IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i7p976-d1363562.html
   My bibliography  Save this article

A Novel Data-Envelopment Analysis Interval-Valued Fuzzy-Rough-Number Multi-Criteria Decision-Making (DEA-IFRN MCDM) Model for Determining the Efficiency of Road Sections Based on Headway Analysis

Author

Listed:
  • Dejan Andjelković

    (Faculty of Applied Sciences Niš, University Business Academy in Novi Sad, Dušana Popovića 22a, 18000 Niš, Serbia)

  • Gordan Stojić

    (Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, Serbia)

  • Nikola Nikolić

    (Faculty of Technical Sciences “Mihajlo Pupin” Zrenjanin, University of Novi Sad, Djure Djakovica bb, 23101 Zrenjanin, Serbia)

  • Dillip Kumar Das

    (Sustainable Transportation Research Group, Civil Engineering, School of Engineering, University of Kwazulu Natal, Durban 4041, South Africa)

  • Marko Subotić

    (Faculty of Transport and Traffic Engineering, University of East Sarajevo, Vojvode Mišića 52, 74000 Doboj, Bosnia and Herzegovina)

  • Željko Stević

    (College of Engineering, Korea University, 145 Anam-Ro, Seongbuk-gu, Seoul 02841, Republic of Korea)

Abstract

The capacity of transport infrastructure is one of the very important tasks in transport engineering, which depends mostly on the geometric characteristics of road and headway analysis. In this paper, we have considered 14 road sections and determined their efficiency based on headway analysis. We have developed a novel interval fuzzy-rough-number decision-making model consisting of DEA (data envelopment analysis), IFRN SWARA (interval-valued fuzzy-rough-number stepwise weight-assessment-ratio analysis), and IFRN WASPAS (interval-valued fuzzy-rough-number weighted-aggregate sum–product assessment) methods. The main contribution of this study is a new extension of WASPAS method with interval fuzzy rough numbers. Firstly, the DEA model was applied to determine the efficiency of 14 road sections according to seven input–output parameters. Seven out of the fourteen alternatives showed full efficiency and were implemented further in the model. After that, the IFRN SWARA method was used for the calculation of the final weights, while IFRN WASPAS was applied for ranking seven of the road sections. The results show that two sections are very similar and have almost equal efficiency, while the other results are very stable. According to the results obtained, the best-ranked is a measuring segment of the Ivanjska–Šargovac section, with a road gradient = −5.5%, which has low deviating values of headways according to the measurement classes from PC-PC to AT-PC, which shows balanced and continuous traffic flow. Finally, verification tests such as changing the criteria weights, comparative analysis, changing the λ parameter, and reverse rank analysis have been performed.

Suggested Citation

  • Dejan Andjelković & Gordan Stojić & Nikola Nikolić & Dillip Kumar Das & Marko Subotić & Željko Stević, 2024. "A Novel Data-Envelopment Analysis Interval-Valued Fuzzy-Rough-Number Multi-Criteria Decision-Making (DEA-IFRN MCDM) Model for Determining the Efficiency of Road Sections Based on Headway Analysis," Mathematics, MDPI, vol. 12(7), pages 1-20, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:7:p:976-:d:1363562
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/7/976/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/7/976/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hossein Samadi & Iman Aghayan & Khaled Shaaban & Farhad Hadadi, 2023. "Development of Performance Measurement Models for Two-Lane Roads under Vehicular Platooning Using Conjugate Bayesian Analysis," Sustainability, MDPI, vol. 15(5), pages 1-26, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:7:p:976-:d:1363562. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.