Article
citation information:
Barczak,
A., Dembińska, I., Kauf, S. The impact of Covid-19 on passenger shipping
activities in selected EU countries: diagnosis and long-term scenarios. Scientific Journal of Silesian University of
Technology. Series Transport. 2025, 128,
51-69. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2025.128.3
Agnieszka BARCZAK[1], Izabela DEMBIŃSKA[2], Sabina KAUF[3]
THE IMPACT OF
COVID-19 ON PASSENGER SHIPPING ACTIVITIES IN SELECTED EU COUNTRIES: DIAGNOSIS
AND LONG-TERM SCENARIOS
Summary. The article is devoted
to the topic of passenger maritime transport in selected EU countries and the
changes in the maritime passenger transport market resulting from the COVID-19
pandemic. The implemented restrictions on movement entailed a voluntary and
temporary suspension of the activities of tourist companies using the maritime
fleet. With the above in mind, the purpose of this article was to identify the
consequences of changes in the mobility behavior of
the population as a result of the COVID-19 pandemic on the operation of
passenger maritime transport. The differences in the number of seaborne
passengers served in selected EU countries between actual and projected values
were indicated. Long-term forecasts were also made, which made it possible to
develop scenarios of possible events, describing the potential development
directions of the branch. We use a combined forecast method based on a weighted
average of individual forecasts (weights inversely proportional to mean
percentage absolute error (MAPE)). We use such forecasting methods as Fourier
spectral analysis, exponential smoothing models, and seasonality indices. We
used time series models to build long-term forecasts. Combined forecasts for
selected EU countries were determined. They were used to supplement long-term
forecasts. This made it possible to assign the obtained results to ambient
scenarios. Combined forecasts showed that in all quarters of 2020-2021, the
number of passengers transported by sea transport was lower than forecast values
in all EU countries analyzed. This confirms the
negative impact of the COVID-19 pandemic on this branch of transportation as
well. Long-term forecasts, built on the basis of combined forecasts and
assumptions about the annual growth rate of passenger numbers, indicate that in
most of the countries analyzed, the most likely
scenario is an annual increase of 10% in passenger numbers. This means that by
2026 only Germany and Denmark will see the number of maritime passengers return
to pre-pandemic levels.
Keywords: passenger shipping, transport, Covid-19, pandemic, combined forecasts,
long-term forecasts
1. INTRODUCTION
In
December 2019, a series of cases of severe pneumonia of unknown origin emerged
in Wuhan, Hubei, China, in people associated with China's Wuhan Huanan Seafood
Wholesale Market. Chinese authorities announced on January 7, 2020, that they
had identified a new type of virus related to viruses such as SARS and MERS. It
has been provisionally named “2019-nCoV” [16]. At the beginning of January
2020, cases of the disease appeared in Thailand and the United States, and in
the second half of January, the first case of infection with the new
coronavirus was recorded in Europe - in France. In February, the International
Committee on Taxonomy of Viruses calls the new coronavirus the severe acute
respiratory syndrome coronavirus SARS-CoV-2, and the World Health Organization
(WHO) calls the new infectious disease caused by the newly recognized
coronavirus, COVID-19. On March 11, 2020, WHO announced the COVID-19 pandemic
[15, 30-31, 42, 45]. In order to control the extremely dynamic increase in
infections, many countries have introduced unprecedented restrictive measures,
ranging from bans on travel and social gatherings to the closure of many
economic activities.
Although
some time has passed since the outbreak of the COVID-19 pandemic, new analyzes and opinions are still emerging regarding its
impact on various areas of social and economic life. This activity and
inquisitiveness of researchers is justified. Firstly, COVID-19 was an
unpredictable and sudden phenomenon, in retrospect even considered a shock.
Secondly, COVID-19 was global, covering all continents. And thirdly, it spread
at an extremely fast pace, which hampered decision-making processes regarding
response or prevention, often leading to decision-making errors. All this has
resulted in many different research orientations, embedded in both empirical
and cognitive-theoretic analyzes.
The
emergence of the COVID-19 pandemic has significantly developed research areas
such as risk management, volatility management, uncertainty management, etc. By
some researchers [23, 2, 1, 35] COVID-19 was identified as a black swan in the
light of Taleb's theory [40] as an event with outsized effects, difficult to
predict and even more difficult to calculate the probability of its occurrence.
Taleb himself opposed this view of COVID-19 [10, 3], claiming that this
pandemic did not meet the criteria he formulated for black swan phenomena
because, for example, it was predictable.
The
COVID-19 pandemic is often put into the framework of the VUCA paradigm [49, 29,
47]. Volatility in the context of COVID-19 refers to the rapid and unexpected
increase in the number of cases since the outbreak of the epidemic. The
COVID-19 pandemic has also created a situation of uncertainty. Almost all
prediction models for disease transmission have almost failed. COVID-19 has
also demonstrated the complexity of the situation. It has been associated with
ambiguity regarding disease dynamics and control measures. The ambiguity of
COVID-19 is understood in terms of unclear cause-and-effect relationships
without available precedents, as a situation with many unknown unknowns,
including the length of the lockdown.
The
perspectives for analyzing the impact of the pandemic
on various areas of life are different; many of them concern a sectoral
approach. The pandemic was a strong determinant for transport. It affected both
cargo and passenger transport, in both cases affecting all branches. Research
in this area not only shows the diversity and scale of problems but also is a
source of experience (management through experience) and practices that are
important in building preventive strategies.
Taking
the perspective of passenger sea transport, it is necessary to analyze the impact of the COVID-19 pandemic on transport
and tourism, i.e., what was the impact of the pandemic on tourism and how it
affected passenger sea transport. Some of the studies [48, 26, 43] carried out
in this area focused on macroeconomic aspects, showing that the sharp decline
in international travel caused by the pandemic and the restrictions introduced
as a result of it had a strongly negative impact on the economies in many
regions of the world, because tourism indirectly supports employment in other
sectors of the economy. It has also been shown that restrictions on passenger
transport introduced to prevent the spread of the epidemic not only negatively
affect passenger transport and tourism directly related to the movement of
people, as well as indirectly related sectors but have significant consequences
for trade in goods and other services. By limiting the movement of people for
business purposes, the possibilities of concluding new contracts are also
reduced [9, 12]. Despite the development of various e-commerce platforms in
many regions of the world, entrepreneurs still travel for transaction purposes,
e.g., in the analyzed period, this concerned as many
as 60% of small and medium-sized entrepreneurs from the Asia-Pacific region
[38].
Other
analyses, especially at the beginning of the pandemic, compared the situation
in maritime passenger transport with past periods to indicate the losses it had
caused in the transport sector. Millefiori et al. [22] showed that there was an
unprecedented slowdown in global shipping mobility, which had been growing
steadily since 2016, with a decline in activity for all ship categories in 2020
compared to forecasts based on the average growth rate of previous years.
Importantly, they showed that the most affected traffic segment was passenger
ships - from March to the end of June 2020, traffic fluctuations were between
-19.57 and -42.77%. Moreover, to prevent the spread of the virus, many cruise
ports have been closed. Silva [37] showed that Florida ports lost $22.2 billion
in 2020.
The
negative impact of the Covid-19 pandemic on the traffic of cruise ships,
passenger ferries, and ferry-ro-pax vessels was also
proven by other authors in their research on Danish waters [8], showing data
including a significant reduction in the number of ships in traffic, a
reduction in average speed, and average draft. Similar observations can be
found in studies [21, 7, 41, 36].
Attention
should be paid to yet another trend of research on the impact of the COVID-19
pandemic on passenger ship traffic. It concerns the analysis of the impact of
the decline in passenger ship traffic on the natural environment. Overall,
studies have shown that SOx emissions from passenger
ships decreased during the pandemic, although the trends varied [8, 27, 24, 11,
20].
An
interesting direction of research is the impact of the COVID-19 pandemic on the
behavior of customers, i.e., passengers. Overall, the
COVID-19 pandemic has had a negative impact on passengers' willingness to
cruise, mainly due to risks related to passenger health safety [34, 14]. These
results show that the cruise industry needs to put more effort into building
confidence in cruise safety in the post-pandemic era, as passengers expected
cruise lines to maintain not only higher cleanliness standards but even make
changes to ship designs to improve ventilation in the future.
The
literature review shows that many different perspectives can be identified to
study the impact of the COVID-19 pandemic on maritime passenger transport. Each
of these research directions enriches our knowledge of the impact of
unpredictable, uncertain, complex, and at the same time global and common
phenomena on transport activities in the maritime passenger transport sector.
But the literature analysis also showed that there is a paucity of research on
transport forecasts after the pandemic and when transport will return to normal
(pre-pandemic) levels. This became the premise for undertaking the research in
this study. The aim of the article is to identify the consequences of changes
in population transport behavior as a result of the
COVID-19 pandemic for the functioning of maritime passenger transport.
Differences in the number of sea transport passengers served in selected EU
countries between actual and forecast values will be indicated. Long-term
forecasts will also be made, which will allow for the development of scenarios
of possible events, describing potential directions of development of the
industry.
This
study has the following structure. Section 2 presents the research methodology.
Section 3 then describes the research results, and Sections 4 and 5 present the
predictions. The study is summarized with conclusions.
2. MATERIALS
AND METHODS
Countries
with more than 20,000 passengers carried in 2019 were selected for analysis
(Fig. 1). In ascending order, these are: Germany (20,162 thousand people),
France (22,685 thousand people), Spain (22,796 thousand people), Sweden (29,501
thousand people), Denmark (31,105 thousand people), and Greece and Italy, where
37,495 and 46,189 thousand passengers were carried, respectively.
Figure
2 illustrates the differences between the number of passengers served in
selected countries using single-basis dynamic indexes. Compared to 2019, 2020
saw passenger declines of more than 60% (France - 61.42%), 50% (Spain - 57.65%,
Sweden - 54.45%). The lowest decrease in the number of passengers this year was
recorded for Italy - 36.20%. With respect to 2019, 2021 declines were slightly
lower than in the previous year. Despite this, they exceeded 50% for France
(55.95%), and 40% for Spain and Sweden (45.05% and 42.46%, respectively). Greece
had the lowest passenger decline in 2021 at 29.46%.
The
brief analysis above (Fig. 2) shows the tremendous impact of the COVID-19
pandemic on passenger maritime transport and confirms the validity of the
stated goal of this study.
Combined
forecasts based on a weighted average of individual forecasts (weights
inversely proportional to mean percentage absolute error (MAPE)) obtained using
such forecasting methods as Fourier spectral analysis (otherwise known as
harmonic analysis), exponential smoothing models, and seasonality indices were
used to forecast the volume of maritime passenger flows. The choice of methods
is based on the graphical and quantitative analysis of the time series, which
showed that the series are characterized by the presence of seasonality. Since
these methods have been widely described in the literature - combinatorial
forecasting, e.g. [46, 19]; Fourier spectrum analysis, e.g., [4, 28, 18];
exponential smoothing methods, e.g., [17, 44, 32-33, 25] seasonality indicators
e.g., [5-6]; and the study focuses on analyzing the
results obtained.
Fig. 1. Number of passengers in
maritime transport in 2019 (in thousands)
Fig. 2. Number of passengers in
maritime transport in 2019 (in thousands)
All
analyses conducted were based on quarterly data from 2013 to 2019. The results
of the combined forecasts were compared with actual quarterly data from
2020-2021. This allowed the relative error of the ex-post forecast to be used
to indicate differences in marine passenger transport resulting from the
restrictions introduced as a result of the COVID-19 pandemic.
The
next step was to produce annual long-term forecasts through 2026 using three
different data sets used for forecasting. The first set is based on actual data
for 2013-2019 and combined forecast values for 2020-2021. Time series models
with the best fit to the data were used here. The second forecast is built on
the assumption that until 2026 the number of passengers will grow year by year
at the rate of the actual average passenger growth that was recorded in the
pre-pandemic years 2013-2019. The last forecast is based on the assumption that
the number of passengers will grow 10% year by year compared to the previous
year.
Table
1 shows the MAPE forecast error values obtained for each country and
forecasting method. As previously mentioned, the weights needed to calculate
the weighted average of individual forecasts were determined from the MAPE
values. This allowed the calculation of combined forecasts for individual
countries.
Tab. 1
Mean absolute percentage error (MAPE) for the individual
forecasting methods included in the combined forecast
|
Country |
Mean absolute percentage error (MAPE) |
||
|
Fourier spectral
analysis |
Exponential smoothing method |
Method of seasonality
indicators |
|
|
Germany |
2,0036 |
1,1379 |
1,4821 |
|
France |
2,3967 |
1,5416 |
1,9144 |
|
Spain |
2,3156 |
1,7409 |
1,9725 |
|
Sweden |
1,5651 |
1,2322 |
1,2322 |
|
Denmark |
1,1436 |
0,7169 |
0,9080 |
|
Greece |
1,6479 |
0,7867 |
0,8808 |
|
Italy |
1,4606 |
0,7788 |
0,8249 |
Table
2 summarizes the quarterly values of actual passengers carried in 2020 and
2021, along with the combined forecast values for these periods and the
relative differences between actual and forecast values using the relative
error of the ex-post forecast.
Actual
value and combined forecast of passenger
volume with
relative ex-post forecast error, assuming that there was no
COVID-19 pandemic in the years 2020 and 2021 (in thousands of people)
|
|
Actual number of passengers |
Value of combined
forecast |
Relative ex post forecast error |
|||
|
2020 |
2021 |
2020 |
2021 |
2020 |
2021 |
|
|
Germany |
||||||
|
IQ |
2 222 |
826 |
3 314 |
3 293 |
-32,95% |
-74,91% |
|
IIQ |
1 517 |
2 206 |
6 230 |
6 209 |
-75,65% |
-64,47% |
|
IIIQ |
4 904 |
6 258 |
7 351 |
7 329 |
-33,28% |
-14,62% |
|
IVQ |
1 891 |
3 087 |
3 360 |
3 338 |
-43,72% |
-7,53% |
|
France |
||||||
|
IQ |
2 207 |
1 111 |
3 679 |
3 621 |
-40,01% |
-69,32% |
|
IIQ |
1 151 |
1 822 |
6 685 |
6 628 |
-82,78% |
-72,51% |
|
IIIQ |
4 691 |
4 920 |
9 076 |
9 018 |
-48,31% |
-45,44% |
|
IVQ |
1 703 |
2 139 |
3 367 |
3 309 |
-49,41% |
-35,36% |
|
Spain |
||||||
|
IQ |
3 073 |
1 353 |
5 424 |
5 818 |
-43,34% |
-76,74% |
|
IIQ |
881 |
2 478 |
7 044 |
7 438 |
-87,49% |
-66,68% |
|
IIIQ |
3 957 |
5 355 |
7 592 |
7 986 |
-47,88% |
-32,94% |
|
IVQ |
1 744 |
3 341 |
4 465 |
4 859 |
-60,94% |
-31,24% |
|
Sweden |
||||||
|
IQ |
4 118 |
1 753 |
5 570 |
5 656 |
-26,07% |
-69,01% |
|
IIQ |
2 126 |
3 303 |
8 174 |
8 260 |
-73,99% |
-60,01% |
|
IIIQ |
4 700 |
7 340 |
10 100 |
10 185 |
-53,46% |
-27,94% |
|
IVQ |
2 494 |
4 578 |
4 078 |
4 164 |
-38,84% |
9,95% |
|
Denmark |
||||||
|
IQ |
4 173 |
2 282 |
7 146 |
7 224 |
-41,61% |
-68,41% |
|
IIQ |
3 208 |
4 270 |
9 776 |
9 853 |
-67,18% |
-56,66% |
|
IIIQ |
8 372 |
9 040 |
9 246 |
9 324 |
-9,46% |
-3,04% |
|
IVQ |
3 356 |
5 264 |
5 585 |
5 663 |
-39,91% |
-7,04% |
|
Greece |
||||||
|
IQ |
3 739 |
1 999 |
5 980 |
6 159 |
-37,47% |
-67,54% |
|
IIQ |
3 162 |
5 395 |
10 181 |
10 360 |
-68,94% |
-47,93% |
|
IIIQ |
10 745 |
14 447 |
15 009 |
15 188 |
-28,41% |
-4,88% |
|
IVQ |
3 045 |
4 607 |
5 683 |
5 863 |
-46,42% |
-21,42% |
|
Italy |
||||||
|
IQ |
5 411 |
2 947 |
7 723 |
8 175 |
-29,94% |
-63,95% |
|
IIQ |
4 628 |
5 926 |
13 348 |
13 800 |
-65,33% |
-57,06% |
|
IIIQ |
14 991 |
15 952 |
18 134 |
18 586 |
-17,33% |
-14,17% |
|
IVQ |
4 439 |
5 216 |
7 529 |
7 980 |
-41,04% |
-34,64% |
In
all quarters in both 2020 and 2021, the number of passengers carried by sea
transport was lower than forecast values in all countries. The exception to
this is the fourth quarter of 2021 in Sweden, where the actual value was 9.95%
lower than the forecast value. The biggest differences were in the second
quarter of 2020 and in the first and second quarters of the following year.
2. RESULTS - MARITIME PASSENGER FORECAST TO 2026
As mentioned earlier, the next
step is to produce three variants of forecasts up to 2026. The best-fit trend
functions to the data were used to build forecasts based on real data combined
with combined forecast values. The estimated models, along with basic measures
of fit to real data, are shown in Table 3. Table R2 shows
the coefficient of determination - a statistical measure of how well a model
reflects real data. That is, it illustrates what proportion of the variation in
the dependent variable is explained by the model. Vs is the
coefficient of variation and a measure of the relative variability of data,
expressed as the ratio of the standard deviation to the mean, most often
reported as a percentage. It is used to compare variability in different data
sets.
In
the case of Germany, the function that best describes real values is the
exponential trend function, and it was chosen to build the first forecast.
During the period under study (2013-2019), the number of maritime passengers,
compared to the previous year, declined from year to year. Growth was recorded
only in 2014 and 2019. As assumed, the second forecast was made considering an
average annual decline of 0.68%. Assuming an annual increase of 10%, the number
of passengers in 2026 will reach pre-pandemic levels (Fig. 3).
Tab. 3
Trend functions with measures
of fit to real data
|
Country |
Function |
|
|
|
Germany |
|
0,6240 |
2,12% |
|
France |
|
0,7526 |
1,95% |
|
Spain |
|
0,8839 |
6,61% |
|
Sweden |
|
0,5683 |
2,03% |
|
Denmark |
|
0,8728 |
0,85% |
|
Greece |
|
0,6714 |
3,98% |
|
Italy |
|
0,8127 |
5,59% |
Fig. 3. Forecasts for
Germany up to 2026 (in thousands of people)
In
France, the function that best describes the actual data is the exponential
trend, which was used to make the first forecast. Between 2013 and 2019, the
number of maritime passengers, compared to the previous year, increased only in
2014 and 2017. The other periods recorded declines. Therefore, the second
forecast was made assuming an average annual decline of 1% in the number of
passengers. The third forecast, which assumes an annual 10% increase in the
number of passengers, indicates that even at this rate of growth, the number of
maritime passengers will not catch up to pre-pandemic levels by 2026 (Fig. 4).
Figure
5 shows three forecasts for Spain. The first is based on a linear trend
function. The second assumes an annual increase of 7.93% in the number of
passengers, while the third assumes an annual increase of 10%. However, in both
cases, passenger numbers will not reach pre-pandemic COVID-19 levels until
2026.
Fig.
4. Forecasts for
France up to 2026 (in thousands of people)
Fig.
5. Forecasts for
Spain up to 2026 (in thousands of people)
As
the combined forecasts for Sweden in 2020 indicate a decrease in the number of
passengers carried (compared to 2019, this is a decrease of 5.35%), and an
increase of only 1.23% the following year, the modeling
resulted in forecasts with a decreasing trend (forecast one). Forecast two
assumes an annual increase of 0.56% in the number of passengers. Forecast
three, which assumes an annual 10% increase in the number of passengers,
outperforms forecasts built with combined forecasts in 2024. However, if the pre-pandemic
trend remains unchanged, passenger numbers will not reach the same level as
before COVID-19 until 2026 (Fig. 6).
The
first forecast for Denmark was made based on a linear trend function. The
second forecast assumes an annual increase of 0.53% in the number of
passengers, while the third forecast assumes a 10% increase. If the passenger
growth rate remains at the average annual level of 2013-2019, passenger numbers
will not return to pre-pandemic levels until 2026. Assuming a 10% increase (the
third forecast), the number of passengers carried by sea transport will reach
pre-COVID-19 levels in 2026 (Fig. 7).
For
Greece, the first forecast was determined based on a parabolic trend. The
second forecast assumes an annual increase of 1.04% in the number of maritime
passengers, while the third forecast assumes a 10% increase. The analysis
showed that, in this case, the number of passengers carried will not return to
its pre-pandemic state by 2026 (Fig. 8).
Fig.
6. Forecasts for
Sweden up to 2026 (in thousands of people)
Fig.
7. Forecasts for
Denmark up to 2026 (in thousands of people)
For
Italy, the function characterized by the best fit to the data is the
exponential trend. Based on it, the first forecast was determined. The second
forecast assumes an annual increase of 4.30% in the number of passengers
transported, while the third forecast assumes a 10% increase. As in the case of
Greece, the number of passengers transported by sea will not return to its
pre-pandemic state until 2026.
Fig.
8. Forecasts for
Greece up to 2026 (in thousands of people)
Fig.
9. Forecasts
for Italy up to (in thousands of people)
4. DISCUSSION
- SCENARIOS OF AMBIENT STATES FOR PASSENGER MARITIME TRANSPORT
One of the methods of predicting the future involves
creating scenarios of possible events. They present an anticipated picture of
the future situation and possible behavior resulting
from the impact of external factors. Scenario methods are used to analyze discontinuous changes, that is, changes that are
not a simple extrapolation of processes occurring in the environment. Scenarios
are not static, single images of the future but consist of a whole series of
them, forming a dynamic history. As a result, scenarios should describe not
only the end state but also the path leading to the future.
Recent years have been fraught with a number of what
are referred to as "black swan" events, i.e., rare and unpredictable
events with a huge impact on the future, and the COVID-19 pandemic was
certainly one such event. This period also coincided with fundamental changes
in maritime transportation, including the energy transition and the need to
decarbonize shipping, the digitization of global supply chains, and the
continuing trend toward vertical and horizontal integration in shipping and ports.
The pandemic revealed systemic failures, but it also showed the potential and
need for change. Such change is possible if we understand the systemic causes
of the situation and if we can envision ways to solve systemic problems. For
those involved in maritime transportation, this means assessing how maritime
management can respond to current challenges and better prepare for similar
events in the future.
In this context, the formulation of scenarios of
possible events for passenger maritime transport seems eminently reasonable.
Without going into a broader discourse on the types and classification of
scenarios, we will use scenarios of environmental states, which are qualitative
in nature. They assess the potential impact of particular factors or processes
on a given entity and estimate the probability of their occurrence. Each factor
is considered from the perspective of the trend of change, i.e., increasing,
decreasing, or stabilizing. Then, the (1) optimistic, (2) pessimistic and (3)
most probable scenarios are then developed.
It is important to point out that population growth
and population density are driving demand for transportation, changing its
distribution. Populations around the world are becoming more urbanized, even
though overall demographic growth is slowing in most regions. The average
distance traveled by people increases as disposable
income rises, and this increases demand for passenger transportation. The
interdependence between economic activity and transportation activity has
resulted in a strong statistical correlation between GDP and transportation
demand. Demand for passenger transportation is projected to grow in all regions
of the world. It will triple by 2050, from 44 trillion in 2020 to 122 trillion
passenger kilometers, according to ITF projections
[49]. The distribution of demand will change significantly. OECD countries were
responsible for 43% of global passenger transport in 2020, but their share will
drop to 24% in 2050. The reason is that demand for passenger transport is
growing relatively fast in other countries, especially China and India.
Among the major factors driving the growth of the
global maritime passenger transport market are (1) the increasing number of travelers, a derivative of the growth of the world's
population, as well as the increase in the wealth of the middle classes, and
(2) the dynamic growth of tourism, which has made travel an important part of
the lifestyle. Maritime tourism is becoming increasingly popular due to the
attractiveness of coastal destinations, the opportunity to explore different
regions and access to a variety of onboard attractions, (3) the increase in
demand for luxury leisure activities, which for some people are a symbol of
prestige and social status, (4) the development of port infrastructure to
handle larger ships, increase capacity, and improve the quality of passenger
service.
The above forms the basis for an attempt to identify
scenarios of environmental states for passenger shipping (Tab. 4).
Tab. 4
Scenarios of ambient
states
|
Aspect |
Optimistic scenario |
Pessimistic scenario |
Most likely
scenario |
|
Number of passengers |
Significant increase in passenger numbers, increased
demand for sea travel |
Decline in passenger numbers, reduction in travel
due to health or economic factors |
Gradual increase in passenger numbers, recovery in
demand after period of restrictions |
|
Tourism development |
Dynamic growth of the tourism sector, increased
interest in travel and coastal attractions |
Collapse in the tourism sector, travel restrictions
and health concerns reducing demand |
Gradual return to growth of tourism sector,
increased interest in local destinations |
|
Development
of port infrastructure |
Investment in port expansion and modernization,
increasing capacity, and improving passenger service |
Lack of
investment in port infrastructure, restrictions on passenger port development |
Gradual development of port infrastructure, adapting
to changing needs and increasing demand |
|
Increase in demand for luxury leisure activities |
Increased demand for luxury sea cruises, unique
attractions, and services on board ships |
Demand for luxury travel declines, less interest in
exclusive experiences |
Gradual increase in demand for luxury travel,
increased interest in comfort and unique attractions |
|
Traveler preferences |
Increased interest in sea travel, greater demands on
service quality, safety, and flexibility |
Travel restrictions, health concerns changing traveler preferences |
Gradual recovery of travelers'
confidence, greater interest in local travel, flexible booking and return
options |
|
Travel
policy |
Open borders, visa facilitation, and reduced
bureaucracy |
Tightening border controls, difficulties in
obtaining visas, and travel restrictions |
Controlled border opening, harmonization of travel
procedures |
The
optimistic scenario for the development of passenger maritime transport assumes
dynamic growth in this sector, taking into account a number of positive factors
and trends. In this scenario, the development of passenger maritime transport
goes hand in hand with the growing demand for sea travel. The industry is
taking steps to meet the diverse preferences of travelers
and adapt to changing trends. Investments in port infrastructure and
technological innovations are enriching the traveler
experience, while tourism development and destination appeal are attracting
more and more passengers. The pessimistic scenario describes a situation in
which the sector faces a number of challenges and constraints that could affect
its development. In this scenario, it is important to take appropriate actions
and adapt to changing conditions in order to meet challenges and survive in
difficult times. Key actions include adapting to changing demand by offering
flexible, booking options, promotions, attractively priced packages and special
offers. Diversifying markets and offerings, focusing on local tourists,
promoting smaller and closer destinations, and looking for other market
segments can also help. Offering flexible booking terms, refunds, and
cancellations can also boost traveler confidence. On
the other hand, introducing refund policies for sudden changes in plans,
offering credits for future trips or the ability to change travel dates may
contribute to the attractiveness of sea travel. In the pessimistic scenario,
adaptation, flexibility and, innovation are essential.
The
most likely scenario includes stable growth for the sector, which assumes
steady but moderate passenger growth. The industry is focused on tailoring its
offerings to the diverse preferences of travelers,
continuing to invest in port infrastructure, developing luxury travel options,
promoting sustainability, and ensuring safe and sanitary travel conditions.
Tab. 5
Matching the
obtained forecasts with the environmental scenarios
|
Country |
Optimistic scenario |
Pessimistic scenario |
Most likely
scenario |
|
Germany |
annual increase
of 10% |
annual decrease
of 0,68% |
data with forecast |
|
France |
- |
data
with forecast/annual decrease of 1% |
annual increase
of 10% |
|
Spain |
data with forecast |
- |
annual increase of
7,93% / annual increase of 10% |
|
Sweden |
- |
data
with forecast/annual increase of 0,56% |
annual increase
of 10% |
|
Denmark |
data with forecast |
annual increase
of 0,53% |
annual increase
of 10% |
|
Greece |
data with forecast |
annual increase
of 1,04% |
annual increase
of 10% |
|
Italy |
data with forecast |
annual increase
of 4,30% |
annual increase
of 10% |
Given
the long-term passenger forecasts made, an attempt can be made to assign them
to the scenarios presented (Tab. 5). The probable scenarios include those in
which demand will grow and passenger numbers will return to their pre-pandemic
state. To the pessimistic scenarios were assigned forecasts that indicate a
decline in the number of passengers or its very slow growth, preventing a
return to the pre-pandemic state. It should be noted, however, that the
assignments to the scenarios are not clear-cut. In summary, it can be seen that
in most of the countries analyzed, the most likely
scenario is an annual increase of 10% in the number of passengers. This means
that by 2026 only Germany and Denmark will see the number of maritime
passengers return to pre-pandemic levels.
5. CONCLUSIONS
The
authors' intention was to identify the consequences of changes in passenger
communication behavior as a result of the COVID-19
pandemic for the functioning of maritime passenger transport. The study is not
cognitive in nature but is an empirical study. Therefore, the literature review
was only intended to show the directions of current research in the field of
the relationship between the pandemic and maritime passenger transport.
Differences in the number of sea transport passengers served between
actual and forecast values were indicated in the example of selected EU
countries. The research can be used as comparative material in comparative
analyses. They are cognitive in nature. The presented long-term forecasts may,
however, constitute material for decision-makers in the tourism and transport
sectors. They can also serve as cognitive material in the process of
formulating a strategy or policy for the maritime transport sector.
References
1.
Ahmad Wasim, Ali M. Kutan, Smarth
Gupta. 2021. “Black swan events and COVID-19 outbreak: Sector level evidence
from the US, UK, and European stock markets”. International Review of Economics&Finance 75: 546-557. ISSN: 1873-8036.
DOI: 10.1016/j.iref.2021.04.007.
2.
Antipova Tatiana. 2020. Coronavirus Pandemic
as Black Swan Event. In: Integrated Science in Digital Age 2020. ICIS
2020. Lecture Notes in Networks and Systems, edited by Tatiana Antipova,
vol. 136. Springer, Cham. ISBN 978-3-030-49264-9. DOI:
10.1007/978-3-030-49264-9_32.
3.
Avishai Bernard. 2020. “The Pandemic Isn’t a
Black Swan but a Portent of a More Fragile Global System”. The New Yorker,
April 21. ISSN: 0028-792X. Available at:
https://www.newyorker.com/news/daily-comment/the-pandemic-isnt-a-black-swan-but-a-portent-of-a-more-fragile-global-system.
4.
Barczak Agnieszka, Izabela Dembińska, Dorota Rozmus,
Katarzyna Szopik-Depczyńska. 2022. “The Impact of COVID-19
Pandemic on Air Transport Passenger Markets-Implications for Selected EU
Airports Based on Time Series Models Analysis”. Sustainability 14(4345).
ISSN: 2071-1050. DOI: 10.3390/su14074345.
5.
Billah Baki, Maxwell L. King, Ralph D. Snyder,
Anne B. Koehler. 2006. “Exponential smoothing model selection for forecasting”.
International Journal of Forecasting 22(2): 239-247. ISSN: 1872-8200.
DOI: 10.1016/j.ijforecast.2005.08.002.
6.
Brown Robert G., Richard F. Meyer. 1961. “The
Fundamental Theorem of Exponential Smoothing”. Operations Research 9:
673-685. ISSN: 1526-546. DOI: 10.1287/opre.9.5.673.
7.
Castaldo Sandro, Lara Penco, Giorgia Profumo.
2021. “Cruising in the COVID-19 pandemic era: Does perceived crowding really
matter?” International Journal of Contemporary Hospitality Management
33: 2586-2612. ISSN: 0959-6119. DOI: 10.1108/IJCHM-07-2020-0688.
8.
Chen Qiong, Ying-En Ge, Yui-yip Lau, Maxim A. Dulebenets, Xiaodong Sun, Tomoya Kawasaki, Abderrahman
Mellalou, Xuezong Tao. 2023. “Effects of COVID-19 on
passenger shipping activities and emissions: empirical analysis of passenger
ships in Danish waters”. Maritime Policy & Management 50(6):
776-796. ISSN: 1464-5254. DOI: 10.1080/03088839.2021.2021595.
9.
Donthu Naveen, Anders Gustafsson. 2020.
“Effects of COVID-19 on business and research”. Journal Business Research 117:
284-289. ISSN: 1873-7978. DOI: 10.1016/j.jbusres.2020.06.008.
10. Drake
John. 2021. “Was Covid-19 A Black Swan Event?” Forbes, Nov 11. ISSN:
0015-6914. Available at:
https://www.forbes.com/sites/johndrake/2021/11/11/was-covid-19-a-black-swan-event/?sh=542aa0c7bd36.
11. Eskafi M., P. Taneja, G.F. Ulfarsson. 2022. “Cruising Under
and Post the COVID Pandemic: Toward Realization of the United Nations’
Sustainable Development Goals”. Journal of Marine Science and Engineering 10(910).
ISSN: 2077-1312. DOI: 10.3390/jmse10070910.
12. Gargoum Suliman A., Ali S. Gargoum.
2021. “Limiting mobility during COVID-19, when and to what level? An
international comparative study using change point analysis”. Journal of
Transport & Health 20(101019). ISSN: 2214-1413. DOI:
10.1016/j.jth.2021.101019.
13. Holland
Jennifer, Tim Mazzarol, Geoffrey Soutar, Suellen Tapsall,
Wendy Elliott. 2021. “Cruising through a pandemic: The impact of COVID-19 on
intentions to cruise”. Transportation Research Interdisciplinary
Perspectives 9(100328). ISSN: 2590-1982. DOI: 10.1016/j.trip.2021.100328.
14. Holland
Jennifer, Tim Mazzarol, Geoffrey Soutar, Suellen Tapsall,
Wendy Elliott. 2021. “Cruise passengers’ risk reduction strategies in the wake
of COVID-19”. Asia Pacific Journal of Tourism Research 26(11): 1189-1206.
ISSN: 17416507. DOI: 10.1080/10941665.2021.1962376.
15. Huang
Chaolin, Yeming Wang, Xingwang
Li, Lili Ren, Jianping Zhao, Yi Hu, Li Zhang, Guohui
Fan, Jiuyang Xu, Xiaoying Gu, Zhenshun
Cheng, Ting Yu, Jiaan Xia, Yuan Wei, Wenjuan Wu, Xuelei
Xie, Wen Yin, Hui Li, Min Liu, Yan Xiao, Hong Gao, Li Guo, Jungang
Xie, Guangfa Wang, Rongmeng
Jiang, Zhancheng Gao, Qi Jin, Jianwei Wang, Bin Cao.
2020. “Clinical features of patients infected with 2019 novel coronavirus in
Wuhan, China”. Lancet. ISSN: 0140-6736. DOI:
10.1016/S0140-6736(20)30183-5.
16. International
Maritime Organization (IMO). 2020. Circular Letter No.4204/Add.1.
Available at:
http://www.imo.org/en/MediaCentre/HotTopics/Documents/CL.4204-Add.1%20English.pdf.
17. Kovačević
Slaviša, Mladen Rebić, Drago Kurušić. 2021. “The Impact of the COVID-19
Pandemic on the Labor Market of Bosnia and Herzegovina: Application of the
Exponential Equalization Methods. Researching Economic Development and
Entrepreneur-ship in Transition Economies, Geopolitics and the Political Economy
of Conflict in the Balkans and the Eastern Medi-terranean: Refugees, Energy
Sector and Prospects for the Future”: 116-119. Available at:
https://www.redete.org/assets/content/conf-prog/conf-proceedings-2021.pdf#page=106.
18. Krantz
Steven G. 2021. “Harmonic analysis”. WIREs Comp Stat 3: 163-167. ISSN:
1939-0068. DOI: 10.1002/wics.143.
19. Mancuso
Aline Castello Branco, Werner Liane. 2013. “Review of combining forecasts
approaches”. Independent Journal of Management & Production 4(1).
ISSN 2236-269X. DOI: 10.14807/ijmp.v4i1.59.
20. Mannarini Gianandrea, Mario Leonardo Salinas, Lorenzo Carelli,
Alessandro Fassò. 2022. “How COVID-19 Affected GHG Emissions of Ferries
in Europe”. Sustainability 14: 5287. ISSN: 2071-1050. DOI:
10.3390/su14095287.
21. March
David, Kristian Metcalfe, Joaquin Tintoré, Brendan J. Godley. 2021. “Tracking
the global reduction of marine traffic during the COVID-19 pandemic”. Nature
Communications 12: 2415. ISSN: 2041-1723. DOI: 10.1038/s41467-021-22423-6.
22. Millefiori
Leonardo M., Paolo Braca, Dimitris Zissis, Giannis Spiliopoulos, Stefano Marano,
Peter K. Willett, Sandro Carniel. 2021. “COVID-19 impact on global maritime
mobility”. Scientific Reports 11: 18039. ISSN: 2045-2322. DOI:
10.1038/s41598-021-97461-7.
23. Mishra
Pramod Kumar. 2020. “COVID-19, Black Swan events and the future of disaster
risk management in India”. Progress in Disaster Science 8: 100137. ISSN:
2590-0617. DOI: 10.1016/j.pdisas.2020.100137.
24. Mocerino
Luiga, Franco Quaranta. 2022. “How emissions from cruise ships in the port of
Naples changed in the COVID-19 lock down period”. Proceedings of the
Institution of Mechanical Engineers, Part M: Journal of Engineering for
the Maritime Environment 236(1):
125-130. ISSN: 1475-0902. DOI: 10.1177/14750902211028421.
25. Montgomery
Douglas C., Cheryl L. Jennings, Murat Kulahci.
“Introduction to Time Series Analysis and Forecasting”. January 2008.
Publisher: Wiley. ISBN: 9780471653974.
26. Moreno-Luna
Libertad, Rafael Robina-Ramírez, Marcelo Sánchez-Oro Sánchez, Jose Castro-Serrano.
2021. “Tourism and Sustainability in Times of COVID-19: The Case of Spain”. International
Journal of Environmental Research and Public Health 18(4): 1859. ISSN:
1660-4601. DOI: 10.3390/ijerph18041859.
27. Mujal-Colilles Anna, Javier Nieto Guarasa,
Jordi Fonollosa, Toni Llull, Marcella Castells-Sanabra.
2022. “COVID-19 impact on maritime traffic and corresponding pollutant
emissions. The case of the Port of Barcelona”. Journal of Environmental
Management 310: 114787. ISSN:
1095-8630. DOI: 10.1016/j.jenvman.2022.114787.
28. Murphy,
J. 2019. “A Course in Harmonic Analysis”. AOER Course Materials 2.
Available at: https://scholarsmine.mst.edu/oer-course-materials/2.
29. Ng
Qin Xiang, Michelle Lee Zhi Qing De Deyn, Wayren
Loke, Hwei Wuen Chan. 2020. “A framework to deal with
uncertainty in the age of COVID-19”. Asian Journal Psychiatry 54:
102263. ISSN: 1876-2026. DOI: 10.1016/j.ajp.2020.102263.
30. Nishiura
Hiroshi, Sung-mok Jung, Natalie M. Linton, Ryo
Kinoshita, Yichi Yang, Katsuma
Hayashi, Tetsuro Kobayashi, Baoyin Yuan, Andrei R.
Akhmetzhanov. 2020. “The Extent of Transmission of Novel Coronavirus in Wuhan,
China”. Journal of Clinical Medicine 9: 330. ISSN: 2077-0383. DOI:
10.3390/jcm9020330.
31. Nishiura
Hiroshi, Natalie M. Linton, Andrei R. Akhmetzhanov. 2020. “Initial Cluster of
Novel Coronavirus (2019-nCoV) Infections in Wuhan, China is Consistent with
Substantial Human-to-Human Transmission”. J. Clin. Med. 9: 488. ISSN
2077-0383. DOI: 10.3390/jcm9020488.
32. Ostertagova Eva, Oskar Ostertag. 2012. “Forecasting
Using Simple Exponential Smoothing Method”. Acta Electrotechnica
et Informatica 12. ISSN: 1338-3957. DOI: 10.2478/v10198-012-0034-2.
33. Ostertagova Eva, Oskar Ostertag. 2011. “The
Simple Exponential Smoothing Model”. Proceedings of the Modelling of
Mechanical and Mechatronic Systems. Košice,
Slovakia. 20-22 September 2011. Available at:
https://www.scribd.com/document/217021224/Smoothing.
34. Pan
Tianyu, Fang Shu, Miranda Kitterlin-Lynch, Eric Beckman. 2021. “Perceptions of
cruise travel during the COVID-19 pandemic: Market recovery strategies for
cruise businesses in North America”. Tourism Management 85. ISSN:
0261-5177. DOI: 10.1016/j.tourman.2020.104275.
35. Phillips
Wendy, Jens K. Roehrich, Dharm Kapletia. 2023. “Responding
to information asymmetry in crisis situations: innovation in the time of the
COVID-19 pandemic”. Public Management Review 25(1): 175-198. ISSN:
1471-9037. DOI: 10.1080/14719037.2021.1960737.
36. Poo
Mark Ching-Pong, Zaili Yang, Yui-yip Lau. 2024. “Vulnerability
analysis of cruise shipping in ASEAN countries facing COVID-19 pan-demic”. Ocean
& Coastal Management 248, 1
February 2024. ISSN: 1873-524X. DOI: 10.1016/j.ocecoaman.2023.106919.
37. Silva da Ana Lucia Rodrigues. 2021. “An overview of the impact of
COVID-19 on the cruise industry with considerations for Florida',
Transportation Research Interdisciplinary”. Perspectives 10. ISSN:
2590-1982. DOI: 10.1016/j.trip.2021.100391.
38. State
of the Region: Special Report on Covid-19, PECC International Secretariat.
PECC, Singapore 2020. P. 33-34. Available at:
https://www.pecc.org/state-of-the-region-reports/286-special-report-covid-19
39.
StatLink. DOI:
http://dx.doi.org/10.1787/888933971993.
40. Taleb
Nassim Nicholas. 2008. The Black Swan: The Impact of the Highly Improbable.
Penguin Books Ltd. ISBN: 978-0141034591.
41. Tapaninen Ulla, Riina Palu. 2022. “Recovery of ro-pax ferry traffic from covid-19 under tightening
environmental regulations: case Helsinki-Tallinn”. Journal of Shipping and
Trade 7(10). ISSN: 2364-4575. DOI: 10.1186/s41072-022-00112-x.
42. Thompson
Robin N. 2020. “Novel Coronavirus Outbreak in Wuhan, China, 2020: Intense
Surveillance Is Vital for Preventing Sustained Transmission in New Locations”. J.
Clin. Med. 9(498). ISSN: 2077-0383. DOI: 10.3390/jcm9020498.
43. Usman
Muhammad, Li Yuyan, Mudassir Husnain, Muhammad Waheed Akhtar. 2023. “COVID-19,
travel restrictions and environmental consequences”. Environment,
Development and Sustainability 18: 1-21. ISSN: 1573-2975. DOI:
10.1007/s10668-023-03146-8.
44. Üyesi Ögretim, Ibrahim Orkun Oralpp. 2019. “Comparison of
The Winters’ Seasonality Exponential Smoothing Method with The Pegels’ Classification: Forecasting of Turkey’s Economic
Growth Rates”. Anadolu Üniversitesi Sosyal Bilimler Dergisi
19(3): 275-294. ISSN: 2667-8683. Available at:
https://dergipark.org.tr/en/download/article-file/828959.
45. WHO-convened
Global Study of Origins of SARS-CoV-2: China Part. Joint WHO-China Study. 14 January-10 February 2021.
Joint Report. Available at:
https://www.who.int/publications/i/item/who-convened-global-study-of-origins-of-sars-cov-2-china-part.
46. Winkler
Robert L., Spyros Makridakis. 1983. “The Combination
of Forecasts”. Journal of the Royal Statistical Society Series A
(General), 146(2): 150-157. ISSN: 00359238. DOI: 10.2307/2982011.
47. Woltés Vicente, Anabel Fernández-Mesa. 2023. “VUCA
environments before the recession caused by covid-19: a systematic literature
review”. Tec Empresarial 17(1): 53-69. ISSN: 1659-3359. DOI:
10.18845/te.v17i1.6539.
48. World
Travel & Tourism Council (2021). Travel & Tourism. Economic Impact
2021. Available at: https://wttc.org/Research/Economic-Impact.
49. Worley
Christopher G., Claudy Jules. 2020. “COVID-19’s Uncomfortable Revelations About
Agile and Sustainable Organizations in a VUCA World”. The Journal of Applied
Behavioral Science 56(3): 279-283.
ISSN: 0021-8863. DOI: 10.1177/0021886320936263.
Received 19.12.2024; accepted in revised form 20.04.2025
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