Sustainable regional rail system pricing using a machine learning-based optimization approach

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dc.authoridDeveci, Muhammet/0000-0002-3712-976X
dc.authoridDelen, Dursun/0000-0001-8857-5148
dc.authoridYASAR, ILGIN/0000-0001-9896-9220
dc.authoridKARAKURT, AHMET/0000-0002-9954-8636
dc.authorwosidDeveci, Muhammet/V-8347-2017
dc.authorwosidDelen, Dursun/AGA-9892-2022
dc.authorwosidYASAR, ILGIN/N-7044-2016
dc.contributor.authorGokasar, Ilgin
dc.contributor.authorKarakurt, Ahmet
dc.contributor.authorKuvvetli, Yusuf
dc.contributor.authorDeveci, Muhammet
dc.contributor.authorDelen, Dursun
dc.contributor.authorPamucar, Dragan
dc.date.accessioned2024-05-19T14:46:30Z
dc.date.available2024-05-19T14:46:30Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractRegional transport pricing is indeed very vital in urban settings where the transportation network is spread out across large areas and can influence travel behavior and the sustainability of cities. Therefore, in addition to existing pricing systems, such as flat fare, distance-based fare, and zonal pricing, this study proposes a sustainable approach to regional rail system pricing using rent prices and a transportation affordability index. The proposed model aims to reduce commuters' overall travel distance in order to reduce air pollution and maintenance costs for public transportation vehicles. Rent-based pricing encourages people to rent houses in regions that shorten their travel distances and fill a gap in the literature on regional rail system pricing by dealing with the decentralization of the cities. A two-step clustering and non-linear optimization modeling approach are proposed based on face-to-face surveys with regional rail system passengers. For various clusters of stations, rent per income rates and rental-based ticket prices were obtained. Furthermore, a sensitivity analysis is conducted to evaluate different conditions of the affordability index and rent prices in the studied regions. Compared to the current pricing system, ticket revenues increased by 3.88% and 1.68% in rent-based pricing.en_US
dc.identifier.doi10.1007/s10479-023-05603-z
dc.identifier.issn0254-5330
dc.identifier.issn1572-9338
dc.identifier.scopus2-s2.0-85172700557en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1007/s10479-023-05603-z
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5535
dc.identifier.wosWOS:001071245700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofAnnals of Operations Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectRent-Based Pricingen_US
dc.subjectRegional Rail System Pricingen_US
dc.subjectPublic Transportationen_US
dc.subjectDecentralizationen_US
dc.subjectClustering Analysisen_US
dc.subjectMathematical Modelingen_US
dc.titleSustainable regional rail system pricing using a machine learning-based optimization approachen_US
dc.typeArticleen_US

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