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Sustainability 2018, 10(5), 1318; https://doi.org/10.3390/su10051318

Article
The Impact of Intelligent Transportation System Implementations on the Sustainable Growth of Passenger Transport in EU Regions
1
Institute of Management, Administration and Logistics, Faculty of Organization and Management, Silesian University of Technology, Roosevelta 26 Street, Zabrze 41-800, Poland
2
Department of Engineering Management and Logistics Processes, Faculty of Applied Sciences, WSB University, Cieplaka 1C Street, Dąbrowa Górnicza 41-300, Poland
*
Author to whom correspondence should be addressed.
Received: 29 March 2018 / Accepted: 22 April 2018 / Published: 24 April 2018

Abstract

:
This article discusses original studies that demonstrate the relation between developed elements of the transportation network (road system density; railway system density; number of regional railway and bus connections, length of regional railway and bus connections, online accessibility to transportation services and other services related to the development of IT techniques to benefit mass transit) and the regional GNP. A new development relative to preceding studies (as quoted) is that the correlation coefficients calculated do not indicate any essential interrelations between elements of the transport system, or even the number of regional passenger transport services and regional GNP. A determination of the remaining data interrelations indicated the elements of the network which are considered essential to the development of mass transit, as resulting from a study carried out for the first time in 2015 for the Górnośląska-Zagłębioska Metropolis. Considering the fact that the number of railway connections has proven to be the most important determinant of the overall number of passenger transport services, the second part of the article presents studies that focus on the modeling of the railway network, applying the graph theory (extensively applied for ITS). Selected optimized models were analyzed and assessed in terms of possible implementability of specific improvements and the resultant growth in the number of passenger transport services. The research method applied was not novel, but the conclusions drawn from it were surprising, as they indicated that an optimized network of railway connections would not cause any significant increase in the number of passenger transport services. Successive surveys (supplementing statistical analyses) have confirmed the importance of ITS in increasing the share of mass transit in overall transit. (1) The study was carried out in Polish regions, with particular emphasis on Silesia. (2) Its conclusions emphasize the importance of data accumulated for ITS in decision-making processes aiming to ensure the sustainable development of mass/passenger transport. The article confirms a hypothesis which claims that “modeling the regional public transportation grid, applying the principles of ITS, stimulates a growth in the share of passenger transport in the overall bulk of transport, thus contributing to the sustainable development of the region”.
Keywords:
ITS; mass transit; development of EU regions

1. Introduction

The article contains original studies that present the relationship between developed elements of the transport network and the GNP of any selected EU region. Particular emphasis is put on the interdependencies between regional passenger transport services and the regional GNP. The authors claim that the results of these studies would facilitate any decisions concerning railway infrastructure and railway connections. The authors propose that data is collected and used for ITS to make investment and organizational decisions. They have proven that the current data is dispersed and non-cohesive, which hinders its processing. The variable correlation results obtained in the study are new. Before, researchers would claim that the development of infrastructure elements was strongly correlated with GNP growth, whereas our study has shown that it is the contrary. Seeking an interdependency between variables, a single element was found, i.e., the number of regional railway connections, which indicates a higher (although not essential with GNP), but nonetheless essential relation with the number of passenger transport services carried out via regional railways. With this result in mind, a study was commenced to optimize the railway network in a selected region. The study can be construed as new due to its geographic range. Optimization of the railway network was carried out for the first time for the Śląsko-Zagłębiowska Metropolis established in 2017. A known graph theory was applied in the optimization process. The simulation has indicated that the proposed network optimization can affect (although insignificantly) the number of passenger transport services carried out with the use of regional railways. The authors recommend periodical modeling of the railway network, applying ITS, due to the Metropolis development dynamic. Unsatisfied with the results of past studies which have not led to the formulation of any specific postulates regarding the sustainable transport policy, the authors conducted their successive studies—ones that were based on surveys. The respondents confirmed that the application of ITS which synchronize the various modes of regional transport could convince them to switch to means of mass transit.
Our comprehensive study of regional transport is an innovative research model, which could be applied when making decisions regarding infrastructural investments and organization of transport network.

1.1. Infrastructure and Regional Development

Many published works have displayed the relationship between infrastructure and the sustainable development of cities and regions [1,2,3,4,5,6].
The majority of studies suggest that the population of a given area is directly related to economic growth, although there are no exact estimates as to how investments in urban infrastructure, including transport infrastructure, contribute to this positive effect. A transport infrastructure that facilitates the development of urban areas and reduces the costs of transport should theoretically be related to faster development of cities and regions, and to the faster development of the entire economy, although a surprisingly small number of studies have scrutinized these effects (probably due to limited access to data). The smart city concept, which has recently gained considerable momentum, promotes the growth of social capital (development of knowledge and social activity) through the development of telecommunication, education and research infrastructure. What is noteworthy, is that infrastructure is an important factor in increasing the attractiveness of a city/region for potential investors, business owners, workforce, which translates into GNP growth [7]. As Glaeser and Gottlieb (2009) [8] have indicated, the correlation between the size of urban population and the level of growth (regional GNP growth) is very close. In turn, Ciccone and Hall (1999) [9] have proven that this relationship is causal by nature—the more people live in large cities, the higher productivity is in this region. This is consistent with the theory of growth coined by Robert Lucas, who accentuated the spread of knowledge in urban areas. These conclusions are confirmed by many other studies, such as [10]. The interrelation between transportation infrastructure and productivity growth is displayed by Caragliu and associates (2009) [11]. The author points to a high correlation between the development of the public transportation network and productivity in cities. He confirms a hypothesis that mass transit contributes to the development of urban areas, among others, by reducing the crowding and providing a better access from the suburbs. For instance, basing on US data, Duranton and Turner (2007) [12] have estimated that a 10% increase in the number of bus connections has resulted in an 0.8% growth in urban population. In turn, Baum-Snow (2007) [13] has indicated that the post-war development of the highway network in the US resulted in the development of suburban areas, reducing the populations of city centers. However, in discussing the roles of cities, transport infrastructure is more and more often superseded by telecommunication or social infrastructure, which determine the speed, in which knowledge or the quality of life spread throughout the city. Above all, many studies point to the fact that the pace of productivity growth in the city is primarily determined by the percentage of (well-) educated people. Yet, this is not a simple effect of higher productivity of people with higher education. According to the development economics theory, it is the positive effect of knowledge spreading. Moretti (2004) [14] has estimated that a 1% increase in the number of people with higher education resulted in an average salary increase of 0.6–1.2% for those with lower education. Shapiro (2005) [15] has displayed that only slightly more than half of the positive effects on the labor market, as resulting from the growing percentage of people with higher education, are the direct effects of higher productivity exercised by these people. The remaining effects are indirect ones, consisting predominantly in growth in demand, and thus growth in supply by such facilities as bars, restaurants, cinema houses, theaters, etc. In the opinion of this author, the quality of life in cities with a high percentage of educated people has been growing, entailing so-called snowball effects—more and more people are coming to the city, which generates high demand, and leads to economic development of the region and of the entire country. Local authorities often face a dilemma: whether to build a road, a library, a park, or a family activity zone? A simple economic theory accentuating the role of infrastructure in the reduction of private-sector costs would probably point to the greater benefits of a road. However, a consideration of the role of cities/regions must include additional factors, aside from the traditional cost-benefit calculation, i.e., the role of an investment in stimulating the development of a smart city. In their article, the authors claim that building roads, organizing and modeling connections and developing smart telecommunication networks dedicated to facilitating mass transit in the region are all important for raising the regional GNP [16,17,18].

1.2. Intelligent Public Transportation Systems

The very term of Intelligent Transportation Systems has been officially defined at the 1994 world congress in Paris [19] and encompasses all systems incorporating a wide variety of technologies (telecommunication, IT, automation, measuring) and management techniques applied in transportation for the purpose of protecting the life and health of traffic participants, increasing the effectiveness of the transportation system, and protecting the natural environment with all its resources. Figure 1 presents the ITS architecture. Table 1 presents a breakdown of Intelligent Transportation Systems according to ISO TC 204.
The benefits of applying Intelligent Transportation Systems [20,21]:
Increase of street throughput by 20–25%.
Increase of road traffic safety (traffic accident reduction by 40–80%).
Improvement of traveling comfort and traffic conditions for drivers, mass transit passengers and pedestrians.
Reduction of rolling stock management costs.
Reduction of paving management and repair costs.
Increase of the economic benefits of the region.
The physical architecture of Intelligent Transportation Systems is illustrated in Figure 2.
One of the most important goals set by countries/regions/cities for themselves when implementing smart solutions in transportation is establishing an ITS architecture, i.e., a series of interconnections (logical, physical and transit) among elements of systems comprising Intelligent Transportation Systems to crease scalable solutions which are also easy to maintain and manage [23]. ITS can also support the development and maintenance of transport infrastructure (including railway infrastructure) as described in more detail later in this article. In Poland, the authorities have not indicated any specific ITS technologies or suppliers, thanks to which they are open systems that increase the competitiveness of implemented solutions. These are however “isolated” by nature, i.e., they separately fulfill their intended roles, but, combined, they may become incompatible, incapable of supporting one another. Data from different ITS areas should however be consolidated. The information generated can serve as support in decisions concerning transportation systems and their performance, as illustrated by numerous studies [24].
To facilitate data uniting and to enable the cooperation of many independent systems, measures should be taken under ISO 24014-1:2015-12, as this very standard defines the basic notions related to the implementation of an interoperative payment system in transport.

1.3. ITS Facilitating the Management and Maintenance of Road Infrastructure towards Increasing the Economic Benefits of the Region

Managing infrastructure maintenance is particularly difficult on so-called rising markets, since countries which only recently joined EU structures have insufficient data which could support national-/regional-/municipal-level authorities in making many important decisions. It is difficult to measure the productiveness of public capital, i.e., the impact of public funds invested (e.g., in transport infrastructure and ITS to support infrastructure management) on the level and pace of GNP growth. The majority of studies conducted on developed markets have indicated a positive and relatively high productivity of capital invested in infrastructure. Researchers further suggest that much depends on the effectiveness of administration. Studies of public capital productivity were pioneered by Aschauera (1989) [25]. In turn, Straub (2007) [26] calculated that approx. 90% of contemporary studies pointed to the positive effect of infrastructural investments on the level and pace of economic development, with 10% having negative effect. What is interesting, these effects are most visible when the impact of specific infrastructure (e.g., transit or telecommunication) is examined. Bom and Ligthart (2008) [27] meta-analyzed 76 studies devoted to the end productivity of public capital. An analysis carried out by the authors has indicated that a flexibility indicator of 0.087 GNP was the best value to summarize all studies, which means that a 1% increase in public capital resources generates a 0.089% GNP increase. To obtain end capital productivity, this value should be divided by the share of public capital resources in the GNP. In the majority of developed countries, the share of public capital in the GNP is approx. 0.4-0.6, which translates into approx. 0.15–0.22% in end productivity of this capital maintaining a flexibility of 0.087. This is a lot, considering that the end productivity of private capital is close to the real interest rate applied in the majority of developed countries, i.e., to 2–4%.
It is even more difficult to calculate the productivity of invested capital on a regional level, primarily due to the aforementioned data insufficiency, hindered access to data, or access to misleading data, e.g., doubled for infrastructural investments implemented by several regions or cities. The precision of calculations can be determined by integrating the monitoring of infrastructural investments, by implementing regional legislative and IT solutions. Monitoring, decisions made, and expenditure must be preceded by a determination of pertinent goals, as illustrated in Figure 3.
Detailed, achievable goals depend on the economic, industrial and social properties of the region, and on the past development of the region. However, their main assumptions are established by the EU and are specified for Poland in the White Book of Infrastructure—16 initiatives for infrastructure. In this document, the planned development of transportation infrastructure considers the needs of particular means of transport (road, railway, inland water, sea, air transport) and changing models of social mobility and economic needs, in a breakdown into areas. A distribution of financing on the development of transport infrastructure must be tied to the mechanism for financing municipalities and poviats [29].
There are many methods of designing transport infrastructure. Methods applying dynamic modeling (as exemplified by the graph theory) have recently become very popular. In the case of dynamic modeling methods, when selecting the sites to locate transportation facilities, all types of fluctuations of source data concerning, among others, mobility patterns, the sizes of transit streams, transit costs and environmental damage are taken into account [30]. Hence the modeling of transportation infrastructure in the region is completed by modeling a network comprising various branches of public transportation (applying network profile measures and the properties of complex networks and IT systems). The development of a modeled transportation system is supported by public campaigns promoting sustainable mobility, i.e., abandoning private cars in favor of mass transit (particularly railway transport) [31]. Management of these campaigns is an element of the overall management of public transportation utilizing ITS.

1.4. Graph Theory in the Analysis of Transport Networks

Network analysis methods have been used for several years now, whether to analyze social systems [32,33,34], or to analyze neuron systems [35,36,37], biological systems [38] and computer systems [39]. This was a result of considerable mathematical development of these methods. The possibilities of analyses carried out with the use of the graph theory are also presented in [40]. According to [40,41,42,43,44,45], the same methods and coefficients which have been successfully applied by sociologists in the analysis of social systems can be now used to analyze transportation networks. [41] contains a comparative analysis of three types of systems which, in terms of parameters, correspond to actual networks. An analysis of their immunity to disturbances was also conducted. In turn, [42] includes an analysis of transportation networks in Poland, assuming three different branches: air transport, railway transport and road transport.
According to information provided in [32,33,34,35,36,37,38,39,40,41,42,43,44,45], networks (social systems, transportation networks, etc.) can be analyzed with the use of three measures, which are sufficient for determining their characteristic features and the “quality” of the entire network. The majority of measures applied and calculations performed produce information which serves as the “leader” from the point of view of the network, serving as a central point for the analyzed network.
Each network can be described as a collection of nodes and their interrelations:
G = V ,   E
where:
  • V—a collection of nodes.
  • E—a collection of their interrelations.
Of course, the following interrelation applies to every network analyzed:
| V | = N ,   | E | = M
As described in detail in [40,42], the following coefficients are most frequently applied in analyses:
Normalized degree (dci) for an i-th node:
d c i = k i N 1
where:
  • ki—degree of this i-th node in the network (the number of node connections with other nodes).
  • N—number of nodes in the network.
The higher the dci coefficient value for this i-th node, the more important role this node serves in the network, or the closer the node is situated to its center.
Eccentricity (eci) of an i-th node in the network:
e c i = max j V d i j
where:
  • dij—number of links among the nodes, wherein a link is understood as the shortest distance between node i and node j.
The lower the eci coefficient value for this i-th node, the more important role this node serves in the network, or the closer the node is situated to its center.
Radius (rci) of this i-th node in the network:
r c i = 1 max j V d i j = 1 e c i
where:
  • dij—number of links among the nodes, wherein a link is understood as the shortest distance between node i and node j.
  • eci—eccentricity of this i-th node in the network.
The higher the rci coefficient value for this i-th node, the more important role this node serves in the network, or the closer the node is situated to its center.
Closeness (cci):
c c i = N 1 j V d i j
where:
  • N—number of nodes in the network.
  • dij—number of links among the nodes, wherein a link is understood as the shortest distance between node i and node j.
Betweenness (bci) for an i-th node in the network:
b c i = l V k l V p l , i , k p l , k
where:
  • pl,i,k—number of connections with the lowest number of nodes between nodes l and k (containing node i).
  • pl,k—number of connections with the lowest number of nodes between nodes l and k (which do not contain node i).
The higher the bci coefficient value for this i-th node, the more important role this node serves in the network, or the closer the node is situated to its center.
Clusterization (gci) of this i-th node in the network:
g c i = 2 E i k i ( k i 1 ) ,   k i > 1
where:
  • Ei—number of links among nodes which are situated closest (neighbors) to the i-th node.
  • ki—degree of this i-th node in the network (number of node connections to other nodes).
The higher the gci coefficient value for this i-th node, the more important role this node serves in the network, or the closer the node is situated to its center.
Formulas (2)–(8) describe the parameters of particular nodes in a network. However, coefficients used to determine the parameters of an entire network are also applied. These are [40,42].
Average shortest paths length (L):
L = 1 N ( N 1 ) i j V d i j
where:
  • N—number of nodes in the network.
  • dij—number of links among the nodes, wherein a link is understood as the shortest distance between node i and node j.
The lower the value of the average shortest path length, the better the analyzed network.
Clusterization coefficient (C):
C = 1 N i V g c i
where:
  • N—number of nodes in the network
  • gci—clusterization coefficient
The higher the value of the clusterization coefficient, the better the analyzed network.
Diameter (D):
D = max i V e c i
where:
  • eci—eccentricity of the i-th node in the network
The smaller the diameter, the better the network.
Radius of a network (R):
R = min i V e c i
where:
  • eci—eccentricity of the i-th node in the network
The smaller the radius of the network, the better the network.
Average nodes degree ( k ¯ ):
k ¯ = 1 N i V k i
where:
  • N—number of nodes in the network
  • ki—degree of this i-th node in the network (number of node connections to other nodes);
The higher the average node degree in the network, the better the network.
Instances when all networks have the same “importance” level practically are not encountered in any of the existing networks. In turn, all networks have key nodes which, more than others, bear the load of correct functioning of the entire network. Determining and locating these nodes can contribute to identifying the current condition of the entire network, providing tips for its improvement.
The studies presented below consisted in the modeling of a railway carrier network in a virtual environment, as well as the calculation of coefficients for individual nodes using the “Gephi” software.

2. Research Methodology—Stages, Goals, Hypotheses, Research Model

At the first stage of the study, the relationship between the number of passenger transport services using means of mass transit and the transportation infrastructures in individual regions, the number and lengths of regional bus and railway connections was identified. The relationship between the GNP of these regions and the mobility of their residents (the number of passenger transport services using means of mass transit, the number of transport services using own means of transport) was also studied. Correlation coefficient is calculated using the following formula:
r = 1 N 1 n ( x x ¯ ) ( y y = ) δ x δ y
where:
  • r—correlation coefficient.
  • N—number of observations.
  • x, y—empirical variable values.
  • δ x ; δ y —standard variable deviations.
Then, the region to be further analyzed at the next stage of the study was selected. In the selected region, the strongest relationship was identified between the number of railway connections and the number of passenger transport services.
At the second stage of the study, the structure of the network of railway connections and simulated optimization was analyzed, where improving the overall network parameters served as the optimization criterion. The purpose of stage two was to display that the optimization of the railway system (improvement of its parameters) would slightly contribute to an increase in the number of passenger transport services.
Optimization results were presented to passengers-respondents participating in stage three of the study. The survey carried out at the third stage of the study indicated that passengers were expecting synchromodal solutions.
The main purpose of the study was to answer several research questions, i.e.,
  • Whether there’s a connection between the elements of the transportation grid from different branches, the number of passenger transport services in the region and the regional GNP?
  • Whether an ITS facilitating the optimization of a transportation grid in one branch of regional transit is capable of contributing to a growth in the number of passenger transport services, or whether synchromodal optimizations are necessary?
  • Which of the ITS supporting passengers would contribute to increasing the number of regional passenger transport services and would thus affect the transit decisions of the local population, i.e., convincing them to switch from their own means of transport to mass transit?
These research questions are interesting and worth answering, as there are few studies that examine the connections among the number of mass transit transport services and the transportation infrastructure and the mobility of passengers and the regional GNP. There is also insufficient data and expert ITS measuring these interconnections. For the purposes of answering the questions formulated above, a statistical analysis was carried out on secondary and resultant data presented in Table 1 and Table 2 (concerning selected regions and the year 2015, 2016 and 2017). A primary study was also conducted to indicate the most important modern ITS solutions (from the “passenger support” and “network and traffic maintenance” groups—see Figure 2), the purpose of which is to convince passengers to switch from their own means of transport to mass transit. A confirmation of three hypotheses was sought:
H1: There are relations among: (1) the number of connections in the region; (2) the lengths of regional transportation lines; (3) the regional GNP; (4) the population of the region; (5) the population of the region’s cities; (6) the number of passenger transport services.
H2: There are no confirmations of the significance of the relationship between the regional GNP and the number of passenger transport services. In the majority of regions examined, the regional GNP has been growing, while the number of passenger transport services has been decreasing (with the calculated correlation coefficient, one that has not indicated any significance). The share of the number of mass transit transport services is higher in regions with a high GNP, but this is mainly determined by the region’s employment rate and urban development.
H3: Optimization of a network with respect to a single branch of mass transit and synchromodal planning in the ITS are the expectations of the residents of the region.
H4: Synchronization of transportation networks from various branches will convince the region’s residents to switch to public communication—thus increasing the share of passenger transport services in total transit.
Figure 4 illustrates the research model developed.
The first stage of the research process consisted in a statistical analysis of the secondary data obtained (applying the correlation coefficient). Then, an analysis of the railway network was carried out, applying the aforementioned graph theory, using the secondary data obtained as well as own data in the simulation. The purpose of quantitative analyses is to identify practical tips for decision-makers who oversee the development and maintenance of infrastructure and who manage public transportation networks (with emphasis on railway transportation). The last stage consisted in a qualitative and direct poll carried out among 500 passengers and concerning the development of ITS. The results of the survey provide an answer the following question: which of the ITS functions change the prevalent traveling patterns. The goals, methods and measures of this study could be used in each EU region. The data used in calculations is sourced from Polish regions. Hypotheses 2 and 3 were confirmed using data from Silesia (a region in southern Poland). On 1 July 2017, a metropolis of 41 municipalities referred to as Górnośląsko-Zagłębiowska Metropolia was established in the Śląskie Voivodeship. The government of the metropolis set the goals, the budget and the financial forecasts for the area. An important goal of the metropolis was to generate economic benefits for the region, among others, by reforming the transportation system (promoting a switch to mass transit) and implementing new investments in the transportation infrastructure.
Table 2 and Table 3 presents source data, Table 4 presents correlation variables for which the coefficients were calculated, Table 5 presents the results of an analysis of correlations among secondary quantitative data, Table 6 and Table 7 also present source data. Table 8, Table 9, Table 10, Table 11, Table 12, Table 13, Table 14 and Table 15 present the results obtained, describing the parameters of the analyzed network of railway connections, and Table 16 presents the results of qualitative polls carried out among passengers and concerning their needs in terms of the most desired ITS from the passenger support group. Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10, Figure 11, Figure 12, Figure 13, Figure 14, Figure 15 and Figure 16 illustrate a modeling of the Silesian Railway (Koleje Śląskie) connection networks from particular analyzed periods, and their proposed modification.

3. The Use of Transportation Infrastructure and ITS in the Context of the Economic Development of Polish Regions (Stage One of the Study)

3.1. Source Data Describing the Transportation Infrastructure and Its Use in Polish Regions

In the past few years, Polish regions have made significant endeavors in investing in their transportation infrastructures and connecting various branches of public transportation by means of ITS implementations. The rate of public investments (i.e., their GNP ratio) has increased from an average level of 3.4% in 1995–2006 to 5.2% in the following years. Counting both the last five years and the last decade, Poland was consistently top-ranked among countries with the highest rate of public investments in Europe, mainly owing to EU financing. However, achieving significant progress in the extension and improvement of infrastructure will require maintaining a successful investment rate at a high level for many years.
Poland has recorded an economic growth of approx. 1.8% GNP in 2016. Mazowsze, Śląsk, Wielkopolska, Dolny Śląsk, Małopolska and Łódzkie are the regions that particularly contributed to this growth, as illustrated in Table 2. These regions are very industrialized, and have the best transportation infrastructures in the countries. They also focus on the development of connections and on the computerization of mass transit (both public and private). They accumulate data related to transportation, preparing for the management of their transportation systems with the use of ITS based on Big Data and 4.0 solutions.
Table 3 presents the regional balance of passenger transport services in the railway system (published data) and using other means of transport (estimated data—calculated on the basis of published data).

3.2. The Use of Transportation Infrastructure and ITS in the Context of the Economic Development of Polish Regions—Results of the First Stage of the Study

Testing hypothesis H1, an analysis of correlations among variables selected from Table 2 and Table 3 was conducted. As mentioned earlier, Table 4 presents correlation variables for which the coefficients were calculated. Table 5 presents the calculated coefficients of variable correlations for the Śląskie Region. For other regions, correlation calculations were completed, but were left out due to the limited volume of the article, focusing further studies on the Śląskie Region.
Completed calculations produced cognitive conclusions for the first stage of the study. There is a straightforward correlation between particular variable couples: 1. number of connections in the region, 2. length of regional connection lines, 3. regional GNP, 4. population of the region, 5. population of the region’s cities, 6. number of passenger transport services. The highest correlation indicator was calculated for:
The number of regional bus connections and the number of passenger transport services using buses traveling among the region’s cities (0.78).
The number of regional railway connections and the number of passenger transport services carried out by the regional railway system (0.81).
The indicators calculated have confirmed hypotheses 1 and 2. The results of the correlation analysis were similar for other Polish regions.
Comparing the correlation results for various regions, a conclusion was drawn that the regional GNP was strongly correlated with the population of the region and, above all, with the populations of the region’s cities, and was higher in the most populated areas and cities in the region. The results have also displayed a strong correlation, i.e., of approx. 0.5, between the number of passenger transport services (carried out by both the railway and the bus system) and the city populations in the Mazowieckie and Małopolska Regions. The number of passenger transport services was the higher the more people lived in the region’s cities. Referring these studies to the road density indicator provided in Table 1, a conclusion is drawn that the lower the road density in the region the higher the number of passenger transport services carried out with the railway and the bus system. Another conclusion from the study has indicated that the higher the density of railway connections the higher the number of passenger transport services carried out with the railway system. A similar relationship was recorded for this transportation branch between the number of connections and the number of passenger transport services.
Statistical analyses produce an important practical conclusion, and namely that the optimization of railway connections is the most important factor in increasing the number of passenger transport services in the region. This conclusion is confirmed by other studies as well [57,58].

4. Analysis of the Structure of Railway Connections—Course of Analyses, Results Obtained and Conclusions Drawn (Stage II of the Study)

4.1. Source Data and Diagrams Illustrating the Railway System in the Śląskie Region

At the second stage of the study, a research hypothesis was formulated, which claimed that an analysis and optimization of the network within a single transportation branch in a selected region (applying the ITS: the graph theory) would stimulate an increase in the number of mass transit transport services. The optimization criterion consisted in so modifying the network of connections that the parameters of the network as a whole are improved. To verify the hypothesis, a set of data concerning the number of passengers in regional transit (considering municipal transportation systems—bus, tram and trolley bus transport) and in railway transit (carried out by Koleje Śląskie) was presented for the Śląskie Region. Data from 2011–2017 was used to increase the accuracy of results.
As illustrated in Table 6, the number of passengers has been gradually decreasing in the Śląskie Region in the recent years. The most noticeable causes of this trend included the absence of measures to coordinate individual carriers in the region and a raise in ticket prices set by the region’s largest carrier, KZK GOP. Despite their large populations, the region and the voivodeship, as well as the Śląsko-Zagłębiowska Metropolis established in its central part, problems related to road bottlenecks or road paralyses are rare. Plus, if we considered the large area of the region and the distances made during the daily commute, we will find that a large number of the region’s residents still prefer individual transportation. This is very strongly affected by the poor synchronization of bus and modal connections, and long travel times, as the author has already indicated in [59,60,61,62]. These may discourage commuters from using means of mass transit.
Decreasing passenger numbers have been recorded by Koleje Śląskie. However, data for 2017 have indicated a slight increase.
Table 7 presents a breakdown of the number of Koleje Śląskie passengers in successive years.
As illustrated above, Koleje Śląskie went through a dynamic increase in the number of passengers in 2011–2013. This is due to the fact that the company was established in 2011 and took over the majority of regional connections from Przewozy Regionalne in 2012. A gradual decrease in the number of passenger transport services has been recorded since 2013. What is important, in 2013–2017, the network of connections for Koleje Śląskie was slightly modified, as illustrated in the network analysis carried out with the use of the graph theory.
A preliminary study of the network of connections for Koleje Śląskie is presented in [63]. The modeled network, as presented in [63], does not account for the distribution of the carrier’s individual lines, and refers to a single year instead of several years, as referred to in this article.
The following figures illustrate the network of connections for Koleje Śląskie from particular periods.
Calculations of parameters according to the network theory were carried out for the connection systems (network of connections), as illustrated in Figure 8, Figure 9, Figure 10, Figure 11 and Figure 12. The parameters obtained are presented in Table 8, Table 9, Table 10, Table 11 and Table 12.
As illustrated in Figure 5, Figure 6, Figure 7, Figure 8 and Figure 9 and presented in Table 8, Table 9, Table 10, Table 11 and Table 12, the network of connections for Koleje Śląskie has been slightly modified throughout the years. According to the information in Table 8, Table 9, Table 10, Table 11 and Table 12, the most important modifications were implemented between 2015 and 2016. In this period, the network was significantly remodeled basing on the importance of its individual nodes. The Katowice node is still the dominant one, however the 2015–2016 network modification gave rise to the Pszczyna node at the expense of the Czechowice-Dziedzice node. What is important, the Pszczyna node is closely tied with the Rybnik node, and is situated closer to the newly established Śląsko-Zagłębiowska Metropolis, compared to the Czechowice-Dziedzice node. This points to a gradual strengthening of this area (the Metropolis) in the carrier’s network of connections. These changes are clearly illustrated in Figure 10, Figure 11, Figure 12, Figure 13 and Figure 14, which present one of the calculated indicators for the network in question.
The indicator illustrated in the figures (the closeness coefficient) displays a clear growth in the importance of nodes situated closer to the center of the region, and this change is also related to the establishment of the Śląsko-Zagłębiowska Metropolis. This can point to the fact that the railway carrier has been focusing on the structure of the network of offered connections, aiming to increase accessibility of the central area of the network. What is important, is that the network of Koleje Śląskie is a Free-scale network, characterized by relatively high immunity to random disturbances, such as rolling stock failures, which contributes to its high reliability, which, in turn, can be an important parameter that will encourage passengers to using this means of transport.

4.2. Diagrams Describing the Railway Transport Network Simulation for the Śląskie Region. Conclusions from the Second Stage of the Study

A simulation of modifications of the network of connections by adding new connections among the main hubs incorporated in the metropolis was carried out as part of the analyses presented herein. The purpose of this optimization was to increase the attractiveness of the network of connections for the commuters by improving its network parameters, and, ultimately, to increase the number of passengers carried in the future. A two-staged modification was simulated:
Stage 1:
direct connections were added between Sosnowiec and Tychy, between Tychy and Rybnik and between Rybnik and Gliwice (Figure 15).
Stage 2:
a connection between Gliwice and Tarnowskie Góry was added to stage 1 of the modification—this connection had been offered by the carrier in the past (Figure 16).
What is noteworthy, the existing railway infrastructure in the region supported all of these modified connections.
The indicators obtained are presented in Table 13 and Table 14.
Then, indicators were calculated for all of the analyzed and proposed networks to determine the condition of the network as a whole. The coefficients obtained are presented in Table 15.
As presented in Table 15, compared to previous periods, coefficients the current network (functioning up to III 2018) were improved (Avg. Clustering Coefficient, Average path length and Average nodes degree). This indicates that the carrier has attempted to optimize the network (improve its parameters as a whole) which translates into an increase in the number of passenger transport services carried out by the railway system (M passengers)—as confirmed by the data in Table 6.
Simulations/successive modifications of the network signal even higher improvements of the network coefficients, which can contribute to an increase in the number of passenger transport services.
The results obtained confirm an assumption that the graph theory can be an effective tool to be used when making investment decisions related to the extension or upgrading of transport networks. Analyses carried out with the use of the graph theory and the results obtained provide information on the parameters of the modeled network and can be used to assess it in terms of a possible increase in the number of passengers. It is therefore recommended that ITS based on algorithms derived from the graph theory be implemented and used in decisions concerning future network modifications.
In this respect, other ITS solutions contributing to a gradual increase in the number of passenger transport services carried out with the railway system in the Śląskie Region are also noteworthy. Apart from the question of taking over connections operated by Przewozy Regionalne, the increase in the number of passengers recorded in 2013–2017 is probably related to improved quality of the rolling stock used. In November 2013, Koleje Śląskie decommissioned their classic railway cars [64] in favor of modern cars equipped with ITS solutions—electric multiple units which provide a traveling comfort and access to important traveling data (information on the current station, the next station, possible delays, switches to other means of transport, etc.). measures to apply ITS solutions were commenced in 2016 in order to promote access to information on the services offered by Koleje Śląskie, and to gradually integrate them with the services provided by other carriers, such as KZK GOP. Some model measures in ITS implementation included:
  • In August 2016, the company entered into cooperation with Google to make its connections visible in the browsing mechanism and in the browsers of all portals integrated with Google [65];
  • The sales of a joint daily ticket with KZK GOP and MZK Tychy was commenced in October 2016, allowing passengers to switch between their means of transport on a single ticket [66];
  • The sales of Koleje Śląskie tickets via the SkyCash app, which is very popular among the youth and people up to the age of 40 (people in the productive age, using innovative mobile solutions and ICT on a regular basis) was commenced in January 2017 [67];
  • The joint ticket offer held with KZK GOP and MZK Tychy was extended to include another carrier, MZKP Tarnowskie Góry in February 2017, thus increasing the number of ticket sales locations [68];
  • In October 2017, the connections offered by the company were displayed on e-podroznik (online browser of connections offered by different carriers and operators) [69].
Considering the statistical data concerning the number of passengers for 2016 and 2017, we are witnessing a slow but gradual increase in their numbers (in 2017, the number of passengers was 400 thousand, which is a 2.58% increase in comparison to the preceding year). This growth probably results from an extension of ITS services and an integration of services offered by Koleje Śląskie with the services of other carriers, as well as the discussed optimization of the carrier’s network of connections.
A conclusion is therefore drawn that the proposed network optimization and the past ITS implementations will stimulate further growth in the number of regional railway passengers.

5. ITS Solutions Promoting Changes in Commute Habits—Poll Results

Stage three of the study consisted in a poll (carried out with the use of a questionnaire prepared according to [70,71]) carried out among the commuting residents of the region. The size of the research sample was 500 people. The purpose of the poll was to answer the question: “Which of the ITS solutions listed will contribute to increasing the number of regional passenger transport services?”. The poll sought to extract solutions which could possibly affect the commute decisions of the local population, i.e., convince them to switch from their own means of transport to mass transit. The poll also asked for a grade of the solution (10 pts.—highest-graded solution, 1 pt.—lowest-graded solution). The results of the third stage are presented in Table 16.
As illustrated in Table 16, easy and comfortable access to travel parameter information both during and before the commute (planning the commute) is the most important factor in changing commute habits. These results are confirmed by other studies as well, for instance by [72].
The respondents pointed to the need of implementing ITS solutions to integrate information from different carriers. In their opinion, an IT platform integrating various transportation branches and different carriers was necessary (available on mobile devices in real time), one they could personalize to receive individualized information. In the opinion of the respondents, the development of such platforms would contribute to changing their commute habits, i.e., would stimulate an increase in the share of passenger transport services in total transit. The results of this poll confirm the fourth hypothesis H4.

6. The Impact of Innovative ITS Solutions on the Number of Passengers—Conclusions

The studies and analyses conducted herein have legitimized the research hypotheses formulated (both the first and the second one), as they indicated that there is a strong relationship between the number of railway connections in the region and the number of passenger transport services. The relationship between the regional GNP and the number of passenger transport services was not confirmed (the calculated correlation coefficient was irrelevant, although it was noticeable that, in the majority of studies regions, the regional GNP was growing while the number of regional passenger transport services was decreasing). According to calculations, the share of passenger transport services carried out by mass transit was higher relative to total transit in regions: with a high GNP, with a large population and a high urban development rate. For further analyses, it is necessary to implement an ITS (service category Traffic management, service name: Infrastructure maintenance management). The effectiveness of ITS in supporting infrastructure maintenance management depends on successively accumulated statistical data.
In verifying the third hypothesis, data from a single region Śląskie was examined. The hypothesis claiming that network optimization in terms of a single transportation branch would cause a slight increase in mass transit transport services was confirmed. For this study, data was obtained from a regional carrier. It is necessary to implement ITS (service category: Railway traffic management, service name: Transport planning support). The effectiveness of ITS in supporting future and successively repeated conditioning optimizations depends on the accumulation of data required by the graph theory. For other branches, a similar study was impossible to conduct, as there was no compatible statistical data available from the road, tram, trolley bus carriers operating in the region. In the Śląskie Region, ITS implementation is necessary (service category Traffic management, service name: Transport planning support). The effectiveness of ITS in supporting transportation planning depends on the quality and amount of successively accumulated statistical data from other transportation branches, provided in standard formats.
Obtaining and analyzing data from primary studies, the fourth hypothesis claiming that the integration of different branches of public transport and implementing synchromodal ITS planning was the ultimate expectation of the region’s residents was explicitly confirmed. Synchronizing the networks for various means of transport will encourage commuters to switch to mass transit, which will ultimately increase the number of passenger transport services in total transit. Synchronizing transportation networks for various means of transport is possible using ITS (service category Traffic management, service name: Traffic control and service category Traffic management, service name: Information provided before and during commute using a means of public transport). However, the effectiveness of ITS is determined by the need to successively accumulate statistical data from all branches of regional transport, provided in standard formats. A conclusion can be drawn basing on the results of the study, that the number of mass transit passenger transport services will increase when information on travel times using different means of transport (in combined passenger transport) is available online. The studies have also indicated that sustainable development of transport (i.e., an increase in the number of passenger transport services) will take place if a single ticket can be used for different carriers, and if railway connections are integrated with bus connections.
In conclusion, what is necessary is a partnership dialogue among all parties interested in investing in regional transport, and for the voice of the commuters to be heard in developing operational ITS. The Management of the Śląsko-Zagłębiowska Metropolis has taken certain steps towards achieving the said dialogue and standardization of public transport, utilizing operative ITS. In the future, the authors of the article plan to study the dependence between the standards adopted in public transport and the development of the region. Studies devoted to modal changes resulting from the implementation of a single ticket for means of transport from different sectors will be a priority.

Author Contributions

Ewa Stawiarska has developed a comprehensive research model, collected and conducted secondary data analysis related to the use of transportation infrastructure and ITS in the context of the economic development of Polish regions. Prepared and conducted primary research for the development of ITS in the selected region (showing passenger requirements), as well as analyzed the data obtained from the conducted research. Paweł Sobczak developed and conducted experiments related to analysis of the structure of railway connections using the graph theory, as well as analyzed the data obtained from the conducted research. He also analyzed the impact of modern ITS solutions on the growth of passenger numbers on the example of the Koleje Śląskie. Authors Ewa Stawiarska and Paweł Sobczak have jointly developed and prepared the introduction and impact of innovative ITS solutions on the number of passengers (conclusions).

Acknowledgments

The translation of the article was financed with BK-235/ROZ0/2018 implemented by the Silesian University of Technology, ul. Akademicka 2A, 44-100 Gliwice.

Conflicts of Interest

The authors declare no conflict of interest.

Terms and Abbreviations

ITInformation Technology
GNPGross National Product
ITSIntelligent Transport Systems
GUSPolish Main Statistical Office
UTKPolish Office of Rail Transport
KZK GOPUpper Silesian Industrial Area Communication Association
MZKPolish Municipal Transport Company
Poviatsecond-level local government unit, a part of the Polish voivodeship

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Figure 1. The counterparts of Intelligent Transportation Systems [20].
Figure 1. The counterparts of Intelligent Transportation Systems [20].
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Figure 2. Physical ITS architecture [22].
Figure 2. Physical ITS architecture [22].
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Figure 3. Evaluation of the regional infrastructure and public transport network [28].
Figure 3. Evaluation of the regional infrastructure and public transport network [28].
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Figure 4. Research model. Source: Own study.
Figure 4. Research model. Source: Own study.
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Figure 5. Network of connections for Koleje Śląskie from IX 2013 to X 2013. Source: Own study.
Figure 5. Network of connections for Koleje Śląskie from IX 2013 to X 2013. Source: Own study.
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Figure 6. Network of connections for Koleje Śląskie from XII 2013 to III 2014. Source: Own study.
Figure 6. Network of connections for Koleje Śląskie from XII 2013 to III 2014. Source: Own study.
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Figure 7. Network of connections for Koleje Śląskie from XII 2014 to VI 2015 Source: Own study.
Figure 7. Network of connections for Koleje Śląskie from XII 2014 to VI 2015 Source: Own study.
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Figure 8. Network of connections for Koleje Śląskie from XII 2015 to XII 2017 Source: Own study.
Figure 8. Network of connections for Koleje Śląskie from XII 2015 to XII 2017 Source: Own study.
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Figure 9. Network of connections for Koleje Śląskie from XII 2017 to III 2018 Source: Own study.
Figure 9. Network of connections for Koleje Śląskie from XII 2017 to III 2018 Source: Own study.
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Figure 10. The closeness coefficient to the network of connections from IX 2013 to X 2013. Source: own study performed using the Gephi software.
Figure 10. The closeness coefficient to the network of connections from IX 2013 to X 2013. Source: own study performed using the Gephi software.
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Figure 11. The closeness coefficient to the network of connections from XII 2013 to III 2014. Source: own study performed using the Gephi software.
Figure 11. The closeness coefficient to the network of connections from XII 2013 to III 2014. Source: own study performed using the Gephi software.
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Figure 12. The closeness coefficient to the network of connections from XII 2014 to VI 2015. Source: own study performed using the Gephi software
Figure 12. The closeness coefficient to the network of connections from XII 2014 to VI 2015. Source: own study performed using the Gephi software
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Figure 13. The closeness coefficient to the network of connections from XII 2015 to XII 2017. Source: own study performed using the Gephi software.
Figure 13. The closeness coefficient to the network of connections from XII 2015 to XII 2017. Source: own study performed using the Gephi software.
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Figure 14. The closeness coefficient to the network of connections from XII 2017 to III 2018. Source: own study performed using the Gephi software.
Figure 14. The closeness coefficient to the network of connections from XII 2017 to III 2018. Source: own study performed using the Gephi software.
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Figure 15. Railway network, taking the stage 1 modification into account (the proposed connections are marked with black lines). Source: own study performed using the Gephi software.
Figure 15. Railway network, taking the stage 1 modification into account (the proposed connections are marked with black lines). Source: own study performed using the Gephi software.
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Figure 16. Railway network, taking the stage 2 modification into account (the proposed connections are marked with black lines). Source: own study performed using the Gephi software.
Figure 16. Railway network, taking the stage 2 modification into account (the proposed connections are marked with black lines). Source: own study performed using the Gephi software.
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Table 1. A breakdown of Intelligent Transportation Systems as per ISO TC 204 [22].
Table 1. A breakdown of Intelligent Transportation Systems as per ISO TC 204 [22].
Service CategoryNo Name of Service
Traveler information1Pre-trip information
2On-trip driver information
3On-trip public transport information
4Personal information services
5Route Guidance and Navigation
Traffic management6Transportation planning support
7Traffic control
8Incident management
9Demand management
10Policing/Enforcing traffic regulations
11Infrastructure maintenance Management
Vehicle12Vision enhancement
13Automated vehicle operation
14Longitudinal collision avoidance
15Lateral collision avoidance
16Safety readiness
17Pre-crash restrain deployment
Commercial Vehicle18Commercial vehicle pre-clearance
19Commercial vehicle administrative process
20Automated roadside safety inspection
21Commercial vehicle on-board safety monitoring
22Commercial vehicle fleet management
Public transport23Public transportation management
24Demand responsive transport management
25Shared transport management
Emergency26Emergency notification and personal security
27Emergency vehicle management
28Hazardous Materials and incident notification
Electronic Payment29Electronic financial transaction
Safety30Public travel safety
31Safety enhancement for vulnerable road users
32Intelligent junctions
Table 2. Economic results for Polish regions, data on transportation infrastructures in regions (data for 2016, for the purposes of consecutive calculations, data was also gathered for 2015 and estimated for 2017). Sources: [46,47,48,49,50,51,52].
Table 2. Economic results for Polish regions, data on transportation infrastructures in regions (data for 2016, for the purposes of consecutive calculations, data was also gathered for 2015 and estimated for 2017). Sources: [46,47,48,49,50,51,52].
Poland/RegionsGNP (B USD)GNP (B PLN)%Road Density/Length of National Roads and Motorways Ratio (km/100 km2)Network Density/Length of Railway Lines Ratio (km/100 km2)Number of Regional Bus Connections in Mass Transit (Both Public and Private)Length of Regional Bus Connections (km)Number of Regional Railway Routes/Connections (Secondary)Length of Regional Railway Connections (km)
Poland5241888100%0.976.22702202,360 19,386.13954
Dolnośląskie44.6160.488.5%1.1–1.58.82545191,483291755.31312
Kujawsko-pomorskie23.183.14.4%1.1–1.56.716310,518231204.07978
Lubelskie20.573.63.9%0.0–0.54.11419272221030.02086
Lubuskie11.541.52.2%1.6–2.06.637225,68415923.20074
Łódzkie32115.16.1%<2.05.939426,568251074.91805
Małopolskie40.9147.27.8%1.1–1.517.226617,150341093.16088
Mazowieckie116.4419.122.2%0.6–1.04.726317,814281671.24809
Opolskie1139.62.1%0.6–1.08.115110,66616762.36147
Podkarpackie20.573.63.9%0.0–0.55.593623811981.5168
Podlaskie11.541.52.2%>0.53.214413,22523645.98464
Pomorskie29.9107.65.7%0.6–1.06.713812,600131226.79278
Śląskie65234.112.4%<2.016424178301973.2944
Świętokrzyskie12.645.32.4%>0.56.234302316726.051
Warmińsko-mazurskie14.250.92.7%0.6–1.04.637631,553161111.97962
Wielkopolskie50.9183.19.7%1.1–1.56.336130,436321879.0695
Zachodnio-pomorskie19.971.73.8%1.1–1.55.2492814131190.40896
Table 3. Regional balance of passenger transport services carried out by the regional railway system (published data) and using other means of transport (estimated data—calculated on the basis of primary studies) [53,54,55,56].
Table 3. Regional balance of passenger transport services carried out by the regional railway system (published data) and using other means of transport (estimated data—calculated on the basis of primary studies) [53,54,55,56].
Poland/RegionsNumber of Passenger Transport Services Carried out by the Regional Railway System (M Passengers)Estimated Number of Passenger Transport Services Carried out in Busses Traveling Among the Region’s Cities (M Passengers)Estimated Number of Car Travels among the Region’s Cities (M Passengers)Population of the Region (M People)Populations of the Region’s Cities (M People)
201520162017201520162017201520162017201520162017201520162017
POLAND300.53291.67257.90512.53495.84438.43NDANDANDA38.46138.41938.41123.20223.12922.716
Dolnośląskie9.047.186.4515.3612.2010.9780.3763.8457.392.9052.8992.8992.0112.0021.956
Kujawsko-PomorskieNDANDANDANDANDANDANDANDANDA2.0892.0862.0861.2501.2441.212
LubuskieNDANDANDANDANDANDANDANDANDA2.1482.1392.1380.9930.9880.962
LubuskieNDANDANDANDANDANDANDANDANDA1.0201.0191.0010.6430.6410.629
Łódzkie3.672.422.066.244.113.5028.3418.6115.852.5022.4912.4121.5821.5701.513
Małopolskie5.684.704.239.667.997.1928.8323.8521.473.3673.3733.3651.6351.6331.620
Mazowieckie61.4359.8253.66104.43101.6991.22288.71281.14252.195.3295.3415.2453.422.93.4303.456
OpolskieNDANDANDANDANDANDANDANDANDA0.9990.9940.9640.5200.5160.498
PodkarpackieNDANDANDANDANDANDANDANDANDA2.1282.1262.1210.8790.8760.862
PodlaskieNDANDANDANDANDANDANDANDANDA1.1911.1871.1780.7200.7180.709
PomorskieNDANDANDANDANDANDANDANDANDA2.3002.3052.3141.4921.4901.476
Śląskie15.2015.3313.5125.8425.8722.97132.24132.40117.554.5844.5694.4573.5423.5243.425
ŚwiętokrzyskieNDANDANDANDANDANDANDANDANDA1.2621.2561.2260.5630.5590.535
Warmińsko-MazurskieNDANDANDANDANDANDANDANDANDA1.4431.4401.4310.8540.8510.834
Wielkopolskie8.178.197.2713.8813.9212.36108.55108.8596.643.4723.4763.4801.9111.9051.873
Zachodnio-pomorskieNDANDANDANDANDANDANDANDANDA1.7161.7121.6921.1781.1731.151
Table 4. Correlation variables.
Table 4. Correlation variables.
SymbolCorrelated Variables
S1Number of regional bus connections
S2Length of regional bus connection lines (km)
S3Number of regional secondary railway routes/connections
S4Length of regional railway connections (km)
S5Number of passenger transport services carried out by the railway system (M passengers)
S6Estimated number of passenger transport services carried out in busses traveling among the region’s cities (M passengers)
S7Estimated number of car travels among the region’s cities (M passengers)
S8Population of the region (M people)
S9Populations of the region’s cities (M people)
S10Regional GNP
Table 5. Variable correlation indicators.
Table 5. Variable correlation indicators.
VariablesS1S2S3S4S5S6S7S8S9S10
S11
S20.661
S30.410.171
S40.380.410.711
S50.520.190.810.681
S60.780.550.360.210.141
S7−0.32−0.21−0.25−0.13−0.12−0.231
S80.450.230.340.380.540.640.761
S90.230.340.270.230.410.480.870.631
S100.210.290.340.310.320.350.540.470.481
Source: Own study on the basis of announced and estimated data from the Main Statistical Office.
Table 6. Number of passengers in the Śląskie Region in 2014–2016 [52,53].
Table 6. Number of passengers in the Śląskie Region in 2014–2016 [52,53].
Year2011201220132014201520162017
Number of passenger transport services in the region, including by municipal transportation (busses, trams, trolley busses)
(M passengers)
NDANDANDA436417414NDA
Passenger transport service by Koleje Śląskie1.7869.13016.32716.03215.90615.33415.730
In total:NDANDANDA452.032432.906429.334NDA
Source: Own study on the basis of data from the Main Statistical Office and the Office of Rail Transport.
Table 7. Number of passengers carried by Koleje Śląskie in 2011–2016 [52].
Table 7. Number of passengers carried by Koleje Śląskie in 2011–2016 [52].
Year2011201220132014201520162017
Number of passengers carried (M people)1.7869.13016.32716.03215.90619715.33405615.730040
Source: Statistical data from the Office of Rail Transport.
Table 8. Indicators for individual network nodes from IX 2013 to X 2013.
Table 8. Indicators for individual network nodes from IX 2013 to X 2013.
City (Node)Normalized DegreeEccentricityRadiusClosness CentralityBetweeness CentralityClusteringEigencentrality
Katowice0.52630.3330.5430.6390.1431.000
Sosnowiec Główny0.15840.2500.3800.0800.0000.459
Częstochowa0.10550.2000.3110.0080.0000.136
Bytom0.21140.2500.4040.1670.3330.547
Tarnowskie Góry0.15850.2000.3220.1090.0000.168
Lubliniec0.15850.2000.3280.0280.0000.153
Gliwice0.15840.2500.4040.0800.3330.368
Krzepice0.05360.1670.2470.0000.0000.041
Tychy Lodowisko0.05340.2500.3580.0000.0000.213
Pszczyna0.26340.2500.4520.1640.3330.631
Czechowice-Dziedzice0.21140.2500.4420.3680.1670.399
Bielsko-Biała0.15850.2000.3330.2050.0000.109
Wadowice0.05360.1670.2530.0000.0000.030
Zwardoń0.05360.1670.2530.0000.0000.030
Wisła Głębce0.05350.2000.3170.0000.0000.135
Cieszyn0.05350.2000.3110.0000.0000.088
Rybnik0.21140.2500.4220.2920.1670.394
Wodzisław Śląski0.05350.2000.3020.0000.0000.087
Racibórz0.10550.2000.3110.1050.0000.096
Chałupki0.05360.1670.2410.0000.0000.025
Source: own calculations performed using the Gephi software.
Table 9. Indicators for individual network nodes from XII 2013 to III 2014.
Table 9. Indicators for individual network nodes from XII 2013 to III 2014.
City (Node)Normalized DegreeEccentricityRadiusClosness CentralityBetweeness CentralityClusteringEigencentrality
Katowice0.52630.3330.5280.6730.1431.000
Sosnowiec Główny0.15840.2500.3650.1050.0000.453
Częstochowa0.05350.2000.2710.0000.0000.097
Bytom0.21140.2500.4040.2810.3330.536
Tarnowskie Góry0.15850.2000.3110.2050.0000.139
Lubliniec0.05360.1670.2410.0000.0000.035
Gliwice0.10540.2500.3800.0001.0000.331
Herby Nowe0.05360.1670.2410.0000.0000.035
Tychy Lodowisko0.05340.2500.3520.0000.0000.215
Pszczyna0.26340.2500.4420.1640.3330.640
Czechowice-Dziedzice0.21140.2500.4320.3680.1670.406
Bielsko-Biała0.15850.2000.3280.2050.0000.112
Wadowice0.05360.1670.2500.0000.0000.031
Zwardoń0.05360.1670.2500.0000.0000.031
Wisła Głębce0.05350.2000.3110.0000.0000.138
Cieszyn0.05350.2000.3060.0000.0000.090
Rybnik0.21140.2500.4130.2920.1670.401
Wodzisław Śląski0.05350.2000.2970.0000.0000.089
Racibórz0.10550.2000.3060.1050.0000.098
Chałupki0.05360.1670.2380.0000.0000.026
Source: own calculations performed using the Gephi software.
Table 10. Indicators for individual network nodes from XII 2014 to VI 2015.
Table 10. Indicators for individual network nodes from XII 2014 to VI 2015.
City (Node)Normalized DegreeEccentricityRadiusClosness CentralityBetweeness CentralityClusteringEigencentrality
Katowice0.58830.3330.5670.6990.0951.000
Sosnowiec Główny0.17640.2500.3860.1180.0000.417
Częstochowa0.05950.2000.2830.0000.0000.084
Bytom0.11840.2500.4050.2210.0000.214
Tarnowskie Góry0.11850.2000.3040.1180.0000.052
Lubliniec0.05960.1670.2360.0000.0000.015
Gliwice0.05940.2500.3700.0000.0000.199
Tychy Lodowisko0.05940.2500.3700.0000.0000.199
Pszczyna0.35340.2500.4720.1760.3330.823
Czechowice-Dziedzice0.35340.2500.4470.3240.1670.670
Bielsko-Biała0.17650.2000.3270.1180.0000.283
Zwardoń0.05960.1670.2500.0000.0000.059
Wisła Głębce0.05950.2000.3270.0000.0000.163
Cieszyn0.05950.2000.3150.0000.0000.134
Rybnik0.29440.2500.4470.3240.1670.617
Wodzisław Śląski0.05950.2000.3150.0000.0000.123
Racibórz0.11850.2000.3270.1180.0000.133
Chałupki0.05960.1670.2500.0000.0000.030
Source: own calculations performed using the Gephi software.
Table 11. Indicators for individual network nodes from XII 2015 to XII 2017.
Table 11. Indicators for individual network nodes from XII 2015 to XII 2017.
City (Node)Normalized DegreeEccentricityRadiusClosness CentralityBetweeness CentralityClusteringEigencentrality
Katowice0.62540.2500.5520.6580.0481.000
Sosnowiec Główny0.18850.2000.3900.1080.0000.450
Częstochowa0.12560.1670.3020.0080.0000.117
Oświęcim0.06350.2000.3640.0000.0000.211
Tarnowskie Góry0.12550.2000.3900.1080.0000.232
Lubliniec0.12560.1670.3020.0080.0000.080
Gliwice0.06350.2000.3640.0000.0000.211
Tychy Lodowisko0.06350.2000.3640.0000.0000.211
Pszczyna0.37530.3330.5160.4920.1670.803
Czechowice-Dziedzice0.31340.2500.4000.3420.0000.464
Bielsko-Biała0.18850.2000.3020.1250.0000.218
Zwardoń0.06360.1670.2350.0000.0000.051
Wisła Głębce0.06340.2500.3480.0000.0000.169
Cieszyn0.06350.2000.2910.0000.0000.102
Rybnik0.31340.2500.4710.2330.3330.672
Racibórz0.12550.2000.3330.0001.0000.183
Bohumin0.12550.2000.3330.0001.0000.183
Source: own calculations performed using the Gephi software.
Table 12. Indicators for individual network nodes from XII 2017 to III 2018.
Table 12. Indicators for individual network nodes from XII 2017 to III 2018.
City (Node)Normalized DegreeEccentricityRadiusClosness CentralityBetweeness CentralityClusteringEigencentrality
Katowice0.52650.2000.4520.5850.0481.000
Sosnowiec Główny0.15860.1670.3330.0940.0000.450
Częstochowa0.10570.1430.2640.0060.0000.117
Oświęcim0.05360.1670.3170.0000.0000.211
Tarnowskie Góry0.10560.1670.3330.0940.0000.232
Lubliniec0.10570.1430.2640.0060.0000.080
Gliwice0.05360.1670.3170.0000.0000.211
Tychy Lodowisko0.05360.1670.3170.0000.0000.211
Pszczyna0.31640.2500.4520.5260.1670.806
Czechowice-Dziedzice0.26340.2500.3800.4330.0000.473
Bielsko-Biała0.15850.2000.3060.2810.0000.230
Zwardoń0.05370.1430.2020.0000.0000.023
Wisła Głębce0.05350.2000.3170.0000.0000.170
Cieszyn0.05350.2000.2790.0000.0000.103
Rybnik0.26350.2000.4130.2840.1670.666
Wodzisław Śląski0.10560.1670.3060.0500.0000.161
Racibórz0.10560.1670.3060.0500.0000.161
Bohumin0.10570.1430.2440.0030.0000.075
Żywiec0.15860.1670.2500.2050.0000.073
Zakopane0.05370.1430.2020.0000.0000.023
Source: own calculations performed using the Gephi software.
Table 13. Network indicators including stage 1 modifications.
Table 13. Network indicators including stage 1 modifications.
City (Node)Normalized DegreeEccentricityRadiusClosness CentralityBetweeness CentralityClusteringEigencentrality
Katowice0.52650.2000.4520.4800.1901.000
Sosnowiec Główny0.21160.1670.3390.0960.3330.498
Częstochowa0.10570.1430.2680.0090.0000.115
Oświęcim0.05360.1670.3170.0000.0000.194
Tarnowskie Góry0.10560.1670.3330.0910.0000.211
Lubliniec0.10570.1430.2640.0060.0000.069
Gliwice0.10560.1670.3390.0001.0000.340
Tychy Lodowisko0.15860.1670.3520.0090.6670.437
Pszczyna0.31640.2500.4520.5260.1670.720
Czechowice-Dziedzice0.26340.2500.3800.4330.0000.381
Bielsko-Biała0.15850.2000.3060.2810.0000.176
Zwardoń0.05370.1430.2020.0000.0000.018
Wisła Głębce0.05350.2000.3170.0000.0000.141
Cieszyn0.05350.2000.2790.0000.0000.079
Rybnik0.36850.2000.4320.3180.2000.749
Wodzisław Śląski0.10560.1670.3170.0500.0000.163
Racibórz0.10560.1670.3170.0500.0000.163
Bohumin0.10570.1430.2500.0030.0000.069
Żywiec0.15860.1670.2500.2050.0000.055
Zakopane0.05370.1430.2020.0000.0000.018
Source: own study performed using the Gephi software.
Table 14. Network indicators including stage 2 modifications.
Table 14. Network indicators including stage 2 modifications.
City (Node)Normalized DegreeEccentricityRadiusClosness CentralityBetweeness CentralityClusteringEigencentrality
Katowice0.52650.2000.45277.0670.2381.000
Sosnowiec Główny0.21160.1670.33916.1670.3330.490
Częstochowa0.10570.1430.2681.5000.0000.114
Oświęcim0.05360.1670.3170.0000.0000.192
Tarnowskie Góry0.15860.1670.33915.8330.3330.287
Lubliniec0.10570.1430.2681.3330.0000.082
Gliwice0.15860.1670.3522.6670.6670.392
Tychy Lodowisko0.15860.1670.3521.6000.6670.429
Pszczyna0.31640.2500.45290.0000.1670.705
Czechowice-Dziedzice0.26340.2500.38074.0000.0000.367
Bielsko-Biała0.15850.2000.30648.0000.0000.168
Zwardoń0.05370.1430.2020.0000.0000.017
Wisła Głębce0.05350.2000.3170.0000.0000.136
Cieszyn0.05350.2000.2790.0000.0000.075
Rybnik0.36850.2000.43254.3330.2000.744
Wodzisław Śląski0.10560.1670.3178.5000.0000.160
Racibórz0.10560.1670.3178.5000.0000.160
Bohumin0.10570.1430.2500.5000.0000.067
Żywiec0.15860.1670.25035.0000.0000.053
Zakopane0.05370.1430.2020.0000.0000.017
Source: own study performed using the Gephi software.
Table 15. Coefficients obtained for the network, in individual variants.
Table 15. Coefficients obtained for the network, in individual variants.
Network Application SystemEffect of Changes
Network CoefficientFrom IX 2013 to X 2013From XII 2013 to III 2014From XII 2014 to VI 2015From XII 2015 to XII 2017From XII 2017 to III 2018From XII 2017 to III 2018 Modification 1From XII 2017 to III 2018 Modification 2
Avg. Clustering Coefficient0.1230.2140.0850.2320.0290.170.174Improvement
Average path length3.0213.1582.9672.8383.3533.33.289Improvement
Diameter6666777Slight deterioration
Radius3333444Slight deterioration
Average nodes degree2.72.52.6672.8232.733.1Improvement
Source: Own study.
Table 16. Grading (an average of the grades given in a scale from 1 to 10), illustrating the impact of the proposed solution on the ITS and on a change in commute patterns towards mass transit).
Table 16. Grading (an average of the grades given in a scale from 1 to 10), illustrating the impact of the proposed solution on the ITS and on a change in commute patterns towards mass transit).
Description of SolutionGrade
Information provided before and during the commute (allowing the passenger to choose the most suitable means of transport, transfer stations and to determine the travel time), prepared for the individual passenger10
Information provided before the commute (calculation of the travel cost using different means of transport)9.5
Information on the travel time using different means of transport (including delays, substitute means of transport, etc.)9.1
Electronic payment for travel in different means of transport8.7
Payment during the travel7.1
Display of the route and information on arriving in the destination6.7
Safety in mass transit (information on the technical condition of vehicles displayed during the travel)6.2
Guaranteed care for weak and/or immobilized passengers4.0
Free use of all means of transport during a smog alert, also for passengers5.8
Free parking lots in the suburbs and free use of municipal transport 5.6
Free WiFi in means of transport4.9
Remote call center for people unskilled in the use of mobile devices or without mobile devices/support to foreigners4
Source: Own study on the basis of primary studies.

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