Application Potential of MCDM/MCDA Methods in Transport—Literature Review and Case Study

Abstract

The paper’s priority aim is to review the scientific literature on multi-criteria analysis in the transport sector. The work is a continuation of research published in the previous works: Broniewicz, Ogrodnik, Multi-criteria analysis of transport infrastructure projects; and Broniewicz, Ogrodnik, A comparative evaluation of multi-criteria analysis methods for sustainable transport. This paper updates the literature review of the subject matter, considering scientific papers published between 2021 and 2024. Based on a literature review, the topic’s popularity under study was assessed, the most popular methods/groups of MCDM/MCDA methods applied to transportation decision-making problems were identified, and new research topics that emerged in recent years were also identified. The article also includes the case study—a multi-criteria analysis of a selected road investment in Poland. The project variant was selected using four different criteria weighting methods, and the obtained results were compared. The comparative analysis performed allowed for the assessment of the application potential of the selected MCDM/MCDA methods. Special attention was paid to the weighting methods. Based on the multi-criteria analysis, a comparable set of weights was obtained using the AHP and Fuzzy AHP methods, while different results were obtained using the CRITIC method characterized by an objective approach to weighting. The TOPSIS method was used for the final ranking of the variants of the selected real road investment. The results confirmed the ranking obtained from the official design documentation of the selected investment.

1. Introduction

The subject of investment projects in transport is very broad. Starting from strategic planning: creating scenarios for the development of transport systems, choosing locations for new roads, investing in infrastructure, and—through operational decisions—choosing specific means of transport on a given route, to increasing transport safety and taking care of its sustainability. Transport is the area in which decision-making regarding planned activities depends on many different, often contradictory, criteria. There are many factors that must be taken into account, such as cost, travel time, safety, environmental impact, user comfort, availability, efficiency, and others. Each of these criteria may have a different weight depending on the specific situation. Often, the criteria for choosing the best option are also contradictory. For example, minimizing costs may lead to longer travel times or lower passenger comfort. Decision-making in the area of transport is also associated with uncertainty and variability of transport conditions.

The application of multi-criteria methods allows for a comprehensive assessment of the available options. They allow for finding compromises that best meet the decision-maker’s goals. In addition, multi-criteria methods help when coping with the uncertainty and the diversity of scenarios, enabling the assessment of different options in diverse conditions.

Many stakeholders often participate in transport decisions: passengers, transport operators, local authorities, and environmental protection organizations. Each of them has divergent priorities, and multi-criteria methods allow for taking into account such diverse perspectives.

Thanks to these features, multi-criteria methods are effective tools for supporting decision-making in the area of transport, making it possible to account for many aspects and find the most optimal solutions.

Multi-criteria decision-making/multi-criteria decision analysis (the terms multiple-criteria decision-making/multiple-criteria decision analysis are also used) is a branch of operations research that includes various techniques and mathematical tools that facilitate the analysis and selection of decision alternatives in light of defined criteria. It can be considered an interdisciplinary field that is based not only on mathematics, but also uses economic theory or computer science [1]. Various ways of classifying multi-criteria methods can be found in the literature. For example, according to Dytczak [2], two fundamental trends can be identified within MCDM: MCDA and MODM. MODM allows for the development of a set of decision alternatives using mathematical programming. MCDA methods, on the other hand, can be divided into three groups, depending on the algorithm. The first group is the so-called aggregation methods, with the AHP method at the forefront. The second group is the so-called outperformance methods; the best-known methods in this group are ELECTRE and PROMETHEE, along with modifications. Due to the continuous development of the methods studied, a third, most diverse group can be distinguished, the so-called residual methods. This group includes, among others, geometric distance methods (mainly TOPSIS and VIKOR), interactive methods (e.g., RUBIS), or methods of verbal decision analysis (e.g., ZAPROS). Kobryn also proposed a similar classification, adding simple ranking methods [3]. Other examples of classifications and examples of implementations can be found in the work of Ogrodnik [4]. It is worth adding that weighting methods are included in some classifications [2,3].

Since the beginning of the 21st century, a rapid development of multi-criteria methods has been observed, and new ones are still appearing. Also in the area of transport, the number of methods used is constantly increasing. The authors of this paper have been observing the development of this issue since 2000, which is reflected in the papers mentioned in various papers [5,6].

Multi-criteria methods have been applied, among other things, to decision-making problems related to safety and transportation quality assessment [7,8]; selection of a scenario for the development of transportation systems [9]; selection of the location of a road investment [10]; or the popular topic of electric vehicles [11]. The topics are diverse, illustrating different sectors of transportation, while the common denominator is the structure of the decision problem: diverse, often conflicting decision criteria and, most often, a finite set of alternatives.

The priority objective of this article is to review scientific works on the application of MCDM/MCDA methods in the field of transport which were published between 2021 and 2024, along with the practical implementation of the most popular methods for selecting the route of a road investment in Poland. The auxiliary objectives are:

• identification of the most popular MCDM/MCDA methods used for selected decision-making problems in the field of transport, along with their classification;
• updating the literature review on the application potential of MCDM/MCDA methods in the field of transport;
• assessment of selected methods that enable the weighing of decision factors and the ranking of variants.

The work consists of four main parts. Part one of the literature review identifies the most popular MCDM/MCDA methods in the field of transport, and indicates the most popular research directions and research locations. Importantly, the obtained results are compared with previous studies [5,6]. The second part presents the multi-criteria analysis algorithm developed for the case study. The case study (the third part) concerned the selection of a route for a road investment in Poland (a large city bypass) using four methods: TOPSIS, AHP, Fuzzy AHP, and CRITIC. The obtained results were compared with the multi-criteria analysis used in the design documentation of the selected investment. The last part of the article contains conclusions from the calculations, recommendations, and directions for future research.

2. Literature Review

The literature review was conducted based on 133 scientific articles indexed in the Web of Science database. The starting point was the analysis of the selected database for the following keywords: “MCDM transport” and “MCDA transport”. Due to the general nature of these keywords, the number of results was significant. On the other hand, the authors wanted to explore a general approach to multi-criteria methods, without indicating their specific names. Therefore, the review was limited to scientific works that had the following clearly defined in the abstract:

• the aim and research problem,
• the methodology and location of the research.

Using this approach, a list of works was prepared and divided into individual years (

Table 1. Review of articles on the application of MCDM/MCDA methods in the field of transport (2021).

#	Author	Research Problem	MCDM Method/Methods	Location
MCDM
1	Çakir E., Tas M.A., Ulukan Z. [12]	Sustainable hybrid electric vehicle selection problem	Neutrosophic Fuzzy MARCOS	Turkey
2	Baric D., Dzambo A. [13]	Evaluation of level crossing design in a congested urban area	AHP	Zagreb (Croatia)
3	Ersoy Y. [14]	Performance evaluation of airports during the COVID-19 pandemic	DEA TOPSIS EDAS	Main international airports (international)
4	Blagojevic A., Kasalica S., Stevic Z., Trickovic G., Pavelkic V. [7]	Assessment of the level of safety at railway crossings	fuzzy FUCOM fuzzy PIPRECIA fuzzy MARCOS	Bosnia and Herzegovina
5	Stankovic J.J., Marjanovic I., Papathanasiou J., Drezgic S. [15]	Social, economic, and environmental sustainability of port regions	Entropy PROMETHEE	37 sea prt regions in seven countries on the European side of the Mediterranean (Europe)
6	Görçün Ö.F. [16]	Evaluation of the selection of proper metro and tram vehicle for urban transportation	CRITIC EDAS	Turkey
7	Pan Y., Zhang L.M., Koh J.L., Deng Y. [17]	Location of the Pedestrian Overhead Bridge (POB) to install lift facilities	CBN TOPSIS	Singapore
8	Matic B., Jovanovic S., Marinkovic M., Sremac S., Das D.K., Stevic Z. [18]	Classification and selection of asphalt production facilities	IRN PIPRECIA IRN EDAS	Autonomous Province of Vojvodina (Serbia)
9	Kumar A. [19]	Identify, classify, and measure the important environmentally responsible transport practices	GC VIKOR	India
10	Gavalas D., Syriopoulos T., Tsatsaronis M. [20]	Assessing key performance indicators in the shipbuilding industry	fuzzy DEMATEL Fuzzy ANP MOORA	Active shipyards of the Bay of Bengal countries (Bay of Bengal)
11	Pamucar D., Deveci M., Canitez F., Paksoy T., Lukovac V. [21]	Prioritizing zero-carbon measures for sustainable transport	BWM TODIM-D	London (Great Britain)
12	Gök-Kisa A.C., Çelik P., Peker I. [22]	Performance evaluation of privatized ports	Entropy ARAS TOPSIS	Turkey
13	Tadić S., Kovac M., Krstić M., Roso V., Brnjac N. [23]	The selection of intermodal transport system scenarios	fuzzy Delphi fuzzy FARE fuzzy MARCOS	South-Eastern Europe region (Europe)
14	Gutiérrez L.R., Oliva M.A.D., Romero-Ania A. [24]	Evaluate public road transportation vehicles	AHP	Madrid (Spain)
15	Rao S.H. [25]	Examine the impacts of intercity railways passenger transportation service	DEMATEL DEMATEL-ANP	Taiwan
16	Pamucar D., Ecer F., Deveci M. [26]	Assessment of alternative fuel vehicles	fuzzy FUCOM neutrosophic fuzzy MARCOS	New Jersey (US)
17	Stoilova S. Munier N. [27]	Railway operators’ policy assessments	SIMUS	Bulgaria
18	Özceylan E., Erbas M., Çetinkaya C., Kabak M. [28]	Analysis of potential high-speed rail routes	Fuzzy AHP ARAS	Turkey
19	Stevic Z., Das D.K., Kopic M. [29]	Road safety assessment	CRITIC DEA MARCOS	Republic of South Africa
20	Huang C.N., Liou J.J.H. Lo H.W., Chang F.J. [30]	Measuring airport resilience	Bayesian BWM modified PROMETHEE	Taiwan
21	Öztürk F. [31]	Passenger Satisfaction Rating	Fuzzy AHP Fuzzy TOPSIS	Istanbul (Turkey)
22	Canbulut G., Köse E., Arik O.A. [32]	The tramway selection problem of a company operating in the public transport sector	AHP GRA MOORA	Turkey
23	Krishankumar R., Pamucar D., Deveci M., Ravichandran K.S. [33]	Prioritization of zero-carbon measures for sustainable urban mobility	EDAS	India
24	Wang X.D., Gou X.J., Xu Z.S. [34]	Performance evaluation of bus companies	CIVDHL-GLDS	Sichuan province (China)
25	Pamucar D., Iordache M., Deveci M., Schitea D., Iordache I. [35]	Prioritizing the alternatives for the development of hydrogen buses	BWM MARCOS	Romania
26	Tumsekcali E., Ayyildiz E., Taskin A. [36]	Public transportation service quality evaluation	IVIF-AHP IVIF-WASPAS	Istanbul (Turkey)
27	Alkharabsheh A., Duleba S. [37]	Public transportation service quality evaluation	Fuzzy AHP	Amman (Jordan)
28	Das D., Kalbar, P.P., Velaga N.R. [38]	Comparative evaluation of car-sharing alternatives for urban and suburban regions	AHP TOPSIS	Mumbai (India)
29	Torbacki W. [39]	Supporting the decision-making process regarding the directions of development of a sustainable transport system in the metropolitan area	DEMATEL PROMETHEE II	Szczecin Metropolitan Area (Poland)
MCDA
30	Cerreta M., Poli G. [40]	Assessing infrastructures alternatives	FAHP	Abruzzo hinterland (Italy)
31	Ammenberg J., Dahlgren S. [41]	Assessments of public bus technologies’ sustainability	MCA	Sweden
32	Pamucar D., Yazdani M., Montero-Simo M.J., Araque-Padilla R.A. Mohammed A. [42]	Airport service quality assessment	SWARA-G MARCOS-G	Spain
33	Morfoulaki M., Papathanasiou J. [43]	Ranking alternative measures of sustainable urban mobility planning	PROMETHEE	Greece
34	Ziemba P. [44]	Selection of electric vehicles	fuzzy TOPSIS fuzzy SAW NEAT F-PROMETHEE II	Poland

,

Table 2. Review of articles on the application of MCDM/MCDA methods in the field of transport (2022).

#	Author	Research Problem	MCDM Method/Methods	Location
MCDM
1	Petrovic N., Mihajlovic J., Jovanovic V., Ciric D., Zivojinovic T. [45]	Evaluation of the operational performance of rail freight and passenger transport systems	Entropia TOPSIS	Serbia
2	Adebiyi S.O., Akinrinmade O.J., Amole B.B. [46]	Bus Rapid Transit Assessment	FAHP VIKOR	Nigeria
3	Ali Y., Sabir M. [47]	Mode-route choice decisions	AHP TOPSIS	Pakistan
4	Stevic Z., Subotic M., Tanackov I., Sremac S., Ristic B., Simic S. [48]	Evaluation of two-lane road sections in terms of traffic risk	CRITIC FUCOM PIPRECIA MARCOS	Bosnia and Herzegovina
5	Feng J.H., Xu S.X., Xu G.Y., Cheng H.B. [49]	Locating parking centers of recyclable waste transportation vehicles	DEMATEL EW WASPAS	China
6	Zapolskyte S., Trépanier M., Burinskiene M., Survile O. [50]	Smart urban mobility system evaluation model	SAW COPRAS TOPSIS AHP	Vilnius (Lithuania) Montreal (Canada) Weimar (Germany)
7	Hajduk S. [51]	Analysis of selected smart cities in terms of urban transport	TOPSIS	Selected cities (international)
8	Garcia-Ayllon S., Hontoria E., Munier N. [52]	Decision-making in assessing alternatives when implementing proposals within Sustainable Urban Mobility Plans	SIMUS WSM	Spain
9	Farooq D., Moslem S. [53]	Estimating driver behavior measures related to traffic safety	PF-AHP PF-DEMATEL PF-ANP	Budapest (Hungary)
10	Cheemakurthy H., Garme K. [54]	Evaluation of ferries	AHP Fuzzy AHP	Stockholm (Sweden)
11	Lu X., Lu J.Q., Yang X.Z., Chen X.M. [55]	Urban Mobility Assessment	IVIF-AHP FCE	Beijing (China)
12	Aydin N., Seker S., Ozkan B. [56]	Planning location of mobility hub for sustainable urban mobility	interval type-2 fuzzy AHP interval type-2 fuzzy WASPAS	Istanbul (Turkey)
13	Lamii N., Bentaleb F., Fri M., Mabrouki C., Semma E. [57]	Identify and analyze risks in seaport dry port system	Delphi AHP	Casablanca (Marocco)
14	Ali Y., Khan A.U., Bin Hameed H. [58]	Choosing a sustainable means of transport	Fuzzy VIKOR	Pakistan
15	Wang C.N., Le T.Q., Chang K.H., Dang T.T. [59]	Measuring road transport sustainability	Entropia CoCoSo	OECD countries
16	Demir G., Damjanovic M., Matovic B., Vujadinovic R. [60]	Sustainable Urban Mobility Plans	Fuzzy-FUCOM Fuzzy-CoCoSo	Podgorica (Montenegro)
17	Chen F., Zhu Y.L., Zu J.C., Lyu J., Yang J.F. [61]	Appraise the road safety attainment	CRITIC ELECTRE FCM	11 countries in Southeast Asia
18	Pamucar, D., Gorcun O.F. [62]	Assessment of European Container Ports	Fuzzy LBWA fuzzy CoCoSo’B	Europe
19	Bahadori M.S., Gonçalves A.B., Moura F. [63]	Ranking potential station locations in the expansion of bike-sharing systems	AHP TOPSIS	Lisbon (Portugal)
20	Hanafiah R.M., Zainon N.S. Karim N.H., Rahman N.S.F.A., Behforouzi M., Soltani H.R. [64]	Controlling maritime transportation accidents	AHP TOPSIS	Straits of Malacca
21	Ozdagoglu A., Oztas G.Z., Keles M.K., Genc V. [65]	A comparative bus selection for intercity transportation	PIPRECIA COPRAS-G	Turkey
22	Patil M., Majumdar B.B. [66]	Key determinants influencing electric two-wheeler usage	AHP RIDIT	India
23	Munim Z.H., Duru O., Ng A.K.Y. [67]	Assessment of the competitiveness of transhipment ports	ANP	Bangladesh
24	Ziemba P. Gago I. [68]	Selection of e-scooters for the vehicle sharing system	PROSA GDSS GAIA	Poland
25	Sharma H.K., Majumder S., Biswas A., Prentkovskis O., Kar S., Skackauskas P. [69]	Evaluate and analyze the factors that modify The Indian Railways Reservation System	DMS	India
26	Zarrinpanjeh N., Javan F.D., Azadi H., Viira A.H., Kurban A., Witlox F. [70]	Analysis of access to different transport options	AHP	Karaj (Iran)
27	Khalife A., Fay T.A., Göhlich D. [71]	Planning strategic rollouts of public charging infrastructure in size and location	AHP	Germany
28	Zhang J.T., Tu Y., Liu J., Liu L.Y., Li Z.M. [72]	Regional traffic safety risk assessments	AHP Sort II CRITIC	China
29	Nguyen T., Tran Q.P., Chileshe N., Huynh T.Y.T., Hallo L. [73]	Identifying potential major risks of railway metro project implementation	ANP	Ho Chi Minh (Vietnam)
30	Turon K. [74]	Car-sharing vehicle fleet selection	ELECTRE III	Poland
31	Shabani A., Shabani A., Ahmadinejad B., Salmasnia A. [75]	Public transport customer satisfaction assessment during the COVID-19 pandemic	BWM Fuzzy TOPSIS	Tehran (Iran)
32	Stoilova S. [76]	Evaluation of fully autonomous subway systems	entropy Shannona BWM TOPSIS EDAS MOORA COPRAS PROMETHEE	Europe
33	Liu Z.Q., Zhang Y.C. [77]	Evaluation of the sustainability of railway projects	DANP VIKOR	China
34	Torkayesh A.E., Yazdani M., Ribeiro-Soriano D. [78]	Analysis of the implementation of Industry 4.0 in the mobility sector	QFD BWM S-CoCoSo	Spain
35	Goyal S., Agarwal S., Singh N.S.S., Mathur T., Mathur N. [79]	Assessment and ranking of the public transport sector	TOPSIS VIKOR ELECTRE DM Fuzzy AHP	India
36	Siksnelyte-Butkiene I., Streimikiene D. [80]	Road transport sustainability assessment	TOPSIS	UE
MCDA
37	Wątróbski J., Baczkiewicz A. [81]	Multi-criteria assessment of sustainable transport	SPOTIS ARAS TOPSIS	European countries (Europe)
38	Gutierrez L.R., Oliva M.A.D., Romero-Ania A. [82]	Management of the urban public transportation system	DEA ELECTRE III	Madrid (Spain)
39	Raad N.G., Rajendran S., Salimi S. [83]	Selecting a dry port location	Fuzzy MULTIMOORA Fuzzy SWARA	Shahid Rajaei (Iran)
40	Lorencic V., Twrdy E., Lep M. [84]	Port efficiency assessment	AHP TOPSIS	Mediterranean cruise ports: Barcelona (Spain), Piraeus (Greece), Civitavecchia (Italy), and Marseille (France)
41	Turon K., Kubik A., Chen F. [85]	Analysis of vehicle selection criteria for car-sharing systems	ELECTRE III	Poland
42	Turon K. [86]	Selection of car models with a classic and alternative drive to the car-sharing services	ELECTRE III	Poland
43	Haase M., Wulf, C., Baumann M., Ersoy H., Koj J.C., Harzendorf F., Estrada L.S.M. [87]	Sustainability assessment for passenger vehicles	TOPSIS	Germany

,

Table 3. Review of articles on the application of MCDM/MCDA methods in the field of transport (2023).

#	Author	Research Problem	MCDM Method/Methods	Location
MCDM
1	Moradi S., Sierpinski G., Masoumi H. [88]	Public transport service quality	Fuzzy AHP Fuzzy Topsis	Katowice (Poland)
2	Sun J.C., Wang H.Y., Cui Z.M. [89]	Maritime supply chain	DEMATEL	Guinea China
3	Sarkar, B., Chakraborty, D., Biswas, A. [90]	Selection of a sustainable transport system	T2PFS	India
4	Cui H.Z., Dong S.W., Hu J.Y., Chen M.Q., Hou B.D., Zhang J.S., Zhang B.T., Xian J.T., Chen F. [91]	Road safety development	CRITIC MABAC	11 countries, Asia
5	Li Y.X., Ding Y.X., Guo Y.L., Cui H.Z., Gao H.Y., Zhou Z.Y., Zhang N.B., Zhu S.Y., Chen F. [92]	Transport safety system analysis	CRITIC MOORA SC	10 countries, Asia
6	Hatefi M.A. [93]	Engine/vehicle selection problem	ROC IROC	Iran
7	Saxena A., Yadav A.K. [94]	Selection of bus technology	Fuzzy AHP Fuzzy TOPSIS	India
8	Moreno-Solaz H., Artacho-Ramírez M.A., Aragonés-Beltrán P., Cloquell-Ballester V.A. [95]	Selection of waste collection trucks	SBWM	Castellon (Spain)
9	Boskovic S., Svadlenka L., Jovcic S., Dobrodolac M., Simic V., Bacanin N. [96]	Electric vehicle selection	AROMAN	Czechia
10	Sivilevicius H., Martisius M. [97]	Asphalt pavement recycling rate	AHP ARTIW-L ARTIW-N DPW	Lithuania
11	Önden I., Pamucar D., Deveci M., As Y., Birol B., Yildiz F.S. [98]	Strategic positioning of railway stations	Fuzzy DOBAS	Turkey
12	Saxena A., Yadav A.K. [99]	Barriers to the adoption of electric freight vehicles in urban areas	FAHP	India
13	Bouraima M.B., Tengecha N.A., Stevic Z. Simic, V., Qiu Y.J. [100]	Identifying the most critical challenges to Bus Rapid Transport implementation	Fuzzy Step-Wise Weight Assessment Ratio Analysis	Dar es Salaam, Tanzania
14	Sadrani M., Najafi A., Mirqasemi R., Antoniou C. [101]	Selecting the best Electric Buses charging strategy	FBWM FRAFSI	Monachium (Germany)
15	Auttha W., Klungboonkrong P. [102]	Environmental effects of transport	FAHP FSM TOPSIS	Khon Kaen City (Thailand)
16	Pamucar D., Durán-Romero G., Yazdani M., López A.M. [103]	Effective solutions in smart mobility systems	LMAW MARCOS	Madrit (Spain)
17	Jou Y.T., Saflor C.S., Marinas K.A., Young M.N. [104]	Service quality of bus transits	AHP SERVQUAL	Philippines
18	Liu A.J., Li Z.X., Shang W.L., Ochieng W. [105]	Evaluate the urban transportation resilience	fuzzy FUCOM CoCoSo	China
19	Kundu P., Görçün O.F., Garg C.P., Küçükönder H., Çanakçioglu M. [106]	Choosing the urban transportation system	fuzzy BWM fuzzy MAIRCIA	general perspective (international)
20	Bouraima M.B., Qiu Y.J., Stevic Z., Simic V. [107]	Assessment of current operational railway systems	IR SWARA CoCoSo	West Africa
21	Li Y.X., Guan S.L., Yin X.Y., Wang X.T., Liu J.L., Wong I.N., Wang G.Z., Chen F. [108]	Road safety situation measurement	CRITIC TODIM NMF	USA
22	Sonar H., Belal H.M., Foropon C., Manatkar R., Sonwaney V. [109]	Criteria relevant to electric vehicle (EVs) adoption	DEMATEL	India
23	Gulcimen S., Aydogan E.K., Uzal N. [110]	Design and planning of urban transportation	HF-AHP MAUT	Turkey
24	Wang C.X., Li L.J. [111]	Determining the sustainable location of dry ports	SWARA WASPAS	China
25	Çaliskan B., Atahan A.O. [112]	Optimum rail route/station location	AHP	Turkey
26	Zhang L.L., Cheng Q., Qu S.Y. [113]	Evaluating railway transportation performance	CRITIC entropy	China
27	Ecer F., Kücükönder H., Kaya S.K., Görçün O.F. [114]	Micro-mobility solutions in cities	LOPCOW CoCoSo IVFNN	Turkey
28	Wang N., Xu Y., Puska A., Stevic Z., Alrasheedi A.F. [115]	Selection of electric vehicle	SWARA MARCOS	Brčko District (Bosnia and Herzegovina)
29	Zhang L.L., Hua X.K. [116]	Evaluating railway transportation efficiency	CERE	China
30	Alshamrani A., Sengupta D., Das A., Bera U.K., Hezam I.M., Nayeem M.K., Aqlan F. [117]	Design of an eco-friendly transportation network	MAUT ELECTRE TOPSIS Exp-TOPSIS	Tripura (India)
MCDA
31	Papaioannou G., Nathanail E., Polydoropoulou A. [118]	Assessment of ferry transport system	AHP PROMETHEE	Greece
32	Oubahman L., Duleba S. [119]	Public transportation service quality	PROMETHEE GAIA	Budapest (Hungary)
33	Hezam I.M., Basua D., Mishra A.R., Rani P., Cavallaro F. [120]	Sustainable urban transportation system	IF-GLDS IF-SPC TOPSIS COPRAS WASPAS CoCoSo	India
34	Wieckowski J., Watróbski J., Kizielewicz B., Salabun W. [121]	Evaluating the group of electric cars	TOPSIS	Poland
35	Kucharski A., Szterlik-Grzybek P. [122]	Locating electric vehicle charging stations	Fuzzy AHP	Lodz (Poland)

and

Table 4. Review of articles on the application of MCDM/MCDA methods in the field of transport (year 2024).

#	Author	Research Problem	MCDM Method/Methods	Location
MCDM
1	Trivedi P., Shah J.T., Esztergar-Kiss D., Duleba S. [123]	Road accident severity analysis	AHP MULTIMOORA	30 cities in India
2	Zhou Z.Y., Zhang Y.H., Zhang Y., Hou B.D., Mei Y.H., Wu P.J., Chen Y.C., Zhou W.J., Wu H.Y., Chen F. [124]	Transport safety engineering	CRITIC GRA GMM	OECD countries
3	Solanki V.S., Agarwal P.K. [125]	Identification of key performance indicators for urban public transit systems	COPRAS TOPSIS AHP FAHP	India
4	Gökgöz F., Yalçin E. [126]	Analyze and compare the performance of European EVs	PSI ADAM DEA	UE
5	Elomiya A., Krupka J., Jovcic S., Simic V., Svadlenka L., Pamucar D. [127]	Evaluating EVCS placement in densely urbanized areas	AHP FAHP SWARA	Praga (Czechia)
6	Wu S.J., Kremantzis M.D., Tanveer U., Ishaq S., O’Dea X., Jin, H. [128]	Assesses the efficiency of 26 international airlines under the impact of the COVID-19 pandemic	DNDEA	international
7	Usun S.O., Bas S.A., Meniz B., Ozkok B.A. [129]	Passenger satisfaction survey in the aviation industry	Type-2 fuzzy TOPSIS	US Airlines passengers (USA)
8	Weng Y.J. Zhang J.Z. Yang, C.L. Ramzan M. [130]	Prioritizing transportation projects	TOPSIS Fuzzy TOPSIS AHP	Chongqing (China)
9	Khichad J.S., Vishwakarma R.J., Gaur A., Sain A. [131]	Optimization of highway performance and safety	FAHP TOPSIS VIKOR	Jaipur (India)
10	Tehrani, M.J., Khavas R.G. [132]	Analyzing the potential of the southern corridor	FDAHP TOPSIS	Iran
11	Yildirim A.K., Kavus B.Y., Karaca T.K., Bozbey I., Taskin A. [133]	A novel seismic vulnerability assessment for the urban roadway	Interval-valued Fermatean fuzzy AHP	Istanbul (Turkey)
12	Hsu C.C., Chang H.C., Li Y.C., Liou J.J.H. [134]	Developing an airport resilience assessment model for climate change	Modified DEMATEL Dombi Weighted Aggregator method modified VIKOR	Taiwan
13	Avila, P., Mota, A., Oliveira E., Castro H., Ferreira L.P., Bastos J., Nuno O.F., Moreira J. [135]	Bus washing process selection	Fuzzy AHP AHP ELECTRE TOPSIS SMART	Portugal
14	Guo Z.Y., Liu J.N., Liu X.C., Meng Z.Y., Pu M.L., Wu H.Y., Yan X., Yang G., Zhang X.J., Chen C.L., Chen F. [136]	Transport safety	LOPCOW MULTIMOORA DBSCAN	European Union
15	Güler A., Polatgil M. [137]	Electric vehicle selection	AHP SWARA Copelanda Bordy	Turkey
16	Kalan O., Isik M., Yüksel F.S. [138]	Selection of international airport transfer center	AHP MOORA ELECTRE	Turkey
17	Rathod R., Joshi G., Shriniwas A. [139]	Analyzes the spatiotemporal variation in transit accessibility	CRITIC	Surat (India)
18	Elomiya A., Krupka J., Simic V., Svadlenka L., Prusa P., Jovcic S. [140]	Strategic placement of hydrogen refueling stations (HRSs)	FAHP TOPSIS	Prague (Czechia)
MCDA
19	Junyent I.A., Casanovas M.M., Roukouni A., Sanz J.M.; Blanch E.R., Roca, Correia G.H.D. [141]	Planning shared mobility hubs in cities	AHP	Barcelona (Spain)
20	Rocchi L., Rizzo A.G., Paolotti L. Boggia A. Attard M. [142]	The climate change vulnerability of coastal roads	VIKOR COPRA PROMETHEE	Malta

).

Based on the literature review, it can be concluded that, in recent years, the most popular MCDM/MCDA methods used in the field of transport are:

• TOPSIS method (used, among others, to assess the sustainable development of road transport, environmental effects of transport, and to prioritize transportation projects),
• AHP method (used, among others, to evaluate public road transportation vehicles and optimum rail route/station locations, and to analyze the severity of road accidents),
• Fuzzy AHP method (used, among others, to assess passenger satisfaction and assess infrastructure alternatives),
• CRITIC method (used, among others, to assess road safety, evaluate railway transportation performance, and analyze the spatiotemporal variation in transit accessibility).

Details are presented in Figure 1, which includes MCDM/MCDA methods that appeared at least twice in the prepared literature review.

Figure 2 shows the location of the research (case study). Again, the chart includes countries/organizations/continents that appeared at least twice in the prepared literature review. The majority of works included in the review concerned various transport issues from the Middle and Far East (Turkey, India, and China). Among European countries, Spain and Poland dominated.

All issues appearing in the articles were grouped into the following research areas:

• Safety and quality of public transport
• Scenarios for the development of public transport systems
• Urban area
• Transport sustainability and resilience
• Road, air, rail, and sea transport
• Electric vehicles
• Other.

In comparison to the latest research conducted by the authors, it can be noticed that two new research areas have appeared: “Urban area” and “Transport sustainability and resilience.” The emergence of new research problems related to urban areas is a result of today’s sometimes rapid and uncontrolled urbanization process. Multi-criteria decision support in the context of urban areas has been addressed in earlier work such as Ogrodnik [143]. At the same time, the area of “Choice of investment location” did not appear in the last period of analysis. The percentage of research conducted in individual areas is presented in Figure 3.

For transportation safety and quality issues, the most frequently used methods were: CRITIC, AHP, FAHP, and TOPSIS, confirming the general trend seen in Figure 1. In the “Scenarios for the development of public transport systems” research area, AHP and TOPSIS methods were used most often. A similar trend was observed in “Urban areas” and “Road, air, rail, and sea transport”. For “Transport sustainability and resilience,” TOPSIS was the most frequently used multi-criteria method. In contrast, the work on electric vehicles varied in terms of the methods used, with AHP, FAHP, and SWARA, among others, being used.

3. Research Methodology

Taking into account the results of the literature review, three methods for weighting decision factors were selected for further research: AHP, Fuzzy AHP, and CRITIC, as well as the TOPSIS method for ranking decision variants. The above methods were used to select the route of a specific road investment planned in Poland. Additionally, the obtained results were compared with the multi-criteria analysis performed as part of the design documentation of the investment project (Figure 4).

4. Case Study

Selected MCDM/MCDA methods were applied to an investment project in the Opole Province, in southern Poland. The construction will be carried out as part of the government’s 100 Bypasses Construction Program for 2020–2030 [144].

4.1. Decomposition of the Decision Problem

The primary objective of the analysis is to select the route of a bypass of a town located in the Opole province. Four variants of the route were analyzed (Figure 5). Four groups of criteria were taken into account, concerning technical, economic, environmental, and social conditions. Figure 6 presents a tree of their hierarchical structure. Within each group, subcriteria were defined (12 in the technical group, 5 in the social group, 11 in the environmental group, and 9 in the economic group). A total of 37 subcriteria were defined, the list of which is provided in

Table 5. List of criteria used for analysis.

Abbr.	Criterion Name
TECHNICAL CRITERIA
CT1	Length of the main route
CT2	SDRR traffic intensity on the investment in 2030
CT3	SDRR traffic volume remaining on existing road in 2030
CT4	Number of heavy vehicles on investment in 2030
CT5	The need to reconstruct the 220 kV line
CT6	Integrated points in the road safety assessment
CT7	Road managers’ preferences
CT8	Geology (geotechnics)
CT9	Surface area of engineering structures
CT10	Route extension indicator
CT11	Share of overtaking sections in the entire route
CT12	Passage through flood-prone areas
SOCIAL CRITERIA
CS1	Collision with planned gas station
CS2	Residential buildings to be demolished
CS3	Number of conclusions from information meetings conducted against a given location variant
CS4	Number of conclusions from information meetings conducted for a given location variant
CS5	Compliance of the route with the Local Spatial Development Plan
ENVIRONMENTAL CRITERIA
CEN1	Collision with protected areas under Article 6, Section 1 of the Act of 16 April 2004 on Nature Conservation
CEN2	Collision with ecological corridors of national importance
CEN3	Land occupancy
CEN4	Agricultural land constituting agricultural land of classes I–III
CEN5	Collision with mining areas, mining areas and natural resource deposits
CEN6	Collision with protected habitats from Annex I of the Habitats Directive
CEN7	Collision with amphibian habitats
CEN8	Collision with bird species found within the boundaries of the investment area
CEN9	Collision with surface streams
CEN10	Collision with water reservoirs
CEN11	Collision with historic buildings
ECONOMIC CRITERIA
CE1	Cost of preparation and works
CE2	Real estate acquisition cost (SKNN)
CE3	Total investment cost
CE4	Average total cost of 1 km of main route
CE5	ERR internal rate of return
CE6	ENPV net present value of the investment
CE7	BCR Benefit-Cost Ratio
CE8	Undiscounted time cost reduction
CE9	Undiscounted accident cost reduction

Source: [144].

. The lowest level includes location variants. For all variants, the commencement of construction is common and will be located before the town of Sidzina at approximately km 65 + 800 of national road no. 46, and their end at km 73 + 900 of this road [144].

4.2. Estimation of Subcriteria Weights Using Selected MCDM/MCDA Methods

The first stage of the multi-criteria analysis concerned the weighing of subcriteria.

Table 6. Technical criteria comparison matrix (classic AHP scale).

	CT1	CT2	CT3	CT4	CT5	CT6	CT7	CT8	CT9	CT10	CT11	CT12
CT1	1.000	1.000	5.000	3.000	7.000	5.000	5.000	3.000	3.000	9.000	7.000	5.000
CT2	1.000	1.000	5.000	3.000	7.000	5.000	5.000	3.000	3.000	9.000	7.000	5.000
CT3	0.200	0.200	1.000	0.333	3.000	1.000	1.000	0.333	0.333	5.000	3.000	1.000
CT4	0.333	0.333	3.000	1.000	5.000	3.000	3.000	1.000	1.000	7.000	5.000	3.000
CT5	0.143	0.143	0.333	0.200	1.000	0.333	0.333	0.200	0.200	3.000	1.000	0.333
CT6	0.200	0.200	1.000	0.333	3.000	1.000	1.000	0.333	0.333	5.000	3.000	1.000
CT7	0.200	0.200	1.000	0.333	3.000	1.000	1.000	0.333	0.333	5.000	3.000	1.000
CT8	0.333	0.333	3.000	1.000	5.000	3.000	3.000	1.000	1.000	7.000	5.000	3.000
CT9	0.333	0.333	3.000	1.000	5.000	3.000	3.000	1.000	1.000	7.000	5.000	3.000
CT10	0.111	0.111	0.200	0.143	0.333	0.200	0.200	0.143	0.143	1.000	0.333	0.200
CT11	0.143	0.143	0.333	0.200	1.000	0.333	0.333	0.200	0.200	3.000	1.000	0.333
CT12	0.200	0.200	1.000	0.333	3.000	1.000	1.000	0.333	0.333	5.000	3.000	1.000

,

Table 7. Social Criteria Comparison Matrix (Classic AHP Scale).

	CS1	CS2	CS3	CS4	CS5
CS1	1.000	0.333	0.200	0.200	0.200
CS2	3.000	1.000	0.333	0.333	0.333
CS3	5.000	3.000	1.000	1.000	1.000
CS4	5.000	3.000	1.000	1.000	1.000
CS5	5.000	3.000	1.000	1.000	1.000

,

Table 8. Environmental criteria comparison matrix (classic AHP scale).

	CEN1	CEN2	CEN3	CEN4	CEN5	CEN6	CEN7	CEN8	CEN9	CEN10	CEN11
CEN1	1.000	0.143	0.200	0.143	0.143	0.143	0.200	0.143	0.200	0.200	0.200
CEN2	7.000	1.000	3.000	1.000	1.000	1.000	3.000	1.000	3.000	3.000	3.000
CEN3	5.000	0.333	1.000	0.333	0.333	0.333	1.000	0.333	1.000	1.000	1.000
CEN4	7.000	1.000	3.000	1.000	1.000	1.000	3.000	1.000	3.000	3.000	3.000
CEN5	7.000	1.000	3.000	1.000	1.000	1.000	3.000	1.000	3.000	3.000	3.000
CEN6	7.000	1.000	3.000	1.000	1.000	1.000	3.000	1.000	3.000	3.000	3.000
CEN7	5.000	0.333	1.000	0.333	0.333	0.333	1.000	0.333	1.000	1.000	1.000
CEN8	7.000	1.000	3.000	1.000	1.000	1.000	3.000	1.000	3.000	3.000	3.000
CEN9	5.000	0.333	1.000	0.333	0.333	0.333	1.000	0.333	1.000	1.000	1.000
CEN10	5.000	0.333	1.000	0.333	0.333	0.333	1.000	0.333	1.000	1.000	1.000
CEN11	5.000	0.333	1.000	0.333	0.333	0.333	1.000	0.333	1.000	1.000	1.000

and

Table 9. Economic criteria comparison matrix (classic AHP scale).

	CE1	CE2	CE3	CE4	CE5	CE6	CE7	CE8	CE9
CE1	1.000	1.000	1.000	7.000	3.000	3.000	5.000	5.000	5.000
CE2	1.000	1.000	1.000	7.000	3.000	3.000	5.000	5.000	5.000
CE3	1.000	1.000	1.000	7.000	3.000	3.000	5.000	5.000	5.000
CE4	0.143	0.143	0.143	1.000	0.200	0.200	0.333	0.333	0.333
CE5	0.333	0.333	0.333	5.000	1.000	1.000	3.000	3.000	3.000
CE6	0.333	0.333	0.333	5.000	1.000	1.000	3.000	3.000	3.000
CE7	0.200	0.200	0.200	3.000	0.333	0.333	1.000	1.000	1.000
CE8	0.200	0.200	0.200	3.000	0.333	0.333	1.000	1.000	1.000
CE9	0.200	0.200	0.200	3.000	0.333	0.333	1.000	1.000	1.000

present the matrices of comparisons of subcriteria in each main group developed with the classical AHP method using the classical 9-point Saaty scale.

The starting point was a set of weights from the project documentation (which was the result of expert evaluation). The documentation used a point scale, with a range of 1–5, Then, within the framework of the article, the points were converted into ratings within a comparison matrix (necessary in the AHP and FAHP methods), maintaining the hierarchy of importance of the criteria from the documentation. The following shows how the weights from the project documentation were converted to the comparison matrix in the AHP method, which in turn was modified using fuzzy numbers to the FAHP matrix.

-

If the criteria had the same weight in the project documentation, then at the stage of pairwise comparisons in the AHP method they received 1.

-

If the difference was 1 point—then a score of 3 from the Saaty scale was awarded.

-

If the difference was 2 points—then a score of 5 from the Saaty scale was awarded.

-

If the difference was 3 pts—then a rating of 7 from the Saaty scale was awarded.

-

If the difference was 4 points—then a score of 9 from the Saaty scale was awarded.

The Fuzzy AHP method, which has enjoyed considerable popularity in recent years, was also used to estimate the weights in this paper. Based on the comparison matrices from the classic AHP using a triangular scale (

Table 10. Classic Saaty scale and selected fuzzy triangular scales.

Definition	Classic Saaty Scale	Fuzzy Triangular Scale
Equal importance	1	1,1,1
Weak or slight	2	1,2,3
Moderate importance	3	2,3,4
Moderate plus	4	3,4,5
Strong importance	5	4,5,6
Strong plus	6	5,6,7
Very strong	7	6,7,8
Very, very strong	8	7,8,9
Extremely strong	9	9,9,9
“If activity i has one of the above non-zero numbers assigned to it when compared with activity j, then j has the reciprocal value when compared with i”	Reciprocals of above	Reciprocals of above

Source: [145,149].

), the weights of the detailed criteria were estimated.

In the literature review, the CRITIC method (which belongs to objective weighing methods), was included among the leaders of MCDM/MCDA methodologies. “The method developed is based on the analytical investigation of the evaluation matrix for extracting all information contained in the evaluation criteria” [146]. The method is characterized by a completely different calculation algorithm compared to the methods from the AHP group, and—importantly—is based solely on the evaluation of variants in the light of criteria and does not take into account preferences.

Table 11. The correlation matrix—technical criteria.

	CT1	CT2	CT3	CT4	CT5	CT6	CT7	CT8	CT9	CT10	CT11	CT12
CT1	1.000	−0.448	0.067	−0.825	0.972	0.989	−0.776	−0.936	0.049	1.000	0.717	−0.987
CT2	−0.448	1.000	0.855	0.863	−0.537	−0.460	0.260	0.125	0.862	−0.445	−0.892	0.462
CT3	0.067	0.855	1.000	0.476	−0.022	0.066	−0.227	−0.373	1.000	0.071	−0.549	−0.064
CT4	−0.825	0.863	0.476	1.000	−0.890	−0.844	0.662	0.575	0.489	−0.823	−0.978	0.846
CT5	0.972	−0.537	−0.022	−0.890	1.000	0.994	−0.870	−0.847	−0.036	0.972	0.827	−0.995
CT6	0.989	−0.460	0.066	−0.844	0.994	1.000	−0.857	−0.901	0.050	0.989	0.762	−1.000
CT7	−0.776	0.260	−0.227	0.662	−0.870	−0.857	1.000	0.681	−0.221	−0.776	−0.667	0.863
CT8	−0.936	0.125	−0.373	0.575	−0.847	−0.901	0.681	1.000	−0.355	−0.937	−0.426	0.896
CT9	0.049	0.862	1.000	0.489	−0.036	0.050	−0.221	−0.355	1.000	0.053	−0.558	−0.049
CT10	1.000	−0.445	0.071	−0.823	0.972	0.989	−0.776	−0.937	0.053	1.000	0.715	−0.987
CT11	0.717	−0.892	−0.549	−0.978	0.827	0.762	−0.667	−0.426	−0.558	0.715	1.000	−0.767
CT12	−0.987	0.462	−0.064	0.846	−0.995	−1.000	0.863	0.896	−0.049	−0.987	−0.767	1.000

,

Table 12. The correlation matrix—social criteria.

	CS1	CS2	CS3	CS4	CS5
CS1	1.000	−0.333	0.333	−0.132	0.333
CS2	−0.333	1.000	0.333	−0.662	−1.000
CS3	0.333	0.333	1.000	0.132	−0.333
CS4	−0.132	−0.662	0.132	1.000	0.662
CS5	0.333	−1.000	−0.333	0.662	1.000

,

Table 13. The correlation matrix—environmental criteria.

	CEN1	CEN2	CEN3	CEN4	CEN5	CEN6	CEN7	CEN8	CEN9	CEN10	CEN11
CEN1	1.000	0.802	−0.961	0.028	−0.941	0.834	−0.855	−0.999	−0.999	−0.854	0.301
CEN2	0.802	1.000	−0.840	−0.329	−0.939	0.920	−0.629	−0.796	−0.796	−0.462	0.358
CEN3	−0.961	−0.840	1.000	0.245	0.975	−0.760	0.681	0.968	0.968	0.869	−0.548
CEN4	0.028	−0.329	0.245	1.000	0.266	0.063	−0.492	−0.004	−0.004	0.048	−0.860
CEN5	−0.941	−0.939	0.975	0.266	1.000	−0.866	0.705	0.943	0.943	0.739	−0.471
CEN6	0.834	0.920	−0.760	0.063	−0.866	1.000	−0.855	−0.816	−0.816	−0.426	0.000
CEN7	−0.855	−0.629	0.681	−0.492	0.705	−0.855	1.000	0.836	0.836	0.581	0.238
CEN8	−0.999	−0.796	0.968	−0.004	0.943	−0.816	0.836	1.000	1.000	0.870	−0.333
CEN9	−0.999	−0.796	0.968	−0.004	0.943	−0.816	0.836	1.000	1.000	0.870	−0.333
CEN10	−0.854	−0.462	0.869	0.048	0.739	−0.426	0.581	0.870	0.870	1.000	−0.522
CEN11	0.301	0.358	−0.548	−0.860	−0.471	0.000	0.238	−0.333	−0.333	−0.522	1.000

and

Table 14. The correlation matrix—economic criteria.

	CE1	CE2	CE3	CE4	CE5	CE6	CE7	CE8	CE9
CE1	1.000	0.520	0.896	0.860	0.495	−0.103	0.506	−0.898	−0.944
CE2	0.520	1.000	0.845	0.630	0.946	0.657	0.952	−0.821	−0.285
CE3	0.896	0.845	1.000	0.865	0.801	0.277	0.811	−0.988	−0.739
CE4	0.860	0.630	0.865	1.000	0.447	−0.163	0.465	−0.930	−0.865
CE5	0.495	0.946	0.801	0.447	1.000	0.797	1.000	−0.730	−0.200
CE6	−0.103	0.657	0.277	−0.163	0.797	1.000	0.786	−0.171	0.421
CE7	0.506	0.952	0.811	0.465	1.000	0.786	1.000	−0.743	−0.214
CE8	−0.898	−0.821	−0.988	−0.930	−0.730	−0.171	−0.743	1.000	0.781
CE9	−0.944	−0.285	−0.739	−0.865	−0.200	0.421	−0.214	0.781	1.000

present the correlation matrices developed for the detailed criteria of each main group, and their weights are collectively presented in

Table 15. Summary of criteria weights.

Abbr.	Name of the Sub-Criterion	Weight (Method from Design Documentation)	Weight (AHP)	Weight (FAHP)	Weight (CRITIC)
	TECHNICAL CRITERIA
CT1	Length of the main route	12.822	0.22	0.215	0.079
CT2	SDRR traffic intensity on the investment in 2030	12.822	0.22	0.215	0.088
CT3	SDRR traffic volume remaining on existing road in 2030	7.692	0.046	0.047	0.070
CT4	Number of heavy vehicles on investment in 2030	10.256	0.107	0.108	0.083
CT5	The need to reconstruct the 220 kV line	5.128	0.022	0.022	0.087
CT6	Integrated points in the road safety assessment	7.692	0.046	0.047	0.081
CT7	Road managers’ preferences	7.692	0.046	0.047	0.094
CT8	Geology (geotechnics)	10.256	0.107	0.108	0.091
CT9	Surface area of engineering structures	10.256	0.107	0.108	0.070
CT10	Route extension indicator	2.564	0.013	0.012	0.079
CT11	Share of overtaking sections in the entire route	5.128	0.022	0.022	0.086
CT12	Passage through flood-prone areas	7.692	0.046	0.047	0.092
SOCIAL CRITERIA
CS1	Collision with planned gas station	11.765	0.05	0.052	0.184
CS2	Residential buildings to be demolished	17.648	0.107	0.111	0.274
CS3	Number of conclusions from information meetings conducted against a given location variant	23.529	0.281	0.279	0.171
CS4	Number of conclusions from information meetings conducted for a given location variant	23.529	0.281	0.279	0.162
CS5	Compliance of the route with the Local Spatial Development Plan	23.529	0.281	0.279	0.210
ENVIRONMENTAL CRITERIA
CEN1	Collision with protected areas under Article 6, Section 1 of the Act of 16 April 2004 on Nature Conservation	2.780	0.015	0.015	0.127
CEN2	Collision with ecological corridors of national importance	11.111	0.145	0.143	0.107
CEN3	Land occupancy	8.333	0.052	0.053	0.070
CEN4	Agricultural land constituting agricultural land of classes I–III	11.111	0.145	0.143	0.086
CEN5	Collision with mining areas, mining areas and natural resource deposits	11.111	0.145	0.143	0.078
CEN6	Collision with protected habitats from Annex I of the Habitats Directive	11.111	0.145	0.143	0.099
CEN7	Collision with amphibian habitats	8.333	0.052	0.053	0.081
CEN8	Collision with bird species found within the boundaries of the investment area	11.111	0.145	0.143	0.079
CEN9	Collision with surface streams	8.333	0.052	0.055	0.079
CEN10	Collision with water reservoirs	8.333	0.052	0.053	0.076
CEN11	Collision with historic buildings	8.333	0.052	0.055	0.116
ECONOMIC CRITERIA
CE1	Cost of preparation and works	14.706	0.219	0.217	0.106
CE2	Real estate acquisition cost (SKNN)	14.706	0.219	0.217	0.081
CE3	Total investment cost	14.706	0.219	0.217	0.093
CE4	Average total cost of 1 km of main route	5.880	0.021	0.021	0.111
CE5	ERR internal rate of return	11.765	0.097	0.099	0.071
CE6	ENPV net present value of the investment	11.765	0.097	0.099	0.091
CE7	BCR Benefit-Cost Ratio	8.824	0.042	0.043	0.071
CE8	Undiscounted time cost reduction	8.824	0.042	0.043	0.214
CE9	Undiscounted accident cost reduction	8.824	0.042	0.043	0.161

Source: [144] and own calculations.

.

4.3. Comparative Analysis

The estimated weights of the specific criteria were compared with the results of the multi-criteria analysis developed as part of the STEŚ-R design documentation. In the aforementioned analysis, a 1–5 point scale was used in relation to the detailed criteria, where:

• 1 point means a criterion of little importance;
• 2 points means a criterion of little decisiveness;
• 3 points means an important criterion;
• 4 points means a significant criterion;
• 5 points means a decisive criterion.

In order to normalize the weights in individual groups and to achieve comparability between the criterion groups, a weight converter was used so that the sum within each group equaled 100.

In the case of the point method and the AHP and FAHP methods, the highest weights among the technical criteria were given to the criteria related to the length of the main route (CT1) and the projected traffic volume (CT2). The least important was the route extension indicator (CT10). In the case of the CRITIC method, the set of weights is characterized by greater diversity, the highest weight was given to criterion CT7—preferences of road managers, while the lowest weight was given to criterion CT3—land occupancy and CT9—surface area of engineering structures.

In the case of social criteria, the highest weights were given to the criteria regarding the number of applications from information meetings conducted against/for a given location variant (CS3 and CS4) and the criterion regarding the compliance of the route with the Local Spatial Development Plan (CS5). It should be noted that the above applies to weights obtained using methods from the design documentation and methods from the AHP family. When it comes to the CRITIC method, the highest weight was given to criterion CS2—residential buildings to be demolished.

Among environmental criteria, the highest weights (using the point method and AHP and FAHP) were given to the following criteria: CEN2, CEN4, CEN5, CEN6 and CEN8. In turn, the highest weight was given to the criterion concerning collisions with protected areas (CEN1) using the CRITIC method.

Among the economic criteria, the highest weights were given to the criteria related to the costs of: work preparation (CE1), property acquisition (CE2) and the total investment cost (CE3). A completely different set of weights was obtained using the CRITIC method, on the basis of which the first place in terms of importance was taken by the criterion CE8—undiscounted reduction of time costs.

4.4. Variants Ranking

After decomposing the decision problem and weighing the criteria, the next stage of multi-criteria analysis is developing a ranking of variants, in this case, location variants. Based on the literature review, it can be stated that currently the most popular method in the field of transport is the TOPSIS method, “based upon the concept that the chosen alternative should have the shortest distance from the ideal solution and the farthest from the negative-ideal solution” ([150], p. 128). A total of four full multi-criteria analyses were performed (for each set of criteria weights), and additionally, the final ranking of variants was compared to the results of the multi-criteria analysis from the design documentation of the analyzed bypass.

The multi-criteria analysis in the design documentation was carried out in several stages. After defining the groups of criteria and subcriteria and assigning weights (the point scale used was described in point 4.3 of this paper) in order to obtain the best representation of differences between variants, the variable values were subjected to linearization. Then, the obtained point values of the variants were multiplied by the weights of the subcriteria. In each group of main criteria, the sum of these products was calculated, which was then used to develop a ranking of variants [144].

Table 16. Decision matrix—technical criteria.

	CT1	CT2	CT3	CT4	CT5	CT6	CT7	CT8	CT9	CT10	CT11	CT12
Units	km	Cars/24 h	Cars/24 h	Cars/24 h	Pieces	Points	Pieces	%	m2	-	%	km
V1	8.54	9615	498	2396	0	70	2	38	12,437.9	1.054	41	15.574
V2	8.496	9252	840	2343	0	69.45	3	34	13,050	1.049	43	15.059
V3	9.704	9591	719	2458	1	58.3	4	15	12,821.5	1.198	39	6.082
V4	8.794	9252	840	2343	0	68.5	2	25	13,039.2	1.086	44	14.448

Source: [144].

,

Table 17. Decision matrix—social criteria.

	CS1	CS2	CS3	CS4	CS5
Units	Pieces	Pieces	Pieces	Pieces	%
V1	0	0	2	3	0
V2	0	1	3	4	35
V3	1	0	3	3	0
V4	0	0	3	1	0

Source: [144].

,

Table 18. Decision matrix—environmental criteria.

	CEN1	CEN2	CEN3	CEN4	CEN5	CEN6	CEN7	CEN8	CEN9	CEN10	CEN11
Units	m	m	ha	%	Pieces	Pieces	M2	Pieces	Pieces	Pieces	Pieces
V1	3240	3842	26.4097	26.5	1	2	1075	0	3	0	0
V2	3156	5115	25.2627	15.5	0	2	4421	0	3	0	2
V3	0	2769	29.6113	21.3	3	1	7084	2	4	2	0
V4	3305	5825	26.052	22	0	3	869	0	3	1	0

Source: [144].

and

Table 19. Decision matrix—economic criteria.

	CEC1	CEC2	CEC3	CEC4	CEC5	CEC6	CEC7	CEC8	CEC9
Units	PLN	PLN	PLN	PLN/km	%	PLN	-	PLN	PLN
V1	296,157,557	39,996,000	416,047,655	48,717,524	6.8	83,042,118	1.31	176,127,763	41,991,670
V2	310,535,032	25,114,000	419,460,649	49,371,545	7.16	96,677,783	1.36	187,394,423	43,708,029
V3	333,996,990	60,811,735	485,013,167	49,980,747	6.53	86,129,768	1.27	239,539,109	44,523,583
V4	318,592,288	26,103,000	430,702,317	48,976,838	6.94	90,507,816	1.33	187,394,423	43,708,029

Source: [144].

show the ratings of the variants in light of the following criteria. In turn,

Table 20. CI value in TOPSIS for each set of weights.

	CI Value
	Project	AHP	FAHP	CRITIC
V1	0.546	0.465	0.467	0.621
V2	0.622	0.73	0.723	0.514
V3	0.405	0.326	0.331	0.496
V4	0.513	0.441	0.443	0.601

shows the values of the final index of the TOPSIS method, on the basis of which the rankings of the variants of the selected road investment were developed (

Table 21. Variant rankings—comparative analysis.

Set of Weights	Variant Ranking
TOPSIS
Project	V2 > V1 > V4 > V3
AHP	V2 > V1 > V4 > V3
Fuzzy AHP	V2 > V1 > V4 > V3
CRITIC	V1 > V4 > V2 > V3
Ranking method + weights from the project	V2 > V1 > V4 > V3

).

Within the TOPSIS method, using weights from the design documentation, as well as weights from AHP and FAHP, the same result was obtained as presented in the project, i.e., W2 was in first place, while W3 turned out to be the worst. It should be emphasized that, in the design analysis, the difference between W2 and W1 was insignificant and amounted to about 1%, while—in the other analyses—(especially from AHP and FAHP) the dominance of W2 was visible.

A different ranking was obtained using the CRITIC weight set, W2 was in the first place, and W4 was in the second place. The only common point with the other rankings was W3 which, in each method, took the last place.

5. Discussion

Due to the fact that several weighing methods were used in the case study,

Table 22. Advantages and disadvantages of selected criteria weighting methods.

Weighing Method	Advantages	Disadvantages	Recommended Research Problems from the Transport Sector
point	simple algorithm possibility of evaluation by a group of experts	no pairwise comparison possible	evaluation of investment options
AHP	possibility of creating a hierarchical structure possibility of pairwise comparisons possibility of calculations in a regular spreadsheet/software possibility of assessing consistency possibility of assessment by a group of experts	possible problems with maintaining consistency of pairwise comparisons, especially in the case of larger matrix dimensions	assessment of the quality of transport systems assessment of preferences of travelers/traffic users assessment of criteria and variants in terms of different scenarios (environmental, economic, technical, etc.)
Fuzzy AHP	possibility of creating a hierarchical structure possibility of pairwise comparison possibility of taking into account uncertainty possibility of evaluation by a group of experts	labor-intensive	assessment of the quality of transport systems assessment of preferences of travelers/traffic users assessment of criteria and variants in terms of different scenarios (environmental, economic, technical, etc.)
CRITIC	the most objective tool the ability to calculate in a regular spreadsheet	does not take into account the decision-maker’s preferences	transport safety assessment

presents their advantages and disadvantages, as well as proposes research problems in the field of transport, for which the use of selected MCDM/MCDA methods is recommended. Furthermore,

Table 23. Evaluation of selected criteria weighting methods.

Criterion	Point	AHP	Fuzzy AHP	CRITIC
Algorithm complexity	−	−	+	−
Preference considerations	+	+	+	−
Fuzzy information	−	−	+	−
Labor-intensive	−	−	+	+
Ease of use	+	+	−	+
Spreadsheet/software	+	+	+	+
Possibility of integration with other methods	+	+	+	+

, as a summary, evaluates the selected weighing methods in the light of the criteria related to their application and algorithm.

6. Conclusions

To sum up, based on the literature review and the multi-criteria analyses performed, it can be stated that:

• the most popular multi-criteria method used in recent years in the transport sector is the TOPSIS method, which enables the ranking of decision variants based on the distance from patterns and anti-patterns,
• methods from the AHP family enjoy constant popularity, especially in the context of factor weighing, a novelty is the CRITIC method, which is distinguished by objective weighing that does not take into account preferences,
• it is recommended to use the AHP and Fuzzy AHP methods interchangeably due to very similar results, not only at the factor weighing stage, but also at the final variant ranking stage,
• the main research areas in the transport sector to which multi-criteria methods have been applied so far are: safety and quality of public transport; scenarios for the development of the public transport system; urban area; transport sustainability and resilience; road, air, rail, and sea transport; electric vehicles; other,
• recently, two research areas have developed: urban area and transport sustainability and resilience, which reflects new problems in the field of transport: transport issues in urbanized areas and the development of transport in accordance with the paradigm of sustainable development,
• due to the complex nature of decision-making problems in the field of transport, the need to take into account many, often contradictory criteria of a social, environmental, economic or technical nature, it is recommended to use at least two multi-criteria methods,
• among the future research directions, it is worth indicating a comparative analysis of the CRITIC method (which is a novelty in decision-making problems in the transport sector) to other methods of objective weighing of criteria, e.g., entropy.