Vehicle Delay Estimation at Signalized Intersections Using Machine Learning Algorithms

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dc.authoridBagdatli, Muhammed Emin Cihangir/0000-0002-1424-6920
dc.contributor.authorBagdatli, Muhammed Emin Cihangir
dc.contributor.authorDokuz, Ahmet Sakir
dc.date.accessioned2024-11-07T13:24:55Z
dc.date.available2024-11-07T13:24:55Z
dc.date.issued2021
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractAccurate determination of average vehicle delays is significant for effective management of a signalized intersection. The vehicle delays can be determined by field studies, however, this approach is costly and time consuming. Analytical methods which are commonly utilized to estimate delay cannot generate accurate estimates, especially in oversaturated traffic flow conditions. Delay estimation models based on artificial intelligence have been presented in the literature in recent years to estimate the delay more accurately. However, the number of artificial/heuristic intelligence techniques utilized for vehicle delay estimation is limited in the literature. In this study, estimation models are developed using four different machine learning methods-support vector regression (SVR), random forest (RF), k nearest neighbor (kNN), and extreme gradient boosting (XGBoost)-that have not previously been applied in the literature for vehicle delay estimation at signalized intersections. The models were tested with data collected from 12 signalized intersections located in Ankara, the capital of Turkey, and the performance of the models was revealed. The models were furthermore compared with successful delay models from the literature. The developed models, in particular the RF and XGBoost models, showed high performance in estimating the delay at signalized intersections under different traffic conditions. The results indicate that the delay estimation models based on the RF and XGBoost techniques can significantly contribute to both the literature and practice.
dc.description.sponsorshipNigde Omer Halisdemir University Scientific Research Projects Coordination Unit [MMT2019/6-BAGEP]
dc.description.sponsorshipThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research has been supported by the Nigde Omer Halisdemir University Scientific Research Projects Coordination Unit, Project No. MMT2019/6-BAGEP.
dc.identifier.doi10.1177/03611981211036874
dc.identifier.endpage126
dc.identifier.issn0361-1981
dc.identifier.issn2169-4052
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85118430078
dc.identifier.scopusqualityQ2
dc.identifier.startpage110
dc.identifier.urihttps://doi.org/10.1177/03611981211036874
dc.identifier.urihttps://hdl.handle.net/11480/14388
dc.identifier.volume2675
dc.identifier.wosWOS:000691042100001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSage Publications Inc
dc.relation.ispartofTransportation Research Record
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectSupport Vector Regression
dc.subjectRandom Forest
dc.subjectFuzzy-Logic
dc.subjectJunctions
dc.subjectModel
dc.titleVehicle Delay Estimation at Signalized Intersections Using Machine Learning Algorithms
dc.typeArticle

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