Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Hala Hmamed
 -- 1700 2023-11-22 20:57:21 |
2 Reference format revised.
Lindsay Dong
 Meta information modification 1700 2023-11-23 09:29:43 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Hmamed, H.; Benghabrit, A.; Cherrafi, A.; Hamani, N. Sustainable Transportation System. Encyclopedia. Available online: https://encyclopedia.pub/entry/51945 (accessed on 21 December 2024).
Hmamed H, Benghabrit A, Cherrafi A, Hamani N. Sustainable Transportation System. Encyclopedia. Available at: https://encyclopedia.pub/entry/51945. Accessed December 21, 2024.
Hmamed, Hala, Asmaa Benghabrit, Anass Cherrafi, Nadia Hamani. "Sustainable Transportation System" Encyclopedia, https://encyclopedia.pub/entry/51945 (accessed December 21, 2024).
Hmamed, H., Benghabrit, A., Cherrafi, A., & Hamani, N. (2023, November 22). Sustainable Transportation System. In Encyclopedia. https://encyclopedia.pub/entry/51945
Hmamed, Hala, et al. "Sustainable Transportation System." Encyclopedia. Web. 22 November, 2023.
Sustainable Transportation System
Edit
This entry is adapted from the peer-reviewed paper 10.3390/su152115372

Sustainable transport has become an important element in structuring urban operations. In this context, sustainable transportation is a key factor to tackle environmental, social, and economic challenges. Eventually, the incorporation of sustainable solutions into transportation networks has grown significantly. Such growth is strictly related to globalization and urban development due to the large increase in transport needs.

sustainable transport sustainable transportation decision-making

1. Introduction

Transportation systems have significant environmental impacts due to their significant contribution to air pollution [1]. The increasing reliance on vehicles for daily needs presents various challenges, including climate change, environmental degradation, and health-related issues [2]. In fact, the harmful pollutants generated by the transportation sector have the potential to severely affect global health [3]. Therefore, it becomes crucial to develop a sustainable transportation infrastructure that prioritizes the efficient use of energy [4]. In this context, communities face the challenge of promoting sustainable transportation solutions to mitigate the different effects of pollution and excessive energy consumption [5]. Moreover, the growing population and business expansion bring new additional challenges, emphasizing the importance of sustainable transportation in meeting both social and economic requirements while sustaining the environment [6]. As a result, adopting sustainable transportation practices has the positive effect of aligning current and future economic development, enhancing transportation efficiency, and preserving the environment [7].
According to [8], sustainable transportation refers to a transportation system that reduces the social and environmental impacts. In fact, sustainable transportation solutions are needed to address the increasing requirement for transportation in expanding urban areas while preventing potential unfavorable social, economic, and environmental consequences [9]. Transportation planning, among the most challenging issues faced by urban transportation systems [10], frequently results in high costs, unscheduled delays, increased energy consumption, and increased emissions, pollution, and noise [11][12]. As a result, it has enormous environmental, economic, and social consequences. In addition, transportation planning in cities, particularly in developing countries, remains a serious concern due to growing urbanization [13]. It raises transportation costs by increasing fuel consumption, carbon emissions, and distribution inefficiencies, resulting in significant consequences for the environment and human health [14][15].
Given the number of conflicting economic, social, and environmental objectives involving sustainable transportation solutions, this issue can be classified as a multiple-criteria decision-making (MCDM) problem in which all of these objectives must be considered concurrently [16][17][18]. In current sustainable transportation problems such as route selection, the focus is not to achieve the lowest total price or the shortest delay [19]. Instead, the objective is to discover an optimal approach that maximizes beneficial impacts while also taking non-benefit characteristics into account [20]. As a result, decision makers are ultimately faced with a challenging set of parameters relative to the available alternative options.
Numerous studies have examined a broad range of variables from different perspectives in the field of route selection decision making [21][22]. These factors frequently include transportation cost effectiveness, time, capacity, and distance optimization. Previous works focused on environmental factors including fuel consumption, pollution, waste, and emissions [23][24][25]. These criteria have been commonly explored using MCDM techniques, allowing researchers to prioritize alternatives and suggest the most optimal choice based on their models. For example, the analytic hierarchy process (AHP) has different applications in the area of transportation [26]. For instance, AHP was used by [27] to determine the optimal logistics network for a crucial freight route. Moreover, [15] used AHP to select the best solutions for developing an environmentally friendly transportation system. In addition, route effectiveness is a critical factor in sustainable transportation. It is determined by several important variables that vary across various transportation network routes and regions.
The performance of transportation networks has frequently been a source of concern and requires more assessment [28]. In this context, the data envelopment analysis (DEA) approach may be performed to evaluate transport route efficiency given a particular set of inputs and outputs in the field of transportation [29]. It is extensively used in manufacturing, operation, leadership, and economics to experimentally evaluate operational effectiveness [30]. The DEA has a broad range of applications for transportation-related issues. For example, [31] presented an approach to employing DEA in maximizing the efficiency of metropolitan public transportation networks. Furthermore, [32] used DEA to assess the effectiveness of possible various state transportation agencies in roadway management. The authors of [33] developed a framework to examine the operational effectiveness of transportation networks. In addition, [34] presented a solution to the logistics challenge that takes into account various inputs and outputs associated with each transportation route. Other research proposed a multimodal transportation routing approach based on the cost and emissions criteria [35].

2. Sustainable Transportation System

Sustainable transport has become an important element in structuring urban operations [36]. In this context, sustainable transportation is a key factor to tackle environmental, social, and economic challenges [37]. Eventually, the incorporation of sustainable solutions into transportation networks has grown significantly. Such growth is strictly related to globalization and urban development due to the large increase in transport needs [2]. The rise in interest in sustainable transportation can be related to community initiatives that emphasize a higher focus on living standards and the efficient administration of public assets [38]. As a result, developing a sustainable transportation system is vital to address significant major problems including climate change, air pollution, traffic congestion, and resource depletion [5].
In fact, traditional transportation planning is primarily concerned with expanding availabilities to meet economic issues. Nonetheless, it overlooked social and environmental challenges such as energy consumption, noise pollution, emissions, energy consumption, ecological damage, waste generation, and road safety [38][39]. Consequently, effective transportation planning which includes route selection is a crucial contributing component in establishing sustainable transportation systems [40]. Indeed, the classification of various transportation routes based on sustainable considerations assists in evaluating their significance in achieving established objectives [28]. However, economic, environmental, and social factors may not exclude the selection of other vital components for transportation route planning. In other words, current transportation planning must be achieved by balancing sustainable aspects and transportation features.
Sustainable transportation planning is known as an integrated approach to elaborate effective, balanced, and environmentally responsible transportation systems [10]. It entails integrating numerous transportation variables such as vehicle, route type, distance, and time. To develop connected, accessible, and safe transportation networks, the planning process considers various aspects such as organizational and environmental elements, urban design, infrastructure, and technological development. It also addresses the requirements of different communities and nations [11]. Sustainable transportation planning promotes the use of renewable energy sources and cutting-edge technologies to maximize energy efficiency and reduce the overall environmental impacts [41]. Sustainable transportation planning seeks to build flexible and resilient transportation systems that support economic growth, enhance public health, and contribute to a more sustainable urban environment. This is achieved by promoting collaboration among stakeholders and engaging with public regulations [39][42].
In this context, previous researchers have studied the effectiveness of various public transportation systems, including cars, buses, trains, light rail, and innovative mobility solutions like ride-sharing and bike-sharing programs [43]. Many researchers have investigated the economic and environmental benefits and challenges associated with different transportation techniques and solutions [44]. MCDM techniques have been extensively applied to address transportation planning challenges and find optimal solutions that consider multiple criteria and objectives. Indeed, MCDM approaches help transportation administrators in making informed decisions by considering several factors [45]. These factors include the studies of transportation mode selection [46], route prioritization [40], infrastructure investment [12], public transport planning [38], transport policy evaluation [47], and freight urban planning [48].
To handle transportation-planning challenges, AHP is frequently used when combined with other MCDM techniques such as DEA, TOPSIS (technique for order preference by similarity to ideal solution), ELECTRE (elimination and choice translating reality), VIKOR, and GRA (grey relational analysis). For example, Ref. [49] used integrated AHP with TOPSIS to address public transportation regarding bus selection. The study proposed a framework based on the hybrid technique to optimize the bus design with respect to a set of decision variables. Additionally, Ref. [50] employed AHP combined with VIKOR to determine efficient petrol station selection in transportation sectors. Moreover, Ref. [51] used an integrated approach of AHP and ELECTRE approach for sustainable path selection to determine transport-effective alternative solutions. Ultimately, these methods help decision makers to analyze complex trade-offs between conflicting objectives and arrive at informed and robust decisions for more optimized transportation systems [52]. In addition, the integration of methodologies into a hybrid evaluation of decisions is not a novel concept; it has been frequently recommended to develop an effective strategy by leveraging their strengths and complementing their limitations [53]. There are several papers on hybrid MCDM techniques for transportation systems as shown in Table 1.
Table 1. Existing studies on transportation MCDM combined approaches.
Reference Research Context Applied Approach Sustainability Dimension
[28] Multimodal transportation networks MCDM, DEA Economic
[49] Bus fleet network AHP, TOPSIS Economic, environmental
[54] Submarine power cable routing selection G-MCDA, DEA Economic, social
[55] Emergency evacuation paths of the urban metro station TOPSIS, GRA Social,
environmental
[56] Route selection in multimodal transportation networks AHP, DEA, TOPSIS Economic
[57] Transmission network of nuclear power plant Fuzzy AHP Environmental,
social
[51]
[58]		 Sustainable tourism paths
Tourism optimal
path selection		 AHP, ELECTRESWOT, AHP Economic
[17] Airline new route selection MCDM Economic
[41] Route selection in multimodal supply chains Fuzzy MCDM Economic
[44] Path and site landfill selection AHP, GIS Social,
economic
[59] Municipal solid waste landfill siting AHP, GIS Environmental, socio-economic
[60] Optimization of public transport networks AHP Economic
[61] Transport service on public roads and passenger transport AHP Economic
[62] Operational efficiencies of Turkish airports AHP, DEA Economic
[63] Sustainable supply network optimization DEA Economic
[11] Vehicle routing optimization model AHP Environmental,
economic
[64] Regional transport sustainability SBM, DEA Economic, environmental,
social
[48] Multimodal freight transportation systems AHP, DEA Economic, environmental
[18] Optimal routing for mass transit systems MCDM Economic, environmental,
social
[7] Sustainable route selection of petroleum transportation MCDM Economic, environmental,
social
[46] An optimization model for sustainable transportation in the mining industry AHP, DEA Economic, environmental
[40] Route prioritization of urban public transportation MCDM Economic, environmental, social
[65] Public road transportation systems AHP, DEA Economic
[53] Multicriteria route selection AHP, TOPSIS Economic
[66] Multimodal green logistics: AHP, DEA Environmental, economic
[35] Route selection in multimodal transportation AHP Environmental, economic
[67] Distribution network planning reliability AHP, DEA Economic
[68] Environmental assessment of land transportation DEA Environmental
[69] Sustainable intermodal transport affected by COVID-19 AHP, DEA Environmental, economic
[39] The last-mile delivery problem with service options MCDE Environmental, social, economic

As reported in Table 1, and according to the literature, the integration of sustainable aspects in handling transportation route selection decision-making problems has not been widely addressed in the literature. In addition, the integration of AHP and DEA for managing sustainable transportation systems for route selection has not been found in the published research. 

References

  1. Ajay, P.; Nagaraj, B.; Arunkumar, R.; Huang, R. Enhancing computational energy transportation in IoT systems with an efficient wireless tree-based routing protocol. Results Phys. 2023, 51, 106747.
  2. Amini, A.; Tavakkoli-Moghaddam, R.; Ebrahimnejad, S.; Ghodratnama, A. Mathematical modeling of a competitive transportation-location arc routing problem. Comput. Ind. Eng. 2023, 182, 109400.
  3. Afghah, M.; Sajadi, S.M.; Razavi, S.M.; Taghizadeh-Yazdi, M. Hard dimensions evaluation in sustainable supply chain management for environmentally adaptive and mitigated adverse eco-effect environmental policies. Bus. Strategy Environ. 2023, bse.3407.
  4. Alamelu, R.; Rengarajan, V.; Dinesh, S.; Nalini, R.; Shobhana, N.; Amudha, R. Sustainable supply chain practices with reverse innovation in healthcare start-ups—A Structural Equation Model (SEM) approach. Mater. Today Proc. 2022, 52, 882–887.
  5. Salman, M.Y.; Hasar, H. Review on environmental aspects in smart city concept: Water, waste, air pollution and transportation smart applications using IoT techniques. Sustain. Cities Soc. 2023, 94, 104567.
  6. Joshi, S.; Sharma, M. Sustainable Performance through Digital Supply Chains in Industry 4.0 Era: Amidst the Pandemic Experience. Sustainability 2022, 14, 16726.
  7. Simić, V.; Milovanović, B.; Pantelić, S.; Pamučar, D.; Tirkolaee, E.B. Sustainable route selection of petroleum transportation using a type-2 neutrosophic number based ITARA-EDAS model. Inf. Sci. 2023, 622, 732–754.
  8. Nguyen, T.-H.; Jung, J.J. Swarm intelligence-based green optimization framework for sustainable transportation. Sustain. Cities Soc. 2021, 71, 102947.
  9. Parviziomran, I.; Mahmoudi, M.; Boloori, A. Evaluating operating models and urbanism for transportation operations of circular reuse platforms. Comput. Ind. Eng. 2023, 177, 109067.
  10. Yang, X.; Feng, R.; Xu, P.; Wang, X.; Qi, M. Internet-of-Things-augmented dynamic route planning approach to the airport baggage handling system. Comput. Ind. Eng. 2023, 175, 108802.
  11. Liu, Z.; Niu, Y.; Guo, C.; Jia, S. A Vehicle Routing Optimization Model for Community Group Buying Considering Carbon Emissions and Total Distribution Costs. Energies 2023, 16, 931.
  12. Alfaggi, W.; Naimi, S. An Optimal Cost Estimation Practices of Fuzzy AHP for Building Construction Projects in Libya. Civ. Eng. J. 2022, 8, 1194–1204.
  13. Anser, M.K.; Yousaf, Z.; Hishan, S.S.; Nassani, A.A.; Sheikh, A.Z.; Vo, X.V.; Zaman, K.; Qazi Abro, M.M. Dynamic linkages between transportation, waste management, and carbon pricing: Evidence from the Arab World. J. Clean. Prod. 2020, 269, 122151.
  14. Mrabti, N.; Hamani, N.; Boulaksil, Y.; Amine Gargouri, M.; Delahoche, L. A multi-objective optimization model for the problems of sustainable collaborative hub location and cost sharing. Transp. Res. Part E Logist. Transp. Rev. 2022, 164, 102821.
  15. Buwana, E.; Hasibuan, H.S.; Abdini, C. Alternatives Selection for Sustainable Transportation System in Kasongan City. Procedia-Soc. Behav. Sci. 2016, 227, 11–18.
  16. Hosouli, S.; Elvins, J.; Searle, J.; Boudjabeur, S.; Bowyer, J.; Jewell, E. A Multi-Criteria decision making (MCDM) methodology for high temperature thermochemical storage material selection using graph theory and matrix approach. Mater. Des. 2023, 227, 111685.
  17. Deveci, M.; Demirel, N.Ç.; Ahmetoğlu, E. Airline new route selection based on interval type-2 fuzzy MCDM: A case study of new route between Turkey- North American region destinations. J. Air Transp. Manag. 2017, 59, 83–99.
  18. Rahimi, M.; Thanh, H.V.; Ebrahimzade, I.; Abbaspour-Fard, M.H.; Rohani, A. A multi-criteria decision-making (MCDM) approach to determine the synthesizing routes of biomass-based carbon electrode material in supercapacitors. J. Clean. Prod. 2023, 397, 136606.
  19. Sarmah, S.P.; Yadav, R.; Rathore, P. Development of Vehicle Routing model in urban Solid Waste Management system under periodic variation: A case study. IFAC-PapersOnLine 2019, 52, 1961–1965.
  20. Ardito, L. The influence of firm digitalization on sustainable innovation performance and the moderating role of corporate sustainability practices: An empirical investigation. Bus. Strategy Environ. 2023, bse.3415.
  21. Dereci, U.; Karabekmez, M.E. The applications of multiple route optimization heuristics and meta-heuristic algorithms to solid waste transportation: A case study in Turkey. Decis. Anal. J. 2022, 4, 100113.
  22. Asefi, H.; Shahparvari, S.; Chhetri, P. Integrated Municipal Solid Waste Management under uncertainty: A tri-echelon city logistics and transportation context. Sustain. Cities Soc. 2019, 50, 101606.
  23. Hai, T.; Alenizi, F.A.; Alshahri, A.H.; Chauhan, B.S.; Metwally, A.S.M.; Almujibah, H.R. Energy and Environmental Analyses of a Sustainable Multi-Generation Municipal Solid Waste-To-Energy Integrated System for Hydrogen Production. Process Saf. Environ. Prot. 2023, S0957582023005116.
  24. Li, Y.; Hong, H.; Sun, C.; Geng, Z.; Zhang, C. Collection and transportation system construction of potentially viral municipal solid waste during the COVID-19 pandemic in China. Sci. Total Environ. 2022, 851, 157964.
  25. Lane, K.J.; Levy, J.I.; Patton, A.P.; Durant, J.L.; Zamore, W.; Brugge, D. Relationship between traffic-related air pollution and inflammation biomarkers using structural equation modeling. Sci. Total Environ. 2023, 870, 161874.
  26. Islam, R.; Saaty, T.L. The Analytic Hierarchy Process in the Transportation Sector. In Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems; Ehrgott, M., Naujoks, B., Stewart, T.J., Wallenius, J., Eds.; Lecture Notes in Economics and Mathematical Systems; Springer: Berlin/Heidelberg, Germany, 2010; Volume 634, pp. 79–91. ISBN 978-3-642-04044-3.
  27. Zoran, D. Application of the AHP Method for Selection of a Transportation System in Mine Planning. Podzemni Radovi 2011, 19, 93–99.
  28. Koohathongsumrit, N.; Meethom, W. An integrated approach of fuzzy risk assessment model and data envelopment analysis for route selection in multimodal transportation networks. Expert Syst. Appl. 2021, 171, 114342.
  29. De Oliveira, M.S.; Steffen, V.; De Francisco, A.C.; Trojan, F. Integrated data envelopment analysis, multi-criteria decision making, and cluster analysis methods: Trends and perspectives. Decis. Anal. J. 2023, 8, 100271.
  30. Lai, P.; Potter, A.; Beynon, M.; Beresford, A. Evaluating the efficiency performance of airports using an integrated AHP/DEA-AR technique. Transp. Policy 2015, 42, 75–85.
  31. Roháčová, V. A DEA based approach for optimization of urban public transport system. Cent. Eur. J. Oper. Res. 2015, 23, 215–233.
  32. Ozbek, M.E.; De La Garza, J.M.; Triantis, K. Data Envelopment Analysis as a Decision-Making Tool for Transportation Professionals. J. Transp. Eng. 2009, 135, 822–831.
  33. Sampaio, B.R.; Neto, O.L.; Sampaio, Y. Efficiency analysis of public transport systems: Lessons for institutional planning. Transp. Res. Part A Policy Pract. 2008, 42, 445–454.
  34. Acar, E.; Aplak, H. A Model Proposal for a Multi-Objective and Multi-Criteria Vehicle Assignment Problem: An Application for a Security Organization. MCA 2016, 21, 39.
  35. Kengpol, A.; Tuammee, S.; Tuominen, M. The development of a framework for route selection in multimodal transportation. Int. J. Logist. Manag. 2014, 25, 581–610.
  36. Taşkın, A.; Demir, N. Life cycle environmental and energy impact assessment of sustainable urban municipal solid waste collection and transportation strategies. Sustain. Cities Soc. 2020, 61, 102339.
  37. Prömpeler, J.; Veltrop, D.B.; Stoker, J.I.; Rink, F.A. Striving for sustainable development at the top: Exploring the interplay of director and CEO values on environmental sustainability focus. Bus. Strategy Environ. 2023, bse.3408.
  38. Bubalo, T.; Rajsman, M.; Škorput, P. Methodological Approach for Evaluation and Improvement of Quality Transport Service in Public Road Passenger Transport. Tehnički Vjesnik 2022, 29, 139–148.
  39. Pourmohammadreza, N.; Jokar, M.R.A. A Novel Two-Phase Approach for Optimization of the Last-Mile Delivery Problem with Service Options. Sustainability 2023, 15, 8098.
  40. Wenz, K.-P.; Serrano-Guerrero, X.; Barragán-Escandón, A.; González, L.G.; Clairand, J.-M. Route prioritization of urban public transportation from conventional to electric buses: A new methodology and a study of case in an intermediate city of Ecuador. Renew. Sustain. Energy Rev. 2021, 148, 111215.
  41. Koohathongsumrit, N.; Chankham, W. Route selection in multimodal supply chains: A fuzzy risk assessment model-BWM-MARCOS framework. Appl. Soft Comput. 2023, 137, 110167.
  42. Gopal, N.; Panchal, D. Fuzzy decision support system for sustainable operational performance optimization for boiler unit in milk process industry. Appl. Soft Comput. 2023, 135, 109983.
  43. Cheng, M.; Wei, W. An AHP-DEA Approach of the Bike-Sharing Spots Selection Problem in the Free-Floating Bike-Sharing System. Discret. Dyn. Nat. Soc. 2020, 2020, 7823971.
  44. Yousefi, H.; Javadzadeh, Z.; Noorollahi, Y.; Yousefi-Sahzabi, A. Landfill Site Selection Using a Multi-Criteria Decision-Making Method: A Case Study of the Salafcheghan Special Economic Zone, Iran. Sustainability 2018, 10, 1107.
  45. Wang, C.-N.; Dang, T.-T.; Nguyen, N.-A.-T.; Wang, J.-W. A combined Data Envelopment Analysis (DEA) and Grey Based Multiple Criteria Decision Making (G-MCDM) for solar PV power plants site selection: A case study in Vietnam. Energy Rep. 2022, 8, 1124–1142.
  46. Gupta, P.; Mehlawat, M.K.; Aggarwal, U.; Charles, V. An integrated AHP-DEA multi-objective optimization model for sustainable transportation in mining industry. Resour. Policy 2021, 74, 101180.
  47. Morgan, R. An investigation of constraints upon fisheries diversification using the Analytic Hierarchy Process (AHP). Mar. Policy 2017, 86, 24–30.
  48. Kaewfak, K.; Huynh, V.-N.; Ammarapala, V.; Ratisoontorn, N. A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation Systems. IEEE Access 2020, 8, 153756–153773.
  49. James, A.T.; Vaidya, D.; Sodawala, M.; Verma, S. Selection of bus chassis for large fleet operators in India: An AHP-TOPSIS approach. Expert Syst. Appl. 2021, 186, 115760.
  50. Ayyildiz, E.; Taskin, A. A novel spherical fuzzy AHP-VIKOR methodology to determine serving petrol station selection during COVID-19 lockdown: A pilot study for İstanbul. Socio-Econ. Plan. Sci. 2022, 83, 101345.
  51. Carra, M.; Botticini, F.; Pavesi, F.C.; Maternini, G.; Pezzagno, M.; Barabino, B. A comparative cycling path selection for sustainable tourism in Franciacorta. An integrated AHP-ELECTRE method. Transp. Res. Procedia 2023, 69, 448–455.
  52. Tavana, M.; Soltanifar, M.; Santos-Arteaga, F.J.; Sharafi, H. Analytic hierarchy process and data envelopment analysis: A match made in heaven. Expert Syst. Appl. 2023, 223, 119902.
  53. Singh, S.P.; Singh, P. A hybrid decision support model using axiomatic fuzzy set theory in AHP and TOPSIS for multicriteria route selection. Complex Intell. Syst. 2018, 4, 133–143.
  54. Jiang, D.; Wu, B.; Yang, X.; Van Gelder, P.H.A.J.M. A fuzzy evidential reasoning based approach for submarine power cable routing selection for offshore wind farms. Ocean Eng. 2019, 193, 106616.
  55. Zhang, J.; Huang, D.; You, Q.; Kang, J.; Shi, M.; Lang, X. Evaluation of emergency evacuation capacity of urban metro stations based on combined weights and TOPSIS-GRA method in intuitive fuzzy environment. Int. J. Disaster Risk Reduct. 2023, 95, 103864.
  56. Koohathongsumrit, N.; Meethom, W. Route selection in multimodal transportation networks: A hybrid multiple criteria decision-making approach. J. Ind. Prod. Eng. 2021, 38, 171–185.
  57. Abdullah, A.G.; Shafii, M.A.; Pramuditya, S.; Setiadipura, T.; Anzhar, K. Multi-criteria decision making for nuclear power plant selection using fuzzy AHP: Evidence from Indonesia. Energy AI 2023, 14, 100263.
  58. Long, C.; Lu, S.; Zhu, Y. Research on Popular Science Tourism Based on SWOT-AHP Model: A Case Study of Koktokay World Geopark in China. Sustainability 2022, 14, 8974.
  59. Kamdar, I.; Ali, S.; Bennui, A.; Techato, K.; Jutidamrongphan, W. Municipal solid waste landfill siting using an integrated GIS-AHP approach: A case study from Songkhla, Thailand. Resour. Conserv. Recycl. 2019, 149, 220–235.
  60. Lin, G.; Xu, H.; Wang, S.; Lin, C.; Huang, C. Performance Optimisation of Public Transport Networks Using AHP-Dependent Multi-Aspiration-Level Goal Programming. Energies 2022, 15, 6479.
  61. Loureiro, E.S.P.; Figueiredo, N.M.D.; Campos Filho, L.C.P.; Filgueiras, T.C.G.M. Analysis of relative efficiency of vessels of passenger transportation in the Brazilian Amazon: An AHP-DEA approach. Acta Sci. Technol. 2022, 45, e58199.
  62. Yilmaz, M.K.; Kusakci, A.O.; Aksoy, M.; Hacioglu, U. The evaluation of operational efficiencies of Turkish airports: An integrated spherical fuzzy AHP/DEA approach. Appl. Soft Comput. 2022, 119, 108620.
  63. Wang, Y.; Shi, Q.; Hu, Q.; You, Z.; Bai, Y.; Guo, C. An efficiency sorting multi-objective optimization framework for sustainable supply network optimization and decision making. J. Clean. Prod. 2020, 272, 122842.
  64. Tian, N.; Tang, S.; Che, A.; Wu, P. Measuring regional transport sustainability using super-efficiency SBM-DEA with weighting preference. J. Clean. Prod. 2020, 242, 118474.
  65. Kumar, A.; Singh, G.; Vaidya, O.S. A Comparative Evaluation of Public Road Transportation Systems in India Using Multicriteria Decision-Making Techniques. J. Adv. Transp. 2020, 2020, 8827186.
  66. Goodarzi, F.; Abdollahzadeh, V.; Zeinalnezhad, M. An integrated multi-criteria decision-making and multi-objective optimization framework for green supplier evaluation and optimal order allocation under uncertainty. Decis. Anal. J. 2022, 4, 100087.
  67. Xin, Z.; Chen, L.; Yang, Y.; Miao, K.; Li, Z. Application of AHP-DEA Model in Comprehensive Evaluation of Distribution Network Planning Reliability. In Proceedings of the 2019 IEEE Sustainable Power and Energy Conference (iSPEC), Beijing, China, 21–23 November 2019; IEEE: Beijing, China, 2019; pp. 290–294.
  68. Wang, C.-N.; Le, T.Q.; Yu, C.-H.; Ling, H.-C.; Dang, T.-T. Strategic Environmental Assessment of Land Transportation: An Application of DEA with Undesirable Output Approach. Sustainability 2022, 14, 972.
  69. Golnar, M.; Beškovnik, B. A Multi-Criteria Approach for Evaluating a Sustainable Intermodal Transport Chain Affected by the COVID-19 Pandemic. JMSE 2022, 10, 1644.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Transportation Science & Technology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Hala Hmamed
,
Asmaa Benghabrit
,
Anass Cherrafi
,
Nadia Hamani
View Times: 332
Revisions: 2 times (View History)
Update Date: 23 Nov 2023
Table of Contents
    1000/1000
    Hot Most Recent
    Video Production Service
    Feedback