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. (December 2022)

• Record Type: Journal Article
• Authors: Wang, Yibing; Yu, Xianghua; Guo, Jinqiu; Papamichail, Ioannis; Papageorgiou, Markos; Zhang, Lihui; Hu, Simon; Li, Yongfu; Sun, Jian
• Is Part Of: Transportation research. Volume 145(2022)
• Publication Date: 2022-12
• Subjects: Freeway traffic flow model calibration and validation -- Congestion tracking -- Traffic flow inhomogeneity -- Weather conditions -- Accidents -- Capacity drop; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2022.103904
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: This work focuses on macroscopic traffic flow model calibration and validation of large freeway networks. An intensive literature review is presented on the subject to determine the strengths and weaknesses of various technical paths, and figure out a viable roadmap for future studies. The paper proposes a benchmarking framework concerning some of the key factors about macroscopic traffic flow model calibration and validation, which including congestion tracking, traffic flow inhomogeneity, adverse weather conditions and accidents, capacity drop, scattering, hysteresis, stop-and-go waves, and traffic heterogeneity. The paper presents comprehensive results of model calibration and validation concerning key factors included in the benchmarking framework as stated above. Works of the same focus were not reported before. Abstract: Macroscopic 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.