Journal article
Bi-scale car-following model calibration based on corridor-level trajectory
Transportation research. Part E, Logistics and transportation review, v 186, p103497
01 Jun 2024
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The precise estimation of macroscopic traffic parameters, such as travel time and fuel consumption, is essential for the optimization of traffic management systems. Despite its importance, the comprehensive acquisition of vehicle trajectory data for the calculation of these macroscopic measures presents a challenge. To bridge this gap, this study aims to calibrate car-following models capable of predicting both microscopic measures and macroscopic measures. We conduct a numerical analysis to trace the cumulative process of model prediction errors across various measurements, and our findings indicate that macroscopic measures encapsulate the accumulation of model errors. By incorporating macroscopic measures into vehicle model calibration, we can mitigate the impact of noise on microscopic data measurements. We compare three car-following model calibration methods: MiC (using microscopic measurements), MaC (using macroscopic measurements), and BiC (using both microscopic and macroscopic measurements) - utilizing real -world trajectory data. The BiC method emerges as the most successful in reconstructing vehicle trajectories and accurately estimating travel time and fuel consumption, whereas the MiC method leads to overfitting and inaccurate macro-measurement predictions. This study underscores the importance of bi-scale calibration for precise traffic and energy consumption predictions, laying the groundwork for future research aimed at enhancing traffic management strategies.
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Details
- Title
- Bi-scale car-following model calibration based on corridor-level trajectory
- Creators
- Keke Long - University of Wisconsin–MadisonHaotian Shi - University of Wisconsin–MadisonZhiwei Chen - Drexel UniversityZhaohui Liang - University of Wisconsin–MadisonXiaopeng Li - University of Wisconsin–MadisonFelipe de Souza - Argonne Natl Lab, 9700 S Cass Ave, Lemont, IL 60439 USA
- Publication Details
- Transportation research. Part E, Logistics and transportation review, v 186, p103497
- Publisher
- Elsevier
- Number of pages
- 17
- Grant note
- DE-AC02-06CH11357 / U.S. Department of Energy Office of Science; United States Department of Energy (DOE) U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO); United States Department of Energy (DOE)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:001238111300001
- Scopus ID
- 2-s2.0-85192207371
- Other Identifier
- 991021889911404721
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Economics
- Engineering, Civil
- Operations Research & Management Science
- Transportation
- Transportation Science & Technology