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
•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 pr...
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| Published in: | Transportation research. Part C, Emerging technologies Vol. 145; p. 103904 |
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| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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01.12.2022
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| ISSN: | 0968-090X, 1879-2359 |
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| Abstract | •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.
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. |
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| AbstractList | •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.
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. |
| ArticleNumber | 103904 |
| Author | Zhang, Lihui Yu, Xianghua Guo, Jinqiu Li, Yongfu Papamichail, Ioannis Papageorgiou, Markos Wang, Yibing Hu, Simon Sun, Jian |
| Author_xml | – sequence: 1 givenname: Yibing surname: Wang fullname: Wang, Yibing email: wangyibing@zju.edu.cn organization: Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou 310058, China – sequence: 2 givenname: Xianghua surname: Yu fullname: Yu, Xianghua organization: Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou 310058, China – sequence: 3 givenname: Jinqiu surname: Guo fullname: Guo, Jinqiu email: guojingqiu@hotmail.com organization: Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China – sequence: 4 givenname: Ioannis surname: Papamichail fullname: Papamichail, Ioannis organization: Dynamic Systems and Simulation Laboratory, Technical University of Crete, Chania 73100, Greece – sequence: 5 givenname: Markos surname: Papageorgiou fullname: Papageorgiou, Markos organization: Dynamic Systems and Simulation Laboratory, Technical University of Crete, Chania 73100, Greece – sequence: 6 givenname: Lihui surname: Zhang fullname: Zhang, Lihui organization: Institute of Intelligent Transportation Systems, Zhejiang University, Hangzhou 310058, China – sequence: 7 givenname: Simon surname: Hu fullname: Hu, Simon organization: Zhejiang University/University of Illinois at Urbana-Champaign Institute (ZJU-UIUC Institute), Haining, 314400, China – sequence: 8 givenname: Yongfu surname: Li fullname: Li, Yongfu organization: Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing, College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China – sequence: 9 givenname: Jian surname: Sun fullname: Sun, Jian organization: Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China |
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| Keywords | Freeway traffic flow model calibration and validation Traffic flow inhomogeneity Capacity drop Congestion tracking Weather conditions Accidents |
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| SubjectTerms | Accidents Capacity drop Congestion tracking Freeway traffic flow model calibration and validation Traffic flow inhomogeneity Weather conditions |
| Title | 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 |
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