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
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.
- Publication:
-
Transportation Research Part C: Emerging Technologies
- Pub Date:
- December 2022
- DOI:
- 10.1016/j.trc.2022.103904
- Bibcode:
- 2022TRPC..14503904W
- Keywords:
-
- Freeway traffic flow model calibration and validation;
- Congestion tracking;
- Traffic flow inhomogeneity;
- Weather conditions;
- Accidents;
- Capacity drop