Fusing Physics-Based and Data-Driven Models for Car-Following Modeling: A Particle Filter Approach
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 12
Abstract
Microscopic modeling of vehicle movements and interactions is pivotal in traffic flow theory. Physics-based car-following (CF) models using mathematical formulations can delineate driving behavior in various traffic conditions with decent interpretability. However, given predetermined mathematical forms, they might fail to characterize complex, highly nonlinear phenomena. Data-driven CF models naturally excel in this regard considering their flexible architectures, but their performance is subject to data quality, especially distribution bias. In this paper, we propose a novel physics-informed particle filter (PIPF) model that fuses and takes advantage of the two approaches. Utilizing the intelligent driver model as the physics-based model and the multioutput Gaussian process regression as the data-driven model, the PIPF model integrates and embeds both models into a particle filter framework, enhancing both model adaptability and accuracy. The performance of the proposed model is examined through both single vehicle and multivehicle numerical experiments using the NGSIM trajectory data set. Compared with physics-based and data-driven models alone, the PIPF model demonstrates a performance improvement in terms of the root mean square error of about 11.16% and 29.43% in scenarios characterized by sparse data and about 19.81% and 3.84% in scenarios with sufficient data. Compared to traditional particle filtering models, the number of particles to achieve optimal results is reduced by 20%, meaning less computational complexity.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study is supported by the Key Project of the National Natural Science Foundation of China (No. 52131203), the “Pandeng” Project of the Natural Science Foundation of Jiangsu Province (No. BK20232019), the Jiangsu Provincial Scientific Research Center of Applied Mathematics (No. BK20233002), and the Major Science and Technology Demonstration Project in Jiangsu Province (No. BE2022860). Yang Yang and Yang Zhang equally contributed to the work.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 12 • December 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Feb 9, 2024
Accepted: Jun 25, 2024
Published online: Sep 30, 2024
Published in print: Dec 1, 2024
Discussion open until: Feb 28, 2025
ASCE Technical Topics:
- Driver behavior
- Engineering fundamentals
- Engineering materials (by type)
- Environmental engineering
- Errors (statistics)
- Filters
- Filtration
- Infrastructure
- Materials engineering
- Mathematical models
- Mathematics
- Model accuracy
- Models (by type)
- Particles
- Statistics
- Traffic engineering
- Traffic models
- Transportation engineering
- Water treatment
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