Analysis of air passenger transport dynamics in selected European countries in the years 2004 – 2015 Analysis of air passenger transport dynamics in selected European countries in the years 2004 – 2015

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(1)PRACE NAUKOWE POLITECHNIKI WARSZAWSKIEJ z. 124. Transport. 2019.

(2) $, Justyna Tomaszewska Aviation Faculty, Polish Air Force University. Marta Woch Institute of Aeronautics and Applied Mechanics, Warsaw University of Technology. Mariusz Zieja Air Force Institute of Technology, Division for IT Support of Logistics. ANALYSIS OF AIR PASSENGER TRANSPORT DYNAMICS IN SELECTED EUROPEAN COUNTRIES IN THE YEARS 2004 – 2015 Manuscript delivered, May 2019. Abstract: In the era of globalization and permanent travel of people between far away regions of the world, air transport is one of the most important means of transport. In this contribution, the chainlinked relative increments for all European countries is presented as well as their gross domestics products. Presented countries are divided into five groups according to the decreases and increases in traffic volumes. Keywords: Chain-linked relative increments, Air Transport, GDP. 1. INTRODUCTION In recent years, there has been a large growth in passenger transport and air freight. Longdistance air transport has an advantage over other modes of shipping. Bigger and bigger planes are being built for even a few hundred people and more and more supersonic planes are taking flight. Modernity and rapidity of aircraft require constant technical progress and huge financial resources. Modern aviation is characterized by an extremely dynamic technological development in the way of transport. Aviation is an area where innovative scientific and technological achievements are applied, and at the same time aviation requires technological solutions resulting in the evolution of new fields in industry and science..

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(6) ?, Marta Woch, Mariusz Zieja. 2. METHODOLOGY An analysis of time series boils down to determining the changes in the phenomenon by its increments and dynamic indices. The increment is one of the simplest measures and can be defined as: [1, 2, 3]: single-base absolute increments, saying how much (in natural units of measurement) the value of the analyzed phenomenon has changed in relation to the base term, chain absolute increments determining how much (in natural units of measurement) the level of the analyzed phenomenon increased or decreased in relation to the previous period, single-base relative increments, allowing to determine how much the percentage changed the level of the analyzed phenomenon in the analyzed period, in relation to the reference one, chain relative increments, indicating how much the percentage changed the level of the analyzed phenomenon in the analyzed period, in relation to the previous one. The increase is an unmodified value or expressed as a percentage, if it takes negative values, it indicates a decrease in the phenomenon over time, and if it is positive, it indicates its increase. In this article relative chain increments have been used. The method based on the relationship between two or more variables was chosen to be the main method of the analysis. This method is usually called correlation; however, in this analysis it is nonsensical correlation, an example being the correlation between a decreasing number of passengers in country A and country B. Such a correlation can be high simply because both variables are related to the state of economy [3]. To characterize this behavior, parametric and nonparametric measures may be used to assess the relationship between the two characteristics. In this paper, a parametric measure called the Pearson coefficient has been applied, which determines the collinearity of the two features. It is described by the following formula [1, 2]: >=. A3CF(BC BD )(C E) ?-/ = , @- @/ GA3CF(BC BD ) A3CF(C E). . where cIJ = KLMN GALOF(xO xE) ALOF(yO yE) is an estimator of correlation cov(X,Y); @- , @/ are estimators of standard deviation. There are two types of techniques of separating developmental tendencies - mechanical and analytical methods. First of them does not provide information, which is important to make a forecast analysis The principle of analytical methods is to find such a mathematical function (trend function) that best describes the changes in the phenomenon over time resulting from the influence of root causes. To establish the trend function, the appropriate regression analysis approach is usually used. The starting point is the assumption of an analytical form of the trend function, in which time is an independent variable. For example, if we take a linear trend assumption, then the trend function will take on a form: P = Q + R.

(7) Analysis of air passenger transport dynamics in selected European countries in the years 2004 …. 173. where t – time, a – linear coefficient, b – free coefficient. The parameters of the linear trend function are estimated using the least squares method =. S A3TFU T Q A3TFU T Q A3TFU , S A3TFU (A3TFU ). the coefficient of linear trend (a) indicates, how on average, the level of the examined phenomenon changes from period to period; R = VT ,D A3TFU D = S+1 The free term (b) in the trend line equation is interpreted as a theoretical level of phenomenon for t = 0. The matching of the linear trend function is evaluated by determining the goodness of the fit (R2): A3TFU(YT VT ) X = 3 . ATFU(T VT ) In regression, the R2 coefficient of determination is a statistical measure of how well the regression predictions approximate the real data points. An R2 of 1 indicates that the regression predictions perfectly fit the data. [1].. 3. DYNAMIC ANALYSIS The data used for this analysis has been taken from Eurostat web page [5]. The analysis covered data originating from 31 European countries, including 28 members of the European Union and three associated countries (Norwegian, Iceland and Swiss). The survey material was taken from the databases of the European Statistical Office (Eurostat). The data covered the period from January 2004 to the end of December 2015 (12 years). In order to analyse the dynamics of passenger air transport in the countries under investigation, single-basic growth indices and average rates of change were determined [7]. The year 2004 was taken as the basis. In 2005, only Malta experienced a slight decrease in the number of passengers carried (by 0.3%) compared to 2004. In other countries, there were increases, with the largest increase in Latvia (by 78.5%). In 2006, only Malta and Cyprus transported fewer passengers than in the previous year, a decrease of 2.1% and 0.7% respectively; the largest increase occurred in Poland (91.3%). The year 2007 proved to be a very good year. In all the countries analysed there was an increase in traffic compared to 2006, with the largest increase in Romania (by 41.6%). Unfortunately, in 2008 in six countries, i.e. Spain, the Netherlands, Iceland, Hungary, Italy,.

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(11) ?, Marta Woch, Mariusz Zieja. the United Kingdom and Spain, there was a decrease in the volume of transport, while in other countries there were still increases, but not as high as in the previous year. In 2009, the downward trend deepened. Thirty countries recorded declines, with the largest collapse occurring in Lithuania (a decrease of 26.9%). The only country where more passengers were carried than in the previous year was Latvia (a 10.1% increase). The situation improved in 2010. Twenty-five out of thirty-one countries surveyed had a growth in passenger transport compared to the previous year, with Lithuania accounting for the largest increase (24.5%). Six countries, i.e: the Czech Republic, Greece, Ireland, Slovakia, Slovenia and the United Kingdom showed decreases, the largest in Ireland with 12.2%. In 2011, only Cyprus, Slovenia and Slovakia transported fewer passengers than in 2010, while other countries recorded increases (the highest in Estonia - 37.8%). Regrettably, 2012 brought a deterioration in the situation in nine countries. The Czech Republic, Greece, Spain, Latvia, Hungary, Italy, Latvia, Romania, Slovakia, Slovenia and Spain experienced a decline in transport. In other countries, more passengers were carried in comparison with the previous year, with the highest increase in Lithuania (17.5%). In 2013, six countries, i.e. Austria, Cyprus, Estonia, Spain, Slovakia and Italy, recorded a decrease in transport volumes, while the remaining countries noted increases, the highest in Iceland (16.3%). The year 2014 appeared to be unfavourable only for France, where there was a 1.2% decrease in traffic, while in other countries there were increases (the highest in Iceland was 20.6%). All the countries surveyed experienced an increase in traffic in 2015 (7.7% on average), with the best performance achieved again in Iceland (25.5%). The transport dynamics in the countries surveyed are shown in Figure 1 and 2, for better visibility countries have been divided into two groups.. Fig. 1. Passenger transport dynamics in the first group of countries surveyed, 2004 was taken as the base year.

(12) Analysis of air passenger transport dynamics in selected European countries in the years 2004 …. 175. Fig. 2. Passenger transport dynamics in the second group of countries surveyed, 2004 was taken as the base year. 3.1. MONOTONICITY Considering the decreases and increases in traffic volumes (Figure 1–2), several groups with identical monotony of the dynamic curves were identified among the analyzed countries (Table 1). The other countries were not taken into account as they did not show the same dynamics as any other country of the analysed group i.e. for Cyprus decrease has been observed in years: 2006, 2009, 2011, 2013, in Great Britain decrease has been observed in years 2008-2010. Table 1 Countries with identical monotony Country. Classification. Country Netherlands Iceland. Belgium Denmark Finland Lithuania Luxembourg Germans Norway Poland Portugal Switzerland Sweden. Description. Classification Group B. Decrease in traffic only in 2008 and 2009. Group C. Decrease in traffic only in 2009 and 2013. Group D. Decrease in traffic only in 2008, 2009, 2012 and 20013. Austria. Group A. Estonia Decrease in traffic only in 2009 Span. Italy. Description.

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(16) ?, Marta Woch, Mariusz Zieja. Group A includes eleven countries where the decrease in transport in relation to the previous year was recorded only in 2009. There are very strong mutual relations (dependences) between the ten countries in this group (Table 2). Only the relationships between Finland and Luxembourg as well as between Finland and Portugal are statistically insignificant. In this group Poland and Lithuania are characterized by much higher transport dynamics. Table 2. Germans. Norway. 0.97 0.99 0.93 1 0.97 0.98 0.95 0.98 0.96 0.97 0.99. 0.97 0.97 0.89 0.97 1 0.96 0.92 0.99 0.99 0.95 0.96. 0.99 0.99 0.96 0.98 0.96 1 0.96 0.98 0.97 0.99 0.99. 0.94 0.95 0.95 0.95 0.92 0.96 1 0.93 0.91 0.98 0.96. 0.99 0.99 0.98 0.97 0.91 0.88 0.98 0.96 0.99 0.99 0.98 0.97 0.93 0.91 1 0.996 1 0.996 0.97 0.96 0.97 0.95. Sweden. Luxembourg. 0.93 0.94 1 0.93 0.89 0.96 0.95 0.91 0.88 0.96 0.98. Switzerland. Lithuania. 0.98 1 0.94 0.99 0.97 0.99 0.95 0.98 0.97 0.98 0.99. Portugal. Finland. 1 0.98 0.93 0.97 0.97 0.99 0.94 0.99 0.99 0.98 0.97. Poland. Denmark. Belgium Denmark Finland Lithuania Luxembourg Germans Norway Poland Portugal Switzerland Sweden. Belgium. Group A dependency matrix. 0.98 0.98 0.96 0.97 0.95 0.99 0.98 0.97 0.96 1 0.98. 0.97 0.99 0.98 0.99 0.96 0.99 0.96 0.97 0.95 0.98 1. In Group B countries, i.e. the Netherlands and Iceland, the upward trend was disrupted by declines in transport in 2008 and 2009. Both countries are strongly related (Table 3), with Iceland characterised by higher transport dynamics. Table 3 Group B dependency matrix Netherlands Iceland. Netherlands 1 0.96. Iceland 0.96 1. Group C (decrease in transport in 2009 and 2013) includes Austria and Estonia. Both countries are highly dependent (Table 4). A higher transport dynamic in the period under review was recorded in Estonia. Table 4 Group C dependency matrix Austria Estonia. Austria 1 0.96. Estonia 0.96 1. Group D (decrease in transport in 2008, 2009, 2012 and 2013) includes Spain and Italy. Although transport volumes in these countries are correlated, this correlation is not statistically significant at the assumed level of significance (p < 0.002). (Table 5)..

(17) Analysis of air passenger transport dynamics in selected European countries in the years 2004 …. 177. Table 5. Spain Italy. Group D dependency matrix Spain Italy 1 0.85 1 0.85. 3.2. LINEARITY Taking under consideration that for eleven countries the same dynamic in the number of travelled passengers has been observed – the descending in the year 2009. These group has been chosen for further analysis to find causes or similarities. When analysing changes in the phenomenon in the longer term, it is also necessary to determine whether there are any regularities of changes occurring over time, i.e. to distinguish the so-called development tendency. It is recommended that the selected function is as simple as possible. In most cases, a linear trend form is used as a function that approximates a trend. In order to establish the possibility of matching a linear trend, determination coefficients have been determined and the results of calculations are presented in Table 6. Table 6 Determination factor for trend linearity for eleven countries with the same dynamic in the number of travelled passengers Determination factor for Country Determination factor for Country trend linearity (R2). Belgium Denmark Finland Germany Lithuania Luxembourg. 0.968 0.882 0.91 0.933 0.895 0.766. trend linearity (R2). Norway Poland Portugal Sweden Switzerland. 0.969 0.917 0.939 0.896 0.981. For ten of the eleven selected countries a very good (R2>0.9) or good (R2>0.8) factor for trend linearity have been observed. The exception has been noticed for Luxembourg (R2<0.8). 4. GROSS DOMESTIC PRODUCT In further analysis, the authors will concentrate on group A countries and investigate whether gross domestic product (GDP) rise could be an aspect determining transport growth. According Eurostat [4] GDP is defined as the value of all goods and services produced less the value of any goods or services used in their creation. The calculation of the annual growth rate of GDP volume is intended to allow comparisons of the dynamics of economic development both over time and between economies of different sizes. For measuring the growth rate of GDP in terms of volumes, the GDP at current prices are.

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(21) ?, Marta Woch, Mariusz Zieja. valued in the prices of the previous year and the thus computed volume changes are imposed on the level of a reference year; this is called a chain-linked series. Accordingly, price movements will not inflate the growth rate. Table 7 Changes in the percentages of passengers’ number of air transport services and GDP in particular groups (on the grey background are presented the changes in GDP, on white one – chain-linked relative increments) Country. 2005 2006 2.0 7.7 Belgium 2.1 2.5 5.4 3.8 Denmark 2.3 3.9 4.5 9.0 Finland 2.8 4.1 7.2 5.6 Germany 0.7 3.7 24.5 Lithuania 7.7 7.4 1.9 3.9 Luxembourg 3.2 5.2 9.7 11.7 Norway 2.6 2.4 16.2 91.3 Poland 3.5 6.2 6.0 6.5 Portugal 0.8 1.6 5.4 14.5 Sweden 2.8 4.7 8.8 10.2 Switzerland 3.1 4.0. 2007 2008 8.9 6.3 3.4 0.8 4.3 1.2 0.9 -0.5 8.2 2.9 5.2 0.7 6.0 1.2 3.3 1.1 21.8 16.6 11.1 2.6 2.3 3.6 8.4 -1.3 7.8 3.8 3.0 0.5 26.6 9.2 7.0 4.2 12.2 2.8 2.5 0.2 4.0 2.9 3.4 -0.6 7.7 5.9 4.1 2.2. 2009 2010 2011 2012 2013 2014 -2.8 6.1 10.2 3.1 1.6 9.4 -2.3 2.7 1.8 0.2 0.2 1.3 -8.5 9.2 6.1 2.7 3.4 6.2 -4.9 1.9 1.3 0.2 0.9 1.6 -6.9 3.2 5.2 0.4 0.6 3.6 -8.3 3.0 2.6 -1.4 -0.8 -0.6 =4.3 5.1 4.9 1.8 0.9 3.0 -5.6 4.1 3.7 0.5 0.5 2.2 -26.9 24.5 15.9 17.5 10.4 9.2 -14.8 1.6 6.0 3.8 3.5 3.5 -9.3 4.6 14.4 3.1 14.5 12.2 -4.4 4.9 2.5 -0.4 3.7 4.3 -2.8 6.5 9.9 6.8 5.8 2.3 -1.7 0.7 1.0 2.7 1.0 2.0 -8.7 7.8 12.2 5.8 6.5 10.5 2.8 3.6 5.0 1.6 1.4 3.3 -3.8 6.7 7.1 2.5 5.5 9.5 -3.0 1.9 -1.8 -4.0 -1.1 0.9 -9.3 5.6 11.5 2.0 3.6 4.1 -5.2 6.0 2.7 -0.3 1.2 2.6 -1.9 4.5 10.1 4.3 2.3 4.2 -2.2 3.0 1.7 1.0 1.9 2.4. 2015 7.7 1.7 3.7 2.3 1.5 0.5 3.9 1.7 10.7 2.0 8.9 3.9 0.1 2.0 12.6 3.8 10.5 1.8 3.7 4.5 4.1 1.3. Source: Own calculations based on Eurostat data. Decrease in air traffic only in 2009 Referring to Table 7 in all (except Poland) countries belonged to Group A an decrease in the number of passengers was observed in 2009. In the years 2005-2007, all countries of Group A recorded GDP growths. The year 2008 ended with a decrease in GDP in three countries, i.e. Denmark, Luxembourg, and Sweden. A year later, the crisis also affected other countries of this group. The exception was Poland (increase by 2.8%). In 2010, all group A countries recorded a GDP growth in all group A countries, while in 2011, only Portugal recorded a GDP decline. The year 2012 ended with a decrease in GDP in the four countries of the group, i.e. the Czech Republic, Slovakia, and Slovakia: Finland, Luxembourg, Portugal, and Sweden. In 2013 GDP declines were observed in Finland and Portugal. All Group A countries except Finland saw GDP growth in 2014. In 2015, all group A countries ended up with GDP growth. In 2008, before the decrease in transport, all group A countries were affected by GDP growth deceleration and ten also by transport growth deceleration (Luxembourg is the exception)..

(22) Analysis of air passenger transport dynamics in selected European countries in the years 2004 …. 179. To check how strong, if at all, is correlation between changes in the percentages of passengers’ number of air transport services and GDP in particular groups, the correlation factor has been determined for every analysed country. The results are presented in table 8. Overall, Lithuania, Denmark, Germany and Finland have the correlation factor greater than 0.8, compared to Luxembourg which has the factor less than 0.5. However, none of the counties shown the strong correlation between changes in the percentages of passengers’ number of air transport services and GDP. Table 8 Correlation factor between changes in the percentages of passengers’ number of air transport services and GDP in particular groups Country Correlation factor Country Correlation factor Belgium 0.754 Norway 0.599 Denmark 0.851 Poland 0.629 Finland 0.810 Portugal 0.743 Germany 0.838 Sweden 0.778 Lithuania 0.906 Switzerland 0.771 Luxembourg 0.479 Source: Own calculations based on Eurostat data. 5. CONCLUSIONS Passenger transport dynamics has been presented as a function of years and its correlation with the GDP. It was observed that the number of travelled people is growing as a linear function of time. The authors’ intention was a statistical analysis and an indication of trends in modern passenger aviation, not an interpretation of data in financial and economic aspects. However, it would be interested to find a common cause of growth or decline the number of passengers in the same time in analysed countries. Boeing and Airbus analysts assume that there are factors that cause more traffic growth than GDP growth i.e. replacing the old aircrafts with new ones, continuation of the development of the low-cost segment, liberalisation of air transport services as well as replacement of small aircraft with larger aircraft capacity [8]. Such suggestions by economist may be a valuable contribution to understanding the above subject and are highly welcome. References 1. Aczel A.: ) #

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(30) F tu Warszawskiego, Warszawa 2007. 4. Sobczyk M.: Statystyka, Wydawnictwo Naukowe PWN, Warszawa 2008. 5. https://ec.europa.eu/eurostat/data/database 6. Giudici P.: Applied Data Mining Statistical Methods for Business and Industry, JohnWiley & Sons, Chichester, UK, 2003. 7. Jacyna M.: Wybrane zagadnienia modelowania systemów transportowych. Oficyna Wydawnicza Politechniki Warszawskiej, Warszawa 2009..

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