Article
citation information:
Świerk, P., Macioszek, E., Granà,
A., Sobota, A. Multi-criteria evaluation of everyday travels’ variants in Polish cities
of the GZM metropolis in the era of paid parking. Scientific Journal of Silesian University of Technology. Series
Transport. 2025, 128, 269-281. ISSN: 0209-3324.
DOI: https://doi.org/10.20858/sjsutst.2025.128.15
Paulina ŚWIERK[1], Elżbieta MACIOSZEK[2], Anna GRANÀ[3], Aleksander SOBOTA[4]
MULTI-CRITERIA
EVALUATION OF EVERYDAY TRAVELS’ VARIANTS IN POLISH CITIES OF THE GZM METROPOLIS
IN
THE ERA OF PAID PARKING
Summary. Due to the rapid
development of civilization, more and more people use private cars to quickly
reach their destination, which is mainly work or school. For this reason, the
phenomenon of transport congestion often occurs on the roads. Road congestion
has several negative effects on economic productivity, environmental quality,
and safety, including deterioration of safety conditions, higher fuel
consumption, increased air pollution, and an increase in the cost of goods and
services. In order to minimize the effects of transport congestion in cities, a
number of actions are taken. One of such action, which was taken in the Górnośląska-Zagłębiowska Metropolis, was the increase of
the Paid Parking Zone in Katowice (Poland). The aim of the article is to
determine the most advantageous way of commuting to the workplace in Katowice
from one of the cities of the Górnośląska-Zagłębiowska
Metropolis after the extension of the area of operation of the Paid Parking
Zone in Katowice. The AHP multi-criteria decision support method was used for
this purpose. The best variant of the choice of means of transport for
commuting to the workplace in the functioning of the Paid Parking Zone in
Katowice was assessed in terms of the following factors: travel time, travel cost,
availability, and number of transfers. Five travel variants were analyzed, of which the most advantageous was the variant
using a passenger car and an electric scooter.
Keywords: public transport, e-scooter, mean of transport choice, AHP method,
multicriteria decision making, paid parking zone, transport decisions making
1. INTRODUCTION
Due
to the rapid development of civilization, more and more people use private cars
to quickly reach their destinations, which are mainly work or school [1,
2]. Mobility and transport patterns are intricately linked to significant
social trends, such as the adoption of suburban lifestyles or the aging of
the population [3]. The increasing level of automotive congestion negatively
affects the quality of life of residents.
The
public transport system is a complex operating system that is available to the
general public and transports paying passengers using various means of
transport (metro, buses, trams, urban railway) from the starting point to the
destination on fixed routes and according to a set timetable [4]. Despite the
increasingly frequent phenomenon of transport congestion, public transport is
still not competitive with individual transport. To achieve this, it is
necessary to take decisive actions aimed at reducing car traffic [5]. In many
cases, local authorities, despite limited financial resources, carry out such
actions. These include, for example, construction of new tram lines and
bicycle paths, expansion of the metro, designation of bus lanes, etc., and
counteracting the growing motorization by limiting car traffic in city centers, the designation of paid parking zones, and the
construction of Park&Ride parking lots. These
actions are characterized by varying effectiveness. However, they are
undoubtedly necessary to reduce the unfavorable
external effects of increasing motorization in cities, such as deterioration of
the air quality in the city, an increase in the perceived noise level, parking
chaos, etc. All of these phenomena reduce the attractiveness of a given city as
a potential place for tourist visits or even settlement [6].
So far, the issue of choosing the means of transport
when traveling has been the subject of numerous studies and scientific
articles.
S. Marszałek states that when choosing means of
transport, the following are taken into account: transport capacity, travel
frequency, travel speed, and travel comfort [7].
O. Wyszomirski lists the
following basic selection criteria: time, convenience, availability, frequency,
cost, safety, speed, and certainty [8].
In [9] it was shown that the use of travel time as a
basic assessment indicator results from its leading role in shaping
quality criteria and the presence of an impact on parameters that determine the
level of transport supply on the route. Additionally, the implementation
of priority traffic for public transport vehicles would result in an
improvement in the quality of passenger service.
In the research on the attractiveness of public
transport conducted by the authors from Nigeria, the following criteria
were considered: accessibility, affordability of travel, waiting time, travel
time, seat comfort, transport fares, safety, and drivers' attitude [10].
The study showed low attractiveness of public transport in Nigeria.
In [11], the influence of factors such as travel
time, cost, number of transfers, and waiting time on the choice of
transport mode was investigated. It was shown that transfers and long waiting
times strongly discourage the use of public transport.
The authors of work [12] analyzed
the impact of travel time, number of transfers, and walking time to the
stop on route and mode choice. They found that each additional transfer reduces
the share of public transport by up to 18.75%.
The review [13] considered factors such as cost,
travel time, frequency, availability, and comfort. The conclusions
confirmed that price and time are the most important, but service quality
and infrastructure are also important.
The report [“What is the Value of Saving Travel
Time?” (ITF/OECD, 2019)] analyzes the value of
saving travel time, including waiting time and transfer time. It was identified
that time outside the vehicle – as more burdensome time – is even twice as
important as travel time.
The
aim of the article was to determine the most advantageous way of commuting
to the workplace in Katowice from one of the cities of the Górnośląska-Zagłębiowska Metropolis after the extension of
the area of operation of the Paid Parking Zone in Katowice. The article is
divided into five sections. After the introduction, the second part presents
a review of the literature on the subject in the field of
research on factors influencing the choice of public transport in
everyday journeys. The procedure using the multi-criteria AHP decision support
method was indicated. Then, using this method, the best variant
of the choice of means of transport in commuting to the
workplace was assessed in the functioning of the SPP in Katowice
in terms of the following factors: travel time, travel cost, availability, and
number of transfers. Five travel variants were analyzed.
A summary with conclusions was presented at the end of the article.
2. PAID PARKING ZONE IN KATOWICE (POLAND)
In December 2023, a new parking policy was
introduced in Katowice by document [15]. The aim of the changes was to
limit the inflow of cars from neighboring cities to
the central part of Katowice, increase turnover, and make parking easier for
residents. The division into the Downtown Paid Parking Zone (covering the
strict city center) and the Paid Parking Zone
(covering the outskirts of the center and part of Koszutki, Osiedle Paderewskiego and Zawodzie)
came into force. As a result, the area in which the fee for leaving a vehicle
must be paid was significantly enlarged. Previously, there were 2,239 spaces in
the paid parking zone; now there are a total of 9,057 parking spaces - 2,243 in
the Downtown Paid Parking Zone (ŚSPP) and 6,814 spaces in the Paid Parking Zone
(SPP) [16].
Residents of the zones have the opportunity to
obtain a Resident Parking Card or an Entrepreneur Parking Card. In
addition, Katowice residents who do not live in paid parking zones can purchase
preferential subscriptions [17].
The table below shows the rates applicable in the
Downtown Paid Parking Zone (the DDPZ) and the Paid Parking Zone (the PPZ).
An alternative to commuting by car to the central
zones of Katowice covered by the paid parking system is the possibility of
parking your car at one of the transfer centers:
"Brynów", "Zawodzie"
and "Ligota" (hereinafter referred to as: CP Zawodzie,
CP Brynów and CP Ligota).
These centres allow you to
leave your car in a safe parking lot, free of charge, and travel quickly and
comfortably by public transport to the city centre:
by tram and bus (CP Zawodzie, CP Brynów)
or by train and bus (CP Ligota) [17].
Tab. 1
Rates applicable in
the DPPZ and the PPZ in Katowice [17]
|
|
Rate applicable
in the DPPZ [€] |
Rate applicable
in the PPZ [€] |
|
Parking up to 30 minutes |
0,71 |
0,47 |
|
Parking over 30
minutes to 1 hour |
0,94 |
0,94 |
|
For the second
started hour of parking |
1,69 |
1,13 |
|
For the third
started hour of parking |
1,98 |
1,32 |
|
For each additional
hour of parking started |
1,41 |
0,94 |
3. MULTI-CRITERIA DECISION SUPPORT – AHP METHOD
The
Analytic Hierarchy Process (AHP) method was developed by Thomas L. Saaty from
the University of Pittsburgh in the 1970s [18].
AHP
is a general hierarchical approach to making multi-criteria decisions, which
allows combining quantified and non-quantified criteria and objectively
measurable and subjective criteria [19]. The AHP method consists of decomposing
the problem into simpler components and processing expert assessments based on
pairwise comparisons. Numerous applications of this method in supporting
economic, technical, or social decisions confirm their usefulness, especially
in these applications. Modeling using the hierarchical analysis of the AHP
problem is especially useful when the functional relationship between the
elements of the decision problem, described in the form of a hierarchy of
factors, is not known, but it is possible to estimate the effect of the
occurrence of given properties and their practical effect.
In
general, the AHP method consists of five elementary stages. The first one is to
create a model showing the structure of the decision problem under
consideration. This model takes the form of a hierarchy tree of factors or the
significance of individual criteria. This is done by comparing criteria in
pairs, using a specific rank scale. In the next step of the AHP method, the
weight values for all criteria are estimated. In order to verify the
assessments, the consistency coefficient is calculated, and in the final
step, a sensitivity analysis is performed [20]. The figure below shows the
research procedure used in the AHP method.
The
initial stage of the AHP method is to create a hierarchical structure of the
decision problem. The general objective of the project is placed at the highest
level of the hierarchy. Then it is decomposed into individual evaluation
criteria selected by the decision maker, which constitute the next level of the
hierarchy. This hierarchy can be multi-level because the selected
evaluation criteria can be divided into sub-criteria, which can be subject to
further division. At the lowest level, the considered decision variants are
placed [22].
The
second step of the AHP method is to compare all the selection criteria in
pairs, i.e., each with each. This measurement is subjective, and in order to
standardize it, a comparative scale is used. The higher the number of points,
the more important a given criterion is than the other. However, the principle
of inverse preferences is used here, which means that if the first element is
more important than the second, the second element is proportionally less
important than the first. There are three possible situations:
· the
first and second elements are equally important (rating: 1),
· the
first element is more important than the second (score: 2, 3, 4, ..., 9),
· the
second element is more important than the first (rating: 1/2, 1/3, 1/4, ...,
1/9).
Fig. 1. Stages
of the AHP method. Own work based on [21]
The
ratings are recorded in the form of a proportional square matrix 
, the elements
of which constitute numerically expressed preferences.
The
next step in the AHP method is to normalize the matrix A, i.e., transform it
into a matrix 
(which is shown in formula 1).
|
|
|
(1) |
where:
– number of
criteria,
– matrix A element,
– matrix B element.
Then,
the weights of the evaluated elements are determined (
), which is
the arithmetic mean of the values in the rows of matrix B (as presented
in formula 2).
|
|
|
(2) |
where:
– weight of the element,
– number of elements,
– matrix B element.
In
order to check the reliability of the comparison made in matrix A, its
consistency is verified. Two measures are used to assess consistency. The
first one is the consistency index 
(presented in formula 3), which increases with
the increase in the inconsistency of the estimates.
|
|
|
(3) |
where:
– the consistency
index,
– maximum eigenvalue of a matrix A,
– number of criteria.
The
second measure is the coherence index 
(presented in formula 4), which is the ratio
of to the mean value of the coherence indices of
random pairwise comparisons.
|
|
|
(4) |
where:
– the consistency
index,
– the average value of the consistency indices
of random pairwise comparisons.
The
expert judgments are consistent if the ratio of to is no greater than 0.1. The value varies depending on the dimension of the
matrix . Usually,
tabulated values of are assumed.
The
first step in the consistency study is to calculate the maximum eigenvalue (
of the matrix A (shown in formula
5).
|
|
|
(5) |
where:
– weight of the cryterium,
– number of criteria,
– product of matrix element and weight.
The
next step of the AHP method is to compare pairwise variants according to each
criterion separately and verify the consistency of the results. These
calculations are performed by applying the same formulas that are used to
compare the criteria.
The
final stage of the procedure is to check which variant will be the best, taking
into account the criteria and weights of all elements of the hierarchical
structure. This decision is made based on calculating the sum of the
products of the weights of all criteria and the equivalent weights of the
variants (formula 6).
|
|
|
(6) |
where:
– number of
criteria.
– final weight of the i-th variant,
– weight of j-th criterion,
– weight of the i-th variant with respect to
the j-th criterion.
The
variant with the highest rating should be selected in the decision-making
process.
4.
MULTI-CRITERIA ASSESSMENT OF VARIANTS FOR GETTING THERE FROM SOSNOWIEC TO THE
WORKPLACE LOCATED IN KATOWICE (POLAND)
The
decision-making problem to which the AHP method was applied is the choice
of means of transport for commuting to the workplace located in Katowice
at Barbara Street. The hierarchical structure includes four selection criteria
and five selection options [23, 24]. The selection criteria that were taken
into account for the evaluation of commuting options are presented below:
· total
travel time (K1) – this criterion takes into account the time it takes to get
to the bus stop or to the car, the time spent in a specific means of transport,
the time it takes to make a transfer, and the time it takes to get from the car
park or bus stop to the destination. This value is expressed in
[min]. The criterion is minimized,
· travel
costs (K2) – includes current prices of tickets for public transport, valid
in the area of the Górnośląska-Zagłębiowska
Metropolis, and actual costs of operating a passenger car (based on
average fuel consumption and the current average price of PB95 per liter
as of July 20, 2024). Value expressed in euros [€]. The criterion
is minimized,
· availability
of means of transport (K3) – expressed in the number of journeys per hour (in
the case of public transport), while for individual means of transport the
value of 60 was assumed (due to the lack of restrictions on their potential
use). The criterion is maximized,
· total
number of transfers during the journey to the destination (K4) – this value
is dimensionless, and the criterion itself is minimized.
The
article assessed five different variants of route selection and means of
transport for commuting to work. The starting point of the journey was Lenartowicza Street in Sosnowiec, while the
destination was Barbary Street in Katowice. It was assumed that
the journey would take place outside the morning rush hours.
The
variants of commuting that were taken into account for the assessment are
presented below:
· private
car – travel by car to the workplace, use of the paid parking zone in Katowice
(directly at the workplace) – defined as W1,
· private
car – travel by car to the workplace, use of a parking space located outside
the paid parking zone, walking – defined as W2,
· private
car + tram – access by car to the "Zawodzie"
transfer centre, transfer connection to tram line no.
14, walking from the bus stop to the workplace - defined as W3,
· private
car + electric scooter – access by car to the “Zawodzie”
transfer center, transfer connection to the workplace by electric scooter –
defined as W4,
· tram
+ electric scooter – access by tram line no. 15 to the "Katowice
Rynek" stop, transfer to an electric scooter to the workplace - designated
as W5.
Information
on the above variants was then collected in terms of the previously
characterized criteria, as presented in the table below.
Tab. 2
Matrix of evaluation of travel options from
Sosnowiec (Lenartowicza Street) to Katowice (Barbara
Street)
|
|
K1 |
K2 |
K3 |
K4 |
|
W1 |
24 |
45,00 |
60 |
0 |
|
W2 |
39 |
6,55 |
60 |
0 |
|
W3 |
48 |
7,74 |
4 |
1 |
|
W4 |
45 |
4,74 |
60 |
1 |
|
W5 |
60 |
7,00 |
4 |
1 |
In
the next step, the evaluation criteria were compared in pairs, and the results
were recorded in the form of a proportional square matrix , which was
presented in the form of a table.
Tab.
3
Pairwise comparison of criteria
|
|
K1 |
K2 |
K3 |
K4 |
|
K1 |
1 |
1 |
2 |
3 |
|
K2 |
1 |
1 |
3 |
4 |
|
K3 |
|
|
1 |
5 |
|
K4 |
|
|
|
1 |
Analyzing
the matrix, it can be stated, for example, that according to the subjective
assessment, criterion K1 (travel time) is definitely more important than
criterion K4 (availability of means of transport), but just as important as
travel cost (K2). The matrix is square, and its diagonal contains elements
equal to 1. Additionally, this matrix A is proportional. In the
next step, matrix A was normalized, i.e. it was transformed into matrix . Then, the
weights of the assessed elements were determined by calculating the sums
of the individual rows of the table, and then dividing it by the number of
criteria (i.e. 4). Matrix B is presented in Table 4.
The
calculations show that the highest weight was assigned to criterion K2 (cost of
travel). The lowest weight was assigned to criterion K4 (total number of
transfers).
The
next step was to verify the consistency of the comparison performed in matrix
A. For this purpose, the maximum eigenvalue of matrix ( of matrix A was calculated.
The maximum eigenvalue of matrix A was 4.23.
Tab. 4
Normalized comparison matrix
|
|
K1 |
K2 |
K3 |
K4 |
sum |
weight |
|
K1 |
0,35 |
0,39 |
0,32 |
0,23 |
1,29 |
0,32 |
|
K2 |
0,35 |
0,39 |
0,48 |
0,31 |
1,53 |
0,38 |
|
K3 |
0,18 |
0,13 |
0,16 |
0,38 |
0,85 |
0,21 |
|
K4 |
0,12 |
0,10 |
0,03 |
0,08 |
0,32 |
0,08 |
The
calculation of and gave the following results: 
, 
. has a value lower than 0.1, so it can be
stated that the comparisons of criteria are consistent. Then, the pairwise
comparison of variants should be performed with respect to each criterion
separately. The calculation results are presented in Table 5 (a-h).
Tab. 5
Evaluation of variants and examination of their
consistency
|
a)
variant evaluation matrix– K1 |
b)
normalized comparison matrix – K1 |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
c)
variant evaluation matrix– K2 |
d)
normalized comparison matrix – K2 |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
e)
variant evaluation matrix– K3 |
f)
normalized comparison matrix – K3 |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
g)
variant evaluation matrix– K4 |
h)
normalized comparison matrix – K4 |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
For
example, the table shows that in terms of K1 (travel time), the best ratings
were given to variants W1 and W2. In both cases, these are journeys by
car, so the travel times are the shortest in relation to the other
variants taken into account in the analysis. All the determined
coherence indices are less than 1, which means that the pairwise
comparison matrix is coherent. The last stage of choosing the most
advantageous way of commuting to the workplace is to check which variant
will be the best, taking into account the criteria and weights of all elements
of the hierarchical structure. The decision was made after calculating the
final weights of all variants. This is presented in Table 6.
Tab. 6
Weighting of variants
in relation to criteria
|
|
K1 |
K2 |
K3 |
K4 |
Final weight |
|
W1 |
0,321 |
0,060 |
0,286 |
0,313 |
0,213 |
|
W2 |
0,321 |
0,215 |
0,286 |
0,313 |
0,272 |
|
W3 |
0,112 |
0,094 |
0,071 |
0,176 |
0,102 |
|
W4 |
0,171 |
0,493 |
0,286 |
0,099 |
0,313 |
|
W5 |
0,075 |
0,138 |
0,071 |
0,099 |
0,100 |
Ultimately,
the highest score was given to option 4 (W4), i.e., travelling by car to the “Zawodzie” transfer center and switching to an electric
scooter.
5. CONCLUSIONS
The
aim of the article was to determine the most advantageous way of commuting to a workplace
in Katowice from one of the cities of the Górnośląska-Zagłębiowska
Metropolis after the extension of the area of operation of the Paid Parking
Zone in Katowice. Four criteria were taken into account for the analysis:
travel time, travel cost, availability and number of transfers, and five
variants of commuting.
In
terms of the first criterion (K1), i.e., travel time, the most advantageous
option is to travel by car and use the paid parking lot located directly at the
workplace. However, this option is the least advantageous from an economic
point of view.
Analyzing
the second criterion (K2), i.e., the cost of travel, the most attractive option
is option 4, i.e. travel by passenger car and a transfer to an electric
scooter.
In
the third criterion (K3) – accessibility defined as the number of possible
journeys per hour, the highest score was given to variants W1, W2 and W4, in
which no public transport was used and the user was not limited by the
functioning timetable.
The
best rating in terms of the number of transfers (K4) was given to variants W1
and W2, in which the journey is made by only one means of transport.
Ultimately,
the highest score was given to option 4 (W4), i.e., driving to the Zawodzie transfer center and switching to an electric
scooter. In this case, there is no need to pay for parking the car in the car
park, so the only costs incurred in this option are the operating costs of the
vehicle or the electricity used to charge the electric scooter.
The
publications cited in the literature review indicated that travel time is one
of the most important factors taken into account when using a means of
transport for travel. The variants concerning public collective participation
(W3 and W5) are the weakest in the assessment. There are also conclusions from
publications indicating that the need to use transfers and waiting time reduce
the attractiveness of public transport.
It
should be emphasized that the conducted research did not exhaustively solve the
research problem due to the limited number of criteria taken into account in
the analysis. This may be the basis for conducting further research in this
area.
Acknowledgment
This
research has been partially supported by the European Union - NextGenerationEU - National Sustainable Mobility Center
CN00000023, Italian Ministry of University and Research Decree n. 1033—
17/06/2022, Spoke 9, CUP B73C22000760001.
References
1.
Gorzelanczyk Piotr, Henryk Tylicki, Kalina Tomáš,
Martin Jurkovič. 2021. „Optimizing the Choice of Means of Transport using
Operational Research”. Communications - Scientific letters of the University
of Zilina 23: A193-A207. DOI: 10.26552/com.C.2021.3.A193-A207.
2.
Ľupták,
Vladimír, Paweł Droździel, Ondrej Stopka, Mária Stopková, and Iwona Rybicka.
2019. “Approach Methodology for Comprehensive Assessing the Public Passenger
Transport Timetable Performances at a Regional Scale”. Sustainability
11(13): 3532. DOI: 10.3390/su11133532.
3.
Jacyna
Marianna, Mariusz Wasiak. 2015. “Multicriteria Decision Support in Designing
Transport Systems”. In Mikulski Jerzy (red.): Tools of Transport
Telematics. In: 15th
International Conference on Transport Systems Telematics, TST 2015, Communications in Computer and Information Science,
vol. 531, 2015, Springer, 382 p. ISBN: 978-3-319-24577-5.
4.
Dudek Mariusz, Katarzyna
Solecka, Matthias Richter. 2018. “A multi-criteria appraisal of the selection of
means of urban passenger transport using the Electre and AHP methods”.
Czasopismo Techniczne 6:79-93. DOI: 10.4467/2353737XCT.18.088.8693.
5.
Oskarbski Jacek, Grzegorz Kuprewicz.
2014. „Priorytety dla transportu zbiorowego z wykorzystaniem systemu sterowania
ruchem”. [In Polish:
„Priorities for public transport using a traffic control system”]. Logistyka 3:
4878-4887.
6.
Wójcik Szymon. 2022.
„Czynniki warunkujące wybór samochodu jako środka transportu w codziennych
podróżach – przykłady miast polskich”. [In Polish: „Factors determining
the choice of a car as a means of transport for everyday journeys - examples
from Polish cities”]. Studia Regionalne i Lokalne 3(89): 49-66. DOI:
10.7366/1509499538904.
7.
Marszałek
Stanisław. 2001. Ekonomika, organizacja i zarządzanie w transporcie. [In Polish: Economics, organization and management in transport]. Silesian University of
Management, Katowice.
8.
Wyszomirski
Olgierd. 2008 Transport miejski. Ekonomika i organizacja. [In Polish: Urban transport. Economics and organization]. University of Gdańsk Publishing House, Gdańsk
9.
Vdovychenko Volodymyr, Igor Ivanov, Serhii
Pidlubnyi, Mykyta Vasyliev. 2023.
“Assessment of impact of priority movement for urban public passenger transport
on the quality of passenger service”. Automobile transport 52: 54-63. DOI:
10.30977/AT.2219-8342.2023.52.0.06.
10. Olowosegun Adebola, Dumiso
Moyo, Deepak Gopinath. 2021. “Multicriteria Evaluation of the Quality of
Service of Informal Public Transport: An Empirical Evidence from Ibadan,
Nigeria”. Case
Studies on Transport Policy 9(4). DOI: 10.1016/j.cstp.2021.08.002.
11. Ha Jaehyun, Sugie Lee, Joonho
Ko. “Unraveling the impact of travel time, cost, and transit burdens on commute
mode choice for different income and age groups”. Transportation Research Part A: Policy and Practice 141: 147-166. DOI: 10.1016/j.tra.2020.07.020.
12. Eluru Naveen, Vincent Chakour,
Ahmed M. El-Geneidy. 2012. “Travel mode
choice and transit route choice behavior in Montreal: insights from McGill
University members commute patterns”. Public Transport 4(2): 129-149. DOI: 10.1007/s12469-012-0056-2.
13. Göransson Jessica, Henrik
Andersson. 2023. “Factors that make public transport systems attractive: a
review of travel preferences and travel mode choices”. Eur. Transp. Res. 15(32). DOI:
10.1186/s12544-023-00609-x.
14. OECD/ITF. 2019. What is the Value of Saving Travel Time? Summary and
Conclusions. International Transport
Forum.
15. Resolution No. LVXI/1387/23 of
the Katowice City Council of 27 July 2023 on the establishment of the Downtown
Paid Parking Zone and the Paid Parking Zone for motor vehicles on public roads
in the city of Katowice.
16. wkatowicach.eu. „It's been six months since the introduction of a
new parking policy in Katowice. Remember to renew your cards and subscriptions”. Available at:
https://www.wkatowicach.eu/informacje/w-katowicach/Mija-pol-roku-od-wprowadzenia-nowej-polityki-parkingowej-w-Katowicach.-Trzeba-pamietac-o-odnawianiu-kart-i-abonamentow/idn:6569.
17. Parking
rules in Katowice. Available at:
https://parkowanie.katowice.eu/.
18. Saaty Thomas Lorie. 2001. Decision making for leaders; The Analytic Hierarchy
Process for decisions in a complex world. RWS Publications, Pittsburgh.
19.
Czyżewska
Wanda. 2001. Podejmowanie decyzji gospodarczych przy wielorakości celów. Optymalna polityka
banku centralnego. [In Polish: Making Economic Decisions with Multiple Goals: Optimal
Central Bank Policy]. Department of Statistical and Economic Research of the Central
Statistical Office and the Polish Academy of Sciences, Warsaw, Z. 275.
20. Górski
Krzysztof. 2019. „Zastosowanie metody AHP do wyboru typu gospodarstwa
agroturystycznego”. [In Polish: „Application of the AHP method to select the type of
agritourism farm”]. Przegląd
budowlany 90(9): 108-112.
21. Cabała Paweł. 2018. „Proces analitycznej
hierarchizacji w ocenie wariantów rozwiązań projektowych”. [In Polish: „The process of analytical hierarchy in the evaluation of
design solution variants”]. Przedsiębiorstwo
we Współczesnej Gospodarce – Teoria i Praktyka 24(1): 23-33.
22. Trzaskalik Tadeusz. 2014. „Wielokryterialne
wspomaganie decyzji. Przegląd metod i zastosowań”. [In Polish: „Multi-criteria
decision support. Overview of methods and applications”]. Zeszyty Naukowe. Organizacja i
Zarządzanie / Politechnika Śląska
74: 239-263.
23. Kiciński
Marcin, Wojciech Judt, Robert Kłosowiak.
2017. „Wielokryterialna ocena wariantów dojazdu mieszkańców aglomeracji
poznańskiej do Poznania”. [In Polish: „Multi-criteria evaluation of options for commuting to Poznań by residents of the Poznań
agglomeration”]. Autobusy:
technika, eksploatacja, systemy transportowe 18(12): 560-563.
24.
Kiciński
Marcin, Dariusz Przybylski, Agnieszka Merkisz-Guranowska.
2018. „Ocena wariantów dojazdów do pracy na przykładzie pracowników wojska 31
Bazy Lotnictwa Taktycznego w Poznaniu”. [In Polish: „Assessment of commuting options based on the example of
military employees of the 31st Tactical Air Base in Poznań”].
Autobusy: technika,
eksploatacja, systemy transportowe 19(12): 1085-1088.
Received 05.12.2024; accepted in revised form 24.03.2025
Scientific Journal of Silesian
University of Technology. Series Transport is licensed under a Creative
Commons Attribution 4.0 International License