A novel rough numbers based extended MACBETH method for the prioritization of the connected autonomous vehicles in real-time traffic management
Апстракт
Digital transformation can help to make better use of existing transportation networks that are congested. One solution to the road congestion problem is real-time traffic management, which focuses on enhancing traffic flow conditions. The advantages of real-time traffic management systems have developed significantly as a result of connected autonomous vehicle (CAV) innovations. CAVs can act as enforcers for managing the traffic. This study aims to propose a novel rough numbers-based extended Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) method for prioritizing real-time traffic management systems. Furthermore, a new approach for defining rough numbers is proposed, based on an improved methodology for defining rough numbers' lower and upper limits. This allows consideration of mutual relations between a set of objects and flexible representation of rough boundary interval depending on the dynamic environmental conditions. In this study, three main alte...rnatives are defined for real-time traffic management systems: real-time traffic management, real-time traffic management integrated with CAVs, and real-time traffic management by using CAVs. For these alternatives, 5 main criteria and 18 sub-criteria are defined and then prioritized using the fuzzy multi-criteria decision-making (MCDM) approach. The proposed method's performance is validated through scenario analysis. The findings demonstrate that the proposed method is effective and applicable to real-world conditions. According to the study's findings, real-time traffic management with CAVs is the most advantageous alternative, while real-time traffic management integrated with CAVs is the least advantageous
Кључне речи:
Rough numbers / Real-time traffic management / Multi-criteria decision making / Fuzzy sets / Digital transformation / Connected autonomous vehicleИзвор:
Expert Systems with Applications, 2023, 211Издавач:
- Pergamon-Elsevier Science Ltd, Oxford
Финансирање / пројекти:
- Scientific and Technological Research Council of Turkey [TUBITAK 1001, 120M574]
DOI: 10.1016/j.eswa.2022.118445
ISSN: 0957-4174
WoS: 000906598300005
Scopus: 2-s2.0-85136510595
Институција/група
Fakultet organizacionih naukaTY - JOUR AU - Gokasar, Ilgin AU - Pamučar, Dragan AU - Deveci, Muhammet AU - Ding, Weiping PY - 2023 UR - https://rfos.fon.bg.ac.rs/handle/123456789/2448 AB - Digital transformation can help to make better use of existing transportation networks that are congested. One solution to the road congestion problem is real-time traffic management, which focuses on enhancing traffic flow conditions. The advantages of real-time traffic management systems have developed significantly as a result of connected autonomous vehicle (CAV) innovations. CAVs can act as enforcers for managing the traffic. This study aims to propose a novel rough numbers-based extended Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) method for prioritizing real-time traffic management systems. Furthermore, a new approach for defining rough numbers is proposed, based on an improved methodology for defining rough numbers' lower and upper limits. This allows consideration of mutual relations between a set of objects and flexible representation of rough boundary interval depending on the dynamic environmental conditions. In this study, three main alternatives are defined for real-time traffic management systems: real-time traffic management, real-time traffic management integrated with CAVs, and real-time traffic management by using CAVs. For these alternatives, 5 main criteria and 18 sub-criteria are defined and then prioritized using the fuzzy multi-criteria decision-making (MCDM) approach. The proposed method's performance is validated through scenario analysis. The findings demonstrate that the proposed method is effective and applicable to real-world conditions. According to the study's findings, real-time traffic management with CAVs is the most advantageous alternative, while real-time traffic management integrated with CAVs is the least advantageous PB - Pergamon-Elsevier Science Ltd, Oxford T2 - Expert Systems with Applications T1 - A novel rough numbers based extended MACBETH method for the prioritization of the connected autonomous vehicles in real-time traffic management VL - 211 DO - 10.1016/j.eswa.2022.118445 UR - conv_2822 ER -
@article{ author = "Gokasar, Ilgin and Pamučar, Dragan and Deveci, Muhammet and Ding, Weiping", year = "2023", abstract = "Digital transformation can help to make better use of existing transportation networks that are congested. One solution to the road congestion problem is real-time traffic management, which focuses on enhancing traffic flow conditions. The advantages of real-time traffic management systems have developed significantly as a result of connected autonomous vehicle (CAV) innovations. CAVs can act as enforcers for managing the traffic. This study aims to propose a novel rough numbers-based extended Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) method for prioritizing real-time traffic management systems. Furthermore, a new approach for defining rough numbers is proposed, based on an improved methodology for defining rough numbers' lower and upper limits. This allows consideration of mutual relations between a set of objects and flexible representation of rough boundary interval depending on the dynamic environmental conditions. In this study, three main alternatives are defined for real-time traffic management systems: real-time traffic management, real-time traffic management integrated with CAVs, and real-time traffic management by using CAVs. For these alternatives, 5 main criteria and 18 sub-criteria are defined and then prioritized using the fuzzy multi-criteria decision-making (MCDM) approach. The proposed method's performance is validated through scenario analysis. The findings demonstrate that the proposed method is effective and applicable to real-world conditions. According to the study's findings, real-time traffic management with CAVs is the most advantageous alternative, while real-time traffic management integrated with CAVs is the least advantageous", publisher = "Pergamon-Elsevier Science Ltd, Oxford", journal = "Expert Systems with Applications", title = "A novel rough numbers based extended MACBETH method for the prioritization of the connected autonomous vehicles in real-time traffic management", volume = "211", doi = "10.1016/j.eswa.2022.118445", url = "conv_2822" }
Gokasar, I., Pamučar, D., Deveci, M.,& Ding, W.. (2023). A novel rough numbers based extended MACBETH method for the prioritization of the connected autonomous vehicles in real-time traffic management. in Expert Systems with Applications Pergamon-Elsevier Science Ltd, Oxford., 211. https://doi.org/10.1016/j.eswa.2022.118445 conv_2822
Gokasar I, Pamučar D, Deveci M, Ding W. A novel rough numbers based extended MACBETH method for the prioritization of the connected autonomous vehicles in real-time traffic management. in Expert Systems with Applications. 2023;211. doi:10.1016/j.eswa.2022.118445 conv_2822 .
Gokasar, Ilgin, Pamučar, Dragan, Deveci, Muhammet, Ding, Weiping, "A novel rough numbers based extended MACBETH method for the prioritization of the connected autonomous vehicles in real-time traffic management" in Expert Systems with Applications, 211 (2023), https://doi.org/10.1016/j.eswa.2022.118445 ., conv_2822 .