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Citation: Monterde-i-Bort, H.; Sucha,
M.; Risser, R.; Kochetova, T. Mobility
Patterns and Mode Choice
Preferences during the COVID-19
Situation. Sustainability 2022,14, 768.
https://doi.org/10.3390/su14020768
Academic Editors: Matjaž Šraml and
Aoife Ahern
Received: 22 November 2021
Accepted: 7 January 2022
Published: 11 January 2022
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Attribution (CC BY) license (https://
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4.0/).
sustainability
Article
Mobility Patterns and Mode Choice Preferences during the
COVID-19 Situation
Hector Monterde-i-Bort 1, Matus Sucha 2, * , Ralf Risser 2and Tatiana Kochetova 3
1Psychonomy Research Unit, Department of Methodology of Behavioural Sciences, Faculty of Psychology,
University of Valencia, 46010 València, Spain; hector.monterde@uv.es
2Department of Psychology, Palacky University in Olomouc, 771 47 Olomouc, Czech Republic;
ralf.risser@richal.at
3
Faculty of Social Psychology, Moscow State University of Psychology and Education, 127051 Moscow, Russia;
kochetovatv@mgppu.ru
*Correspondence: matus.sucha@upol.cz
Abstract:
The empirical research on the COVID-19 epidemic’s consequences suggests a major drop
in human mobility and a significant shift in travel patterns across all forms of transportation. We
can observe a shift from public transport and an increase in car use, and in some cases also increase
of cycling and (less often) walking. Furthermore, it seems that micromobility and, more generally,
environmentally friendly and comanaged mobility (including shared services), are gaining ground.
In previous research, much attention was paid to the mode choice preferences during lockdown, or
early stages of the SARS-CoV-2 situation. The blind spot, and aim of this work, is how long observed
changes in mode choice last and when or if we can expect the mode choice to shift back to the situation
before the SARS-CoV-2 episodes. The research sample consisted of 636 cases; in total, 10 countries
contributed to the sample examined in this study. The data were collected in two phases: the first
in the spring of 2020 and the second in the fall of the same year. Results showed that respondents
reduced mobility by car, local public transport and walking, but not bicycling during the lockdown,
compared to the time before the pandemic started. When the easing came, respondents assessed their
own use of the car and walking as almost back to normal. They also reported an increase in the use
of public transport, but not reaching the level prior the pandemic by far. It seems that cycling was
affected least by the pandemic; use of a bicycle hardly changed at all. As for the implication of our
study, it is evident that special attention and actions will be needed to bring citizens back to public
transport, as it seems that the impact of the pandemic on public transport use will last much longer
than the pandemic itself.
Keywords: mobility; traffic psychology; COVID-19; mode choice
1. Introduction
The authors of this study discussed changes in transportation mode use from a broad
viewpoint, examined transportation mode changes and compared the distribution of the
population in the modal groupings for the survey’s time periods—before, during and
after lockdown.
Several research initiatives looking into the effects of the COVID-19 outbreak have
found that that there is a significant reduction of human mobility and significant changes
in travel patterns [
1
]. Borkowski et al., (2021) noticed that during pandemics the travel
time decrease was determined by the means of transport used to travel. Other authors
showed that the number of daily trips per person was on average decreased by 50% during
the lockdown. Trips on foot were increased, private car was mainly used for commuting
and public transport modal shares were heavily reduced. Trip durations were generally
increased, as travelling was considered a recreational activity per se [2].
Sustainability 2022,14, 768. https://doi.org/10.3390/su14020768 https://www.mdpi.com/journal/sustainability
Sustainability 2022,14, 768 2 of 13
Anke et al. studied “the effects of this disruptive situation” as they called the situation
caused by the COVID-19 pandemic. They asked how and in what areas mobility behaviour
in Germany changed during the COVID period, thereby looking at federal states with
and without lockdown with the help of an online survey. The data showed a shift away
from public transport and an increase in car use, walking and cycling [
3
]. This was also
confirmed by Muley and colleagues, who showed that the public transportation mode share
was considerably reduced and people preferred cars and active modes. These changes also
showed positive impacts on air pollution [
4
]. Findings presented by colleagues in Sicily
showed that the public transport demand for commuting trips was reduced by half in
Sicily during the second period of the pandemic [
5
]. Eisenmann et al. studied general and
individual changes in transportation mode usage and attitudes, with a focus on the bicycle,
vehicle and public transportation [
6
]. Changes in the perception of individual mobility
alternatives were also explored, with an emphasis on car-free households. The findings
showed that during the highly constrained period of lockdown, public transportation lost
ground, whereas individual means of transportation, particularly the private vehicle, grew
in importance. According to Przybylowski et al. the pandemic made authorities all around
the world react by issuing both advice and legal acts, which also had implications for
outdoor mobility. The authors looked especially at the city of Gdansk (Poland). According
to their study, 90% of the respondents limited the use of public transport. Three quarters of
them planned to return to public transport when times become normal again while one
quarter will possibly be lost for public transport [
7
]. The authors concluded that, therefore,
authorities should start working on the image of public transport immediately, so that there
is no further decline, and to make sure that the development pro public transport during
the last decades before COVID is revitalised. An article by McKinsey [
8
] stated that before
COVID-19, cost and convenience played the most important role for customers in their
choice of transportation modes, while during the pandemic, reducing the risk of infections
became the top reason for travellers’ choices both concerning private and business trips.
This change definitely puts public transport at a disadvantage. Empirical findings have
showed that public users’ emotional perceptions led them to restrict certain travel choices.
Feelings of anxiety, fear and stress were the most common [
9
].
Abdullah et al., (2020)
[
10
]
confirmed that in normal circumstances, elements influencing mode choice, such as travel
time savings, comfort, and cost, became less important during pandemics. However, the
authors stated that public transport will remain in demand despite the risks of pandemic.
According to the study’s findings, efforts should be implemented to make public trans-
portation safer during a pandemic. However, one can also see another focus of research on
human mobility of behaviour. One of the few studies presented by Javid and colleagues
also focused on “what lies behind the behaviour”, showing that the perceived behavioural
control and personal norms have negative correlations with public transport use during
COVID-19 restrictions [11].
Bergantino et al. [
12
] studied “one of the few positive externalities” of the COVID-
19 pandemic and changing micromobility. They stress that micromobility, as well as
ecologically friendly and comanaged mobility in general, is gaining attraction. Bike-sharing
has enjoyed double-digit growth rates in its different incarnations throughout Italy, in
particular. It is a cost-effective solution with zero emissions, convenience, short-distance
speed and health benefits for users. In addition, the authors looked at how consumer
habits evolved over time, comparing them before and after the COVID-19 lockdown and
emphasizing the health benefits for both potential and present users. However, the authors
noted that, while fully recognising the positive implications related to the bike-sharing
system, people still preferred using their cars [12].
Barbieri and colleagues [
13
] focused their research on comparing people’s mobility
before and during the restrictions and the associated risks. Their findings stated that
significant mobility disruptions related to the restrictions enforced to tackle the COVID-19
pandemic pertained to all transportation modes and all travelling purposes, albeit the
extent of the transformations was different. We can observe similar findings in the research
Sustainability 2022,14, 768 3 of 13
of Chan and colleagues [
14
], who studied risk attitudes and overall human mobility during
the COVID-19 pandemic. They looked at the link between changes in human mobility
during the COVID-19 outbreak and the average risk preferences of people in 58 countries.
Almost all regions had a decrease in trips (mobility) to all locations other than residential
sites, notably in the early weeks of the sample period. Authors also noted an increase in
visits to parks, which might have been considered a “relatively safe environment” [14].
Some authors have a wider view in this respect, connecting risk attitudes not only to
pandemic environment but to other hazards including terrorist attacks, public transport
strikes or oil shortages. Colard et al. [
15
] focused on what the pandemic does to outdoor mo-
bility of the French and if it is possible to learn about the control of greenhouse gas (GHG)
emissions in transport. Restrictions and lockdowns reduced freedom of movement greatly,
producing hindrances to larger parts of society. Health and economic consequences have
not been fully assessed, yet. Colard et al. dealt with changes in mobility practices. They pro-
posed to look at long-term effects, and that today’s analyses only allowed to see temporary
impacts [
15
]. When reflecting upon the possible effects of the COVID-19 pandemic, Van
Exel and Rietveld [
16
] suggested looking at earlier disruptive events of medium/long term
character or medium/long term consequences. The authors considered the consequences
of public transport strikes that affect public transport every now and then. Such strikes may
induce a change of one’s habits. They analysed 13 studies in order to deal with this issue
and summarised that such strikes tend to increase car use—including congestions—and to
do mild but persisting long-term harm to public transport. This might also be the case as
far as the ongoing COVID-19 situation is concerned.
Nguyen-Phuoc et al.
[
17
] also dealt
with the disturbances of strikes in public transport. They asked 648 public transport users
in Melbourne how such events affected them, and it showed that 43% of users shifted to the
car. Sharpe and Tranter [
18
] dealt with potential impacts of disruptions (an oil crisis in their
case) on children’s independent mobility. More and more children were brought to school
by car, cutting down their freedom to explore the city, reducing their bodily fitness and
enhancing obesity (concerning the relevance of considering children see, e.g., Leden [
19
]).
Oil shortage would make more experts and decision makers argue for speed reductions.
The authors show that a holistic understanding of urban transport shows that reducing
speeds also would support children’s autonomous mobility, as parents would be less afraid
for their children’s safety, one frequent reason for transporting them by car. This study
shows that there is a potential for disruptions to also have positive societal effects.
Based on the presented research, we identified and operationalised the research gap
for this work, which explains the novelty of this work. In previous research much attention
has been paid to the mode choice preferences during lockdowns, or early stages of the
SARS-CoV-2 situation. The blind spot, or research gap in this respect, is how long observed
changes in mode choice last and when or if we can expect the mode choice to shift back
to the situation before the SARS-CoV-2 episodes. In other words, our work focuses on the
longer lasting effects of the behavioural change with possible prediction of future behaviour.
This is especially true for the mode choice shift away from the public transport. The other
relevant aspects of this work, which fit with the widely discussed topic of climate change,
are active travel modes and possible gains of SARS-CoV-2 situation in this respect.
Based on the identified research gap, we formulated the aim of this research as follows.
The study aims to understand how the SARS-CoV-2 situation influenced the preferences of
the different traffic modes during the early stages of a SARS-CoV-2 episode and to explore
whether these changes have longer lasting effects. A special attention was paid to active
traffic modes and public transport.
We formulated the following research questions:
Did the SARS-CoV-2 situation influence mode choice? If so, how?
Will mode choice changes during SARS-CoV-2 last even when the epidemiological situation
gets better?
Will people fear using public transport and prefer other modes of transport?
What will be the effects on the individual car use? Will it replace public transport trips?
Sustainability 2022,14, 768 4 of 13
How will the SARS-CoV-2 situation affect active traffic modes—walking and cycling? Will
these modes possibly replace trips by other modes—car and/or public transport?
2. Method
2.1. Sample
The sample consisted of 636 cases, compiled from various countries through a survey
distributed on the Internet utilizing social media and applying the “snowball” method.
The sampling, therefore, was random but not probabilistic.
The data were collected in two phases: the first in the spring of 2020 and the second in
the fall of the same year. During the first phase, data were compiled of the periods before
and during the health crisis provoked by the pandemic; the second phase consisted of data
collected after 5 months of living with the pandemic situation (retest). Of the initial sample,
the number in the case group which completed both phases was reduced to 456, due to a
loss of cases which commonly occurs when retesting any same group of individuals after a
prolonged period.
Once the compilation of data was completed, the countries which completed the two
phases of the survey were selected. They included: Austria (AT), Czech Republic (CZ),
Spain (ES), Croatia (HR), Italy (IT), Lithuania (LT), Portugal (PT), and Russian Federation
(RU), Sweden (SE) and United Kingdom (UK). In total, 10 countries contributed to the
sample examined in this study.
The distribution by gender resulted in 214 men, 419 women and 3 nonbinary or other.
In relation to age, the mean was 39.65 (median 37) years of age, with a minimum of 14
and maximum of 81 years of age, and a standard deviation of 15.56.
Most were full-time workers (351) and students (127), while 56 were part-time workers
and 46 retired.
2.2. Instrument
Through a survey methodology, in different languages, data were collected on the
frequency of use of 4 modes of transport: private car, local public transport, bicycle and
walking. The survey was distributed and answered online in the different countries in
which the study was carried out.
The answers were collected using a Likert-type 5-point answer scale, the possible
answers of which were adapted to the characteristics of the question (e.g., 1, not at all; 2,
seldom (few times a year); 3, a couple of times a month; 4, once or twice a week; 5, almost daily
or daily).
These questions were repeated for each of the periods measured (before, during and
after 5 months under the pandemic).
The survey was divided into two parts, corresponding to the two phases of the study.
The first phase, distributed in the spring of 2020, covered questions referring to the “before”
and “during” periods of the pandemic, as well as the corresponding demographic data.
The second phase, distributed during the fall of 2020 utilizing the list of email addresses
collected in the first phase, covered questions related to that moment in time (after 5 months
of living with the pandemic and the risk of contracting the disease).
In both phases and parts of the questionnaire, email addresses were requested from
the respondents and an individual personal code was constructed for identification based
on a series of personal questions (the day of the month of birth, the first two initials of the
mother’s name,
. . .
), which were repeated in both parts, given that the questionnaire was
anonymous.
The email address given in the first part was used to send the second part of the survey
to those who answered the first, during the second phase of the study. The email address,
along with the individual personal code, was used to pair up each respondent to their
answers to both parts of the survey.
Sustainability 2022,14, 768 5 of 13
2.3. Design
Randomized block design (RBD) was used, in which each nationality
´
s group of par-
ticipants completed the three measurements at three distinct moments in time: before the
pandemic, during the pandemic and after five months living with the pandemic situation.
This experimental design allows one to obtain representativeness with small and not
very representative samples, since the total sample is divided into small groups (in our case
the national samples) and the same procedure is applied in each one, so that all the groups
go through the same effects or conditions (in our case the situations “before”, “during” and
“after” the health crisis—not after the threat of the pandemic, which still remained with
less intensity then). Thus, this design minimizes the effects of systematic error, when the
experimenter focuses exclusively on the differences between treatments or situations, in
our case the effects of the COVID-19 threat.
The differences between the three moments in each of the aspects measured for each
mode of transport were verified by means of graphs to show the evolution and tests of
statistical significance.
To determine if the differences between the three moments were statistically significant,
and given the ordinal character of the variables [20], nonparametric tests were applied:
-
The Friedman test, equivalent to the analysis of variance, to check the statistical
significance of the effect (over the time period);
-
The Wilcoxon test (for related samples), to check the statistical significance between
pairs of time periods: before–during and during–after. In these comparisons, the
Bonferroni correction was applied to the decision (alpha).
3. Results
3.1. Private Car and Urban Public Transport
The two following graphs represent the evolution of the means and medians, compar-
ing the three periods regarding the use of these two modes of transportation.
As illustrated in Figures 1and 2, a reduction in usage is observed of both modes
of transportation in the most critical period of the pandemic (during the health crisis),
however, public transport was affected the most by the changes. The greatest reduction was
produced between the before and during periods, while after 5 months (at the end of the
health services crisis, marked by moments of hospital saturation) an increase is observed in
the frequency of use, although not to the levels seen before the pandemic.
Sustainability 2022, 14, x FOR PEER REVIEW 6 of 14
Figure 1. Means and medians of the responses on the frequency of use of private cars in the three
evaluated time periods.
Figure 2. Means and medians of the responses on the frequency of use of urban public transport in
the three evaluated time periods.
The following tables show the results of the Friedman tests.
As can be seen in Tables 1 and 2 summarizing the results of the Friedman test, the
time period had a statistically significant effect on the use of both modes of transport. This
leads us to affirm that the pandemic had a statistically significant influence on the fre-
quency of use of both these transportation modes. Figures 1 and 2 illustrate such influence.
3.73 3.37 3.66
444
Before During After
Mean Median
3.22
1.66
2.49
3
1
2
Before During After
Mean Median
Figure 1.
Means and medians of the responses on the frequency of use of private cars in the three
evaluated time periods.
Sustainability 2022,14, 768 6 of 13
Sustainability 2022, 14, x FOR PEER REVIEW 6 of 14
Figure 1. Means and medians of the responses on the frequency of use of private cars in the three
evaluated time periods.
Figure 2. Means and medians of the responses on the frequency of use of urban public transport in
the three evaluated time periods.
The following tables show the results of the Friedman tests.
As can be seen in Tables 1 and 2 summarizing the results of the Friedman test, the
time period had a statistically significant effect on the use of both modes of transport. This
leads us to affirm that the pandemic had a statistically significant influence on the fre-
quency of use of both these transportation modes. Figures 1 and 2 illustrate such influence.
3.73 3.37 3.66
444
Before During After
Mean Median
3.22
1.66
2.49
3
1
2
Before During After
Mean Median
Figure 2.
Means and medians of the responses on the frequency of use of urban public transport in
the three evaluated time periods.
The following tables show the results of the Friedman tests.
As can be seen in Tables 1and 2summarizing the results of the Friedman test, the time
period had a statistically significant effect on the use of both modes of transport. This leads
us to affirm that the pandemic had a statistically significant influence on the frequency of
use of both these transportation modes. Figures 1and 2illustrate such influence.
Table 1. Friedman test on private car use: before, during and after 5 months.
N Mean St. Dev.
Percentiles
Mean Rank
25 50 (Median) 75
Before COVID health
crisis I used a private car
636 3.73 1.377 3 4 5 2.11
During COVID health
crisis I used a private car
632 3.37 1.386 2 4 5 1.79
Now (autumn 2020) I
use a private car 456 3.66 1.377 3 4 5 2.10
Friedman’s Test
N (valid) 454
Chi-square 59.238
d.f. 2
2-Tailed p <0.0001
To make a comparison between the different periods, before–during and during–after,
the Wilcoxon test was utilized as stated in the Section 2.
In each of the following tables (Tables 3and 4)we compared, by pairs, the time periods
under examination, during with respect to before and after with respect to during. Since
multiple comparisons were made, the Bonferroni correction was adopted for the alpha value.
All comparisons (before–during and during–after) were statistically significant for
both modes of transport, which is to say, the decrease in usage resulting from the health
crisis generated by the pandemic, with respect to usage before the crisis, was statistically
significant for both modes of transport. The posterior increase in the frequency of use after
5 months of living with the risk of contagion with respect to the frequency of use during
the health crisis was also statistically significant for both modes of transport.
Sustainability 2022,14, 768 7 of 13
Table 2. Friedman test on inner city public transport use: before, during and after 5 months.
N Mean St. Dev.
Percentiles
Mean Rank
25 50 (Median) 75
Before COVID health
crisis I used inner
city transport
634 3.22 1.492 2 3 5 2.53
During COVID health
crisis I used inner
city transport
633 1.66 1.066 1 1 2 1.43
Now (autumn 2020) I
use inner city transport 454 2.49 1.308 1 2 3 2.04
Friedman’s Test
N (valid) 451
Chi-square 374.121
d.f. 2
2-Tailed p <0.0001
Table 3. Wilcoxon test comparing the use of private car between (each) 2 time periods.
I Use the Private Car N Mean Rank Sum Ranks Z 2-Tailed P
During COVID crisis
vs.
Before COVID crisis
Neg. ranks 221 a160.14 35,390.00
Pos. ranks 88 b142.10 12,505.00
Tied ranks 323 c
Total 632 −7.522 <0.0001
Now (autumn 2020)
vs.
During COVID crisis
Neg. ranks 70 d105.84 7409.00
Pos. ranks 165 e123.16 20,321.00
Tied ranks 219 f
Total 454 −6.465 <0.0001
a
During COVID health crisis, I used the car < before COVID health crisis.
b
During COVID health crisis, I used
the car > before COVID health crisis.
c
During COVID health crisis, I used the car = before COVID health crisis.
d
Now (autumn 2020), I use the car < during COVID health crisis.
e
Now (autumn 2020), I use the car > during
COVID health crisis. fNow (autumn 2020), I use the car = during COVID health crisis.
Table 4. Wilcoxon test comparing the use of inner-city public transport between 2 time periods.
I Use Inner City Transport N Mean Rank Sum Ranks Z 2-Tailed P
During COVID crisis
vs.
Before COVID crisis
Neg. ranks 436 a241.32 105,216.00
Pos. ranks 30 b119.83 3595.00
Tied ranks 165 c
Total 631 −17.624 <0.0001
Now (autumn 2020)
vs.
During COVID crisis
Neg. ranks 38 d136.38 5182.50
Pos. ranks 252 e146.88 37,012.50
Tied ranks 163 f
Total 453 −11.442 <0.0001
a
During COVID health crisis, I used inner city transport < before COVID health crisis.
b
during COVID health
crisis, I used inner city transport > before COVID health crisis.
c
During COVID health crisis, I used inner city
transport = before COVID health crisis.
d
Now (autumn 2020) I use inner city transport < during COVID health
crisis.
e
Now (autumn 2020) I use inner city transport > during COVID health crisis.
f
Now (autumn 2020) I use
inner city transport = during COVID health crisis.
These statistical tests (Friedman and Wilcoxon) added the condition of statistical signifi-
cance to the variations observed between the three periods represented in Figures 1and 2.
3.2. Cycling and Walking
The two following graphs represent the evolution of the means and medians, compar-
ing the three periods regarding the use of these two modes of transportation.
Sustainability 2022,14, 768 8 of 13
As illustrated in Figures 3and 4, the situation of the pandemic had less influence on
these modes of transport (cycling, walking) than on the other two previously analysed
(private car and urban public transport). Specifically, there was scarce or no influence on
the use of a bicycle, and modest influence on the frequency of walking and/or its choice
as a mode of transport, which suffered a slight reduction in usage during the most critical
period of the pandemic (during the health crisis), surely due to the confinement measures.
Sustainability 2022, 14, x FOR PEER REVIEW 9 of 14
Figure 3. Means and medians of the responses on the frequency of use of a bicycle in the three
evaluated time periods.
Figure 4. Means and medians of the responses on the frequency of use of walking in the three eval-
uated time periods.
The following Tables 5 and 6 show the results of the Friedman tests about the statis-
tical significance of the pandemic situation effect on cycling and on walking, respectively.
2.15 2.22 2.19
2
1
2
0
1
2
3
4
5
6
Before During After
Mean Median
3.84 3.51 3.76
444
0
1
2
3
4
5
6
Before During After
Mean Median
Figure 3.
Means and medians of the responses on the frequency of use of a bicycle in the three
evaluated time periods.
Sustainability 2022, 14, x FOR PEER REVIEW 9 of 14
Figure 3. Means and medians of the responses on the frequency of use of a bicycle in the three
evaluated time periods.
Figure 4. Means and medians of the responses on the frequency of use of walking in the three eval-
uated time periods.
The following Tables 5 and 6 show the results of the Friedman tests about the statis-
tical significance of the pandemic situation effect on cycling and on walking, respectively.
2.15 2.22 2.19
2
1
2
0
1
2
3
4
5
6
Before During After
Mean Median
3.84 3.51 3.76
444
0
1
2
3
4
5
6
Before During After
Mean Median
Figure 4.
Means and medians of the responses on the frequency of use of walking in the three
evaluated time periods.
The following Tables 5and 6show the results of the Friedman tests about the statistical
significance of the pandemic situation effect on cycling and on walking, respectively.
Sustainability 2022,14, 768 9 of 13
Table 5. Friedman test on bicycle use: before, during and after 5 months.
N Mean St. Dev.
Percentiles
Mean Rank
25 50 (Median) 75
Before COVID health
crisis I used the bicycle 635 2.15 1.375 1 2 3 1.98
During COVID health I
used the bicycle 631 2.22 1.452 1 1 4 2.03
Now (autumn 2020) I
used the bicycle 456 2.19 1.345 1 2 3 2.00
Friedman’s Test
N (valid) 453
Chi-square 1.355
d.f. 2
2-Tailed p =0.508
Table 6. Friedman test on walking use: before, during and after 5 months.
N Mean St. Dev.
Percentiles
Mean Rank
25 50 (Median) 75
Before COVID health
crisis I used walking 636 3.84 1.287 3 4 5 2.08
During COVID health
crisis I used walking 631 3.51 1.395 2 4 5 1.86
Now (autumn 2020) I
use walking 456 3.76 1.328 3 4 5 2.06
Friedman’s Test
N (valid) 453
Chi-square 23.145
d.f. 2
2-Tailed p <0.0001
As can be seen in Table 5, the time period did not have a statistically significant effect
of the use of bicycle as the main transportation mode. This leads us to affirm that the
SARS-CoV-2 pandemic did not have a statistically significant influence on the frequency of
cycling as a transportation mode. Figure 3illustrates this result. The observed effect is not
statistically significant.
Concerning the effect of the pandemic situation on the frequency of walking, Table 6
shows the Friedman’s test results.
As can be seen in Table 6, the time period had a statistically significant effect on the
use of walking as the main transportation mode. This leads us to affirm that the SARS-
CoV-2 pandemic had a statistically significant influence on the frequency of walking as a
transportation mode, but with less effect than the private car and urban public transport,
especially with respect to this last. Figure 4illustrates such influence.
To make a comparison between the different periods, before-during and during-after,
the Wilcoxon Test was utilized as stated in the Section 2.
As in the former section, in each of the following tables (Tables 7and 8) we compare,
by pairs, the time periods under examination: during with respect to before and after with
respect to during. Since multiple comparisons were made, the Bonferroni correction was
adopted for the alpha value.
Sustainability 2022,14, 768 10 of 13
Table 7. Wilcoxon test comparing the use of bicycle between (each) 2 time periods.
I Use a Bicycle . . . N Mean Rank Sum Ranks Z 2-Tailed P
During COVID crisis
vs.
Before COVID crisis
Neg. ranks 95 a98.93 9398.50
Pos. ranks 113 b109.18 12,337.50
Tied ranks 423 c
Total 631 −1.757 =0.079 *
Now (autumn 2020)
vs.
During COVID crisis
Neg. ranks 109 d111.32 12,134.00
Pos. ranks 100 e98.11 9811.00
Tied ranks 244 f
Total 453 −1.380 =0.168
a
During the COVID health crisis, I used a bicycle < before the COVID health crisis.
b
During the COVID health
crisis, I used a bicycle > before the COVID health crisis.
c
During the COVID health crisis, I used a bicycle = before
the COVID health crisis.
d
Now (autumn 2020), I use a bicycle < during the COVID health crisis.
e
Now (autumn
2020) I use a bicycle > during COVID health crisis.
f
Now (autumn 2020), I use a bicycle = during COVID health
crisis. * Note: the Bonferroni correction established the alpha value at 0.025.
Table 8. Wilcoxon test comparing the frequency of walking between (each) 2 time periods.
I Use Walking as
Main Transportation Mode N Mean Rank Sum Ranks Z 2-Tailed P
During COVID crisis
vs.
Before COVID crisis
Neg. ranks 211 a168.45 35,542.00
Pos. ranks 111 b148.30 16,461.00
Tied ranks 309 c
Total 631 −5.848 <0.0001
Now (autumn 2020)
vs.
During COVID crisis
Neg. ranks 107 d129.76 13,884.00
Pos. ranks 168 e143.25 24,066.00
Tied ranks 178 f
Total 453 −3.955 <0.0001
a
During the COVID health crisis, I used walking as the main transportation mode < before the COVID health
crisis.
b
During the COVID health crisis, I used walking as the main transportation mode > before the COVID
health crisis.
c
During the COVID health crisis, I used walking as the main transportation mode = before the
COVID health crisis. dNow (autumn 2020) I use walking as the main transportation mode < during the COVID
health crisis.
e
Now (autumn 2020) I use walking as the main transportation mode > during the COVID health
crisis. fNow (autumn 2020) I use walking as the main transportation mode = during the COVID health crisis.
Neither of the two pairs of comparisons (before–during and during–after 5 months)
were statistically significant for the use of a bicycle, that is, there has not been a statistically
significant decrease or increase between each time period compared to its previous.
Nevertheless, in the case of the use of walking as a transportation mode, both pairs
of comparisons (before–during and during–after 5 months) were statistically significant,
that is, both the decrease that occurs during the health crisis compared to before, and the
increase that occurs after 5 months of living with the risk of contagion, compared to the
previous moment, were statistically significant.
4. Discussion and Conclusions
A pandemic is a disruptive situation. Therefore, when trying to learn from the litera-
ture what the impacts of the SARS-CoV-2 pandemic could be, one can make use of both
recent studies referring to the SARS-CoV-2 pandemic directly and to studies dealing with
other disruptive situations, such as energy crises, long-term strikes, economic crises, etc.
Such literature shows a reduction of human mobility and a significant change in travel
patterns in such settings. According to some studies, during pandemics, the travel time
and number of trips change. This affects all transportation modes, albeit the extent of the
transformations is different. Some authors show that there tends to be a shift away from
public transport and an increase in car use, walking and cycling. Specifically, the private
car becomes more important, and there is an increase in visits to parks. Public transport
loses ground and some of the customers will probably not return to public transport. On
Sustainability 2022,14, 768 11 of 13
the other hand, some studies show zero impact on walking and on cycling. This might be
caused by a so-called ceiling effect: all (or mostly all) who considered walking or cycling
as a transportation mode already did so before pandemics and those who stopped using
public transport shifted to individual car use.
The literature also shows that the motives for mode choice changed. Before SARS-
CoV-2, cost, convenience and safety played the most important role; during the pandemic,
reducing the risk of infections became the top reason. Some studies indicate that environ-
mentally friendly mobility is gaining ground, e.g., bike-sharing or other sharing services.
In our study, we wanted to find out both how the emerging pandemic influenced
mode choice and mobility compared to the situation before the pandemic and how long
the observed changes in mode choice would last when restrictions due to the pandemic
are eased or fully removed. In our case, we dealt with an easing—not a full removal of
restrictions—and based on our results, we can answer our research questions as follows.
Did the SARS-CoV-2 situation influence mode choice? If so, how? The respondents stated
that they reduced mobility by car, local public transport and walking, but not bicycling
during the lockdown, compared to the time before the pandemic started. It is necessary
to note that mobility (number of trips) decreased also in total. These results are well
comparable to what we learned from literature.
Will mode choice changes during SARS-CoV-2 last even when the epidemiological situation
gets better? When the easing came, they assessed their own use of the car and walking as
almost back to normal. They also reported an increase of the use of public transport, but
not reaching the level prior the pandemic by far. Przybylowski et al. [
7
] predicted this in
their study from the Polish city of Gdansk.
Will people fear using public transport and prefer other modes of transport? Our results
indicate that this is the case. However, open-ended questions and answers are needed
in order to understand whether motives like fear or other reasons lie behind the reduced
use of public transport. In any case, the study of McKinsey [
8
] would corroborate the
assumption that fear plays an important role. To answer this question in detail, further
research with qualitative design (interviews or focus groups) is needed.
What will the effects be on individual car use? Will it replace public transport trips? From
our results, we cannot decide whether there was a replacement of public transport use by
car use. What we can say is that the respondents considered their car use during the easing
phase as similar to before the pandemic started, which was not the case concerning their
use of local public transport.
How will the SARS-CoV-2 situation affect active traffic modes (walking and cycling)?
Will these modes possibly replace the use of other modes? According to the statements of
the respondents, cycling was affected least by the pandemic. They indicated that their use
of a bicycle hardly changed at all. They even reported an increase during the lockdown,
though not significant, while the level during the easing was the same as the one before
the lockdown. Walking as a transportation mode was considered as less prevalent during
the lockdown. One could hypothesise that the reduced number of walks to the stops and
stations of public transport contributed to this. One could also hypothesise that respondents
did not fully consider leisure walks as “transport”, while such leisure walks could well have
increased in numbers, as Chan et al. [
14
] found in their study. In any case, respondents in
our study perceived their mobility by walking as significantly reduced during the lockdown
compared to before the pandemic, while they reported an increase almost to the status
before the pandemic during the easing. We cannot answer the question whether walking
and/or cycling replaced the use of other modes for sure, but one might certainly assume
that some public transport trips were replaced by bicycling. Here, as well, more dialogue
with the respondents is needed.
To sum up, one can say that, while car use, bicycling and walking recovered after the
confinement, tending to return to their initial values before the pandemic, the use of local
PT seems to have decreased significantly and will probably not return to its initial values
for a long time.
Sustainability 2022,14, 768 12 of 13
4.1. Limitations of the Study
As for the limitations of the study, we consider the main limitation the fact that we
used only Likert-scale questions, which categorize answers into five categories from “not at
all” to “almost daily”. This approach allows us to collect frequencies of mode choice but does
not allow us to learn about the motives and other psychological factors, which “lie behind
the behaviour” (e.g., motives, norms, beliefs, habits). Based on this, we can conclude how
the behaviour (mode choice) changed, but we cannot explain why. As one and the same
motive can lead to many different behaviours and one observed behaviour can have many
motives, this shortcoming should be tackled by another future study.
The other limitation is the way we stated the baseline. We asked respondents about
their mode choice before the pandemic situation (e.g., 2–3 months prior). Based on the
literature (e.g., Maycock, Lockwood and Lester, 1991 [
21
]) we know that people are not
very good at recollecting the past, even after just a few months.
Another limitation is that we have answers from only 636 persons from all over Europe.
This is of course not sufficient to represent the mobility patterns of ten different countries.
On the other hand, we could not find a single study that produced data that could be
generalized. We have a large sample of answers from all over Europe and get an impression
of the reactions of people to the prevailing situation, as far as their mobility behaviour is
concerned. Our results may be seen in the context of all the studies carried out on this
subject. In the discussion, we have inserted references that can help to put our results
in perspective.
4.2. Implications of the Study
The implications of our study are in the field of urban mobility planning, especially in
the field of public transport. It is evident, that special attention and actions will be needed
to bring back citizens to public transport, as it seems that the impact of the pandemic on
public transport use will last much longer than the pandemic itself. Furthermore, the fact
that mode choice preference persists even when the crisis is over (or eased) shows that in
all cases when supporting sustainable and active traffic modes, we cannot rely only on
“getting back to normal”, but we need to pay special attention to the promotion and support
of a shift to a wished-for mode choice (see also Risser and Sucha, 2020, who elaborated on
some suggestions on how this could be achieved [22]).
Author Contributions:
H.M.-i.-B.: conceptualization, formal analysis, investigation, methodology,
writing—original draft, writing—review and editing. M.S.: conceptualization, methodology, writing—
original draft, writing—review and editing. R.R.: investigation, writing—original draft, writing—
review and editing. T.K.: writing—original draft, writing—review and editing. All authors have read
and agreed to the published version of the manuscript.
Funding:
The funding for the present publication was provided by the Czech Ministry of Education,
Youth and Sports for specific research (IGA_FF_2021_018).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data are available upon request from corresponding author.
Acknowledgments:
Authors would like to thank all the colleagues who collaborated on data collec-
tion in selected countries, namely: Sari Kukkamaa, Glenn Berggard, Dario Babic, Justina Slavinskiene,
Petya Ventsislavova, Lenka Kristlova, Tereza Vaculova, Fatima Pereira da Silva and Laura Tamburini.
Conflicts of Interest: The authors declare no conflict of interest.
Sustainability 2022,14, 768 13 of 13
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