AIC) was
signicantly higher for the one-level MNL model
(10804.331), which supports the choice of the two-
level MNL for the multivariate analysis (see Hox et
al., 2018: 38–39). e model’s goodness-of-t can be
assessed with McFadden’s and Nagelkerke’s Pseudo
R2 values (Grabowski, 2019: 215–239). According
to Hox et al. (2018: 123–124), values between 0.2
and 0.4 indicate a good t of the model, which leads
to the conclusion that the outcomes of the empirical
analysis are acceptable and reliable.
5. Conclusions
In terms of the eciency or everyday functioning of
the transport system, holiday travel is not directly
comparable to, e.g., commuting. It comprises
occasional journeys, which occur infrequently
(mainly during the holiday season) and which are
channeled along the main transport routes. ese
do not signicantly aect congestion in the most
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147
urbanized regions, where the number of road users
is already high. However, in more remote, less
populated, and less intensively developed areas,
as well as areas with a critically fragile natural
environment, such travel behavior causes serious
adverse eects. At the same time, it increases local
demand for goods and services, thus supporting
a wide range of economic entities and stimulating
investment (e.g., technical infrastructure, service
facilities, or public spaces) that serves both tourists
and residents. For the latter, this means interweaving
benets with nuisances (e.g., higher prices, noise,
crowds, congestion). ose issues were addressed
by, e.g., Archer et al. (2005), as far as tourism, in
general, is concerned, or Bursa (2021), who focused
on the impact of tourists’ travel behavior.
5.1. Main ndings and limitations
e paper provides the rst complex examination
of factors that aect holiday transport behavior in
Poland, a CEE country in which the path of socio-
economic development has deviated considerably
from the one observed in Western countries. It
turns out, however, that this divergent development
path has not had much eect on the choice of mode
for holiday travel. e 30-year transition period
has been long enough to make Polish society very
similar to Western societies in this respect. When
going on holiday, Poles most frequently choose
cars. is behavior is more typical of people who
live far from the highly accessible public transport
found in cities. Furthermore, trains are also chosen
less frequently, which is attributed to the limited
level of development of the rail infrastructure. is
nding becomes even more interesting if we take
into account the tendency of the inhabitants of this
part of Europe to return to the same destinantions
during subsequent holidays. at was observed
by Coerria et al. (2015). is raises the need for
a continuation of this line of research in future. If
relevant long-term data become available, it will be
worth examining whether the patterns of transport
behavior on holidays still do not change and what
possibly might determine that.
Furthermore, in accordance with the hypothesis
formulated in the introductory part of this article,
we can conclude that age, gender, household
composition, and income usually affect travel
behavior in a similar manner to that found in the
results in other countries. However, we added new
insights on the role of the size of the household and
travel party, the traveler’s level of education, and the
municipality in which he or she lives.
As for the methodological contribution of this
study, we conrmed that the multivariate analysis
that was carried out using a multilevel multinomial
logit model can capture not only the impact of
individuals’ factors on mode choice, but also the
spatial dierences of their choices related to the
area where they live. Nonetheless, the use of this
tool remains rare in holiday travel behavior studies.
e outcomes of the empirical analysis show that,
among Polish citizens, this heterogeneity of choices
is relatively low but not negligible, and it diers
across the modes of transport. is phenomenon
can be explained by the infrastructural disparities
between Polish regions. To some extent, that reects
the impact of the over-hundred-year political
partition that lasted until the beginning of the 20th
century.
Our paper is, to the best of our knowledge, the
rst such comprehensive attempt to investigate the
determinants of holiday travel behavior in Poland.
However, we are aware that there are limitations to
our research. Firstly, the sample is not in line with
the characteristics of the Polish population. For
example, the rural population is underrepresented
in the survey. Secondly, research design regarding
the source data suered from some methodological
aws, i.e., a lack of information regarding the exact
date of traveling or missing attributes of choice
alternatives. Finally, 2020 brought completely
unexpected diculties for travelers due to the
restrictions and uncertainty caused by the COVID-19
pandemic. ese facts may have inuenced the
present structure characteristic and determinants of
holiday trips in Poland. However, in our opinion,
in general, our results and conclusions are in line
with the main processes taking place nowadays.
Additionally, this research oers a solid base for
comparing how travel patterns were aected during
the pandemic.
5.2. Policy implications
e results presented in this paper are important for
the commercial sectors (carriers, accommodation,
retail, and supporting industries). Knowledge of
travelers’ clear inclinations for domestic holiday travel
and socio-economic features is an indispensable
basis for precise customer targeting and the outlining
of development strategies for the future. However,
our ndings are even more signicant for policy,
especially regarding transportation policy, tourism
development, and spatial planning. ey allow for
a more adjusted implementation of instruments that
enhance holidaymakers’ desired behavior. ere
Iwona Pielesiak et al.
/ Bulletin of Geography. Socio-economic Series / 61 (2023): 135–157
148
is already a good starting point. e observed
preference for domestic travel means that national
and local development should be supported in
multiple economic sectors. Furthermore, it requires
shorter distances to be covered, which contributes
to less gas emissions than longer-distance journeys.
As those features are in line with the principles of
sustainable development, the authorities should
encourage them on a regular basis.
Travel behavior in Poland has become comparable
to that observed in Western European countries
although, economically, it lags behind. us, it may
and should benet more from those countries’ rich
and more mature policy experience that slowly
evolved under market economy conditions. Like
other CEE countries, Poland had to abruptly adapt
to new political and economic circumstances.
e fast pace did not allow for unhurried testing
or thoughtful learning and implementation of
solutions and instruments. Political decisions were
made quickly and boldly, and the long-term results
were not always in line with the policymakers’
intentions. e time has come to develop and
execute an updated comprehensive policy that
eectively combines economic, transportation, and
environmental principles and that learns from tried-
and-tested experiences in the West.
One of the key issues to be addressed by
such a policy is the relationship between Polish
holidaymakers’ awareness and their actual choices.
We revealed a dissonance that challenges pro-
environmental policy and the shi towards more
sustainable tourism. According to recent public
surveys (e.g., Ministerstwo Klimatu i Środowiska
2020; CBOS 2020), environmental awareness is
developing, and it may be seen to be catching up
with Western Europe. However, as already stated,
Poland lingers behind the West in economic terms.
That is clearly noticeable as far as household
disposable income is concerned, for example (see
OECD statistics). In a country with insuciently
developed public transport, the car remains a status
symbol. But it also remains a basic means of holiday
travel for short and medium distances (including
trips abroad), especially for those who travel in
groups.
In such circumstances, appealing to travelers’
environmental awareness is obviously ineective,
and more fundamental needs and resources should
be addressed. ere are incentives that aect travel
costs and time, as well as security and comfort
for passengers. Basic measures include increasing
subsidies for cheaper family tickets and substantially
enhancing and promoting seasonal rail lines to
popular tourist destinations. Moreover, there are
special trains to festivals and major sports events.
Also, schedules are tweaked to make switching
means of transport easier and more convenient,
and the overall travel time more competitive with
private means of transport.
Special attention should be paid to railway
connections due to their high transport capacity,
speed, and comfort for passengers. Although the
railway network covers the entire country, there are
signicant regional disproportions, which should
be tackled urgently. If that were accompanied
by replacing conventional sources for generating
electricity with renewable ones, railway transport
would become the most sustainable alternative.
e changes recommended above, which are
intended to reduce travel costs and oer fast,
safe, and comfortable traveling, are the attractors
aimed especially at the huge group of families
with children. ose travelers, according to our
ndings, would not give up their cars otherwise.
If successful, apart from the direct eects, such
as reducing greenhouse emissions and generating
additional revenues for public transport, another
goal will be accomplished, and that is familiarizing
young travelers with sustainable means of transport.
e traveling experiences and habits of younger
age groups may aect future behavior, making the
desired outcomes more durable.
We are aware that Poles’ great attachment to cars
probably requires other transitional solutions. e
more eective development of electromobility seems
to be a way of decarbonizing, at least temporarily.
However, a major challenge is the development
of power infrastructure that meets the demand.
Another challenge is the already mentioned need
to increase the share of green energy supply, as
renewable sources still contribute less than 20% of
total production in Poland. Finally, implementing
technical measures that make the manufacturing
and management of equipment more sustainable
also remains a challenge. Implementing all
those recommendations would be a challenge
in normal times, but, especially now, in the face
of the extraordinary economic difficulties and
political uncertainty in the world today, that seems
particularly problematic.
In order to ensure greater operational eciency
of the proposed recommendations, additional in-
depth research is advisable. It should reveal the
impact of potential global determinants, but it
could also extend our knowledge of the role of
the local spatial context, e.g., urban structure
and environment-related factors, as well as the
psychological foundations for personal attitudes and
preferences. e results of the quantitative analysis
Iwona Pielesiak et al.
/ Bulletin of Geography. Socio-economic Series / 61 (2023): 135–157
149
in this paper remain a solid starting point for such
an endeavor. e most reasonable solution would be
to expand the survey that our paper was based on
to contain questions on norms, beliefs, intentions,
and denial mechanisms in subsequent editions.
Additionally, based on what Nordærn et al. (2015)
reported for Norway, surveying travelers’ fears and
worries might produce an interesting basis for
practical use. Norwegians seem to be encouraged
by a lower risk of accidents. If that observation
also proves true for Poland (infamous for having
one of the highest road accident rates in the EU), a
far-reaching and continuous information policy, in
contrast to the rudimentary and sporadic campaigns
already carried out on the safety of traveling by
public transport, may be expected.
Notes
1. e data were collected in accordance with the
two-stage stratied sampling technique. Sample
representativeness was adjusted to the sociode-
mographic characteristics of the general pop-
ulation in the given territorial unit. In cases
where representativeness was not assured sam-
pling weights were calculated in order to facili-
tate the generalization of the results.
2. The multilevel multinomial logit model is
known in the research literature under a varie-
ty of names (see Garson 2013: 3-12; Hox et al.
2018: 8). e most popular names include the
mixed multinomial logit model, the random pa-
rameters multinomial logit model, and the hi-
erarchical multinomial logit model. We use the
name multilevel multinomial model to empha-
size the focus on the structure of the data used
in the empirical analysis. A similar approach
can be found in Arbués et al. (2016).
3. Among the adverse eects of motorized trans-
port, the respondents also mentioned noise, ac-
cidents, parking in prohibited areas and other.
ese variables were not statistically signicant
predictors of travel mode choice, so they were
not included in the nal model specication.
Acknowledgements
This work was supported by National Science
Centre of Poland [grant number UMO-2019/35/B/
HS4/00286] and the funding programme for
young researchers at the Faculty of Economics and
Sociology, University of Lodz.
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Appendix. Determinants of travel behavior taken into consideration in research on holiday, leisure and long-
distance travel
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Key: factors marked in bold were found to be statistically most signi cant in each study (thresholds are not cited due to high methodological diversity). For factors in italics, the assessment of signi cance was un-
clear or not applicable.
Source: own elaboration.
