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Macroscopic traffic flow modelling of large-scale freeway networks with field data verification: State-of-the-art review, benchmarking framework, and case studies using METANET

Wang Yibing, Yu Xianghua, Guo Jinqiu, Papamichail Ioannis, Papageorgiou Markos, Zhang Lihui, Hu Simon, Li Yongfu, Sun Jian

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URIhttp://purl.tuc.gr/dl/dias/B38ECD21-C0D9-4E6C-B588-D1CEC0EC0E63-
Identifierhttps://doi.org/10.1016/j.trc.2022.103904-
Identifierhttps://www.sciencedirect.com/science/article/pii/S0968090X22003175-
Languageen-
Extent43 pagesen
TitleMacroscopic traffic flow modelling of large-scale freeway networks with field data verification: State-of-the-art review, benchmarking framework, and case studies using METANETen
CreatorWang Yibingen
CreatorYu Xianghuaen
CreatorGuo Jinqiuen
CreatorPapamichail Ioannisen
CreatorΠαπαμιχαηλ Ιωαννηςel
CreatorPapageorgiou Markosen
CreatorΠαπαγεωργιου Μαρκοςel
CreatorZhang Lihuien
CreatorHu Simonen
CreatorLi Yongfuen
CreatorSun Jianen
PublisherElsevieren
Content SummaryMacroscopic traffic flow models are of paramount importance to traffic surveillance and control. Before their employments in applications, the models need to be calibrated and validated against real traffic data. The model calibration determines an optimal set of model parameters that minimizes the discrepancy between the modeling results and real traffic data. The model validation is furthermore performed to corroborate the accuracy of a calibrated model using data other than used for calibration. The model calibration aims to reflect traffic reality, while model validation focuses on the prediction of future traffic using calibrated models. This paper delivers a comprehensive review of state-of-the-art works on macroscopic model calibration and validation, proposes a benchmarking framework on traffic flow modeling, and has conducted a large number of case studies based on the framework using macroscopic traffic flow model METANET with respect to the urban expressway network in Shanghai. In comparison to previous works, quite more comprehensive results on model calibration have been presented in this paper, in consideration of congestion tracking, traffic flow inhomogeneity, capacity drop, stop-and-go waves, scattering, adverse weather conditions, and accidents. The paper has also reported many results of model validation with respect to the same field examples. The results demonstrate that METANET is able to model complex traffic flow dynamics in large-scale freeway networks with sufficient accuracy. The paper is closed with discussion on limitations and future works.en
Type of ItemΑνασκόπησηel
Type of ItemReviewen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2024-01-18-
Date of Publication2022-
SubjectFreeway traffic flow model calibration and validationen
SubjectCongestion trackingen
SubjectTraffic flow inhomogeneityen
SubjectWeather conditionsen
SubjectAccidentsen
SubjectCapacity dropen
Bibliographic CitationY. Wang, X. Yu, J. Guo, I. Papamichail, M. Papageorgiou, L. Zhang, S. Hu, Y. Li, and J. Sun, “Macroscopic traffic flow modelling of large-scale freeway networks with field data verification: State-of-the-art review, benchmarking framework, and case studies using METANET,” Transp. Res. Part C Emerging Technol., vol. 145, Dec. 2022, doi: 10.1016/j.trc.2022.103904.en

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