A Bayesian Optimization Approach for Calibrating Large-Scale Activity-Based Transport Models

Addressing complexity in transportation in cases such as disruptive trends or disaggregated management strategies has become increasingly important. This in turn is resulting in the rising adoption of Agent-Based and Activity-Based modeling. Still, a broad adoption is hindered by the high complexity...

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Vydáno v:IEEE open journal of intelligent transportation systems Ročník 4; s. 740 - 754
Hlavní autoři: Agriesti, Serio, Kuzmanovski, Vladimir, Hollmen, Jaakko, Roncoli, Claudio, Nahmias-Biran, Bat-Hen
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Activity-based transport modeling
Algorithms
Bayes methods
Bayesian analysis
Bayesian optimization
Behavioral sciences
Calibration
Complexity
Computational modeling
Data models
machine learning
model calibration
Optimization
Parameters
surrogate model
Transportation
Transportation models
ISSN:2687-7813, 2687-7813
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Shrnutí:Addressing complexity in transportation in cases such as disruptive trends or disaggregated management strategies has become increasingly important. This in turn is resulting in the rising adoption of Agent-Based and Activity-Based modeling. Still, a broad adoption is hindered by the high complexity and computational needs. For example, hundreds of parameters are involved in the calibration of Activity-Based models focused on behavioral theory, to properly frame the required detailed socio-economical characteristics. To address this challenge, this paper presents a novel Bayesian Optimization approach that incorporates a surrogate model defined as an improved Random Forest to automate the calibration process of the behavioral parameters. The presented solution calibrates the largest set of parameters yet, according to the literature, by combining state-of-the-art methods. To the best of the authors' knowledge, this is the first work in which such a high dimensionality is tackled in sequential model-based algorithm configuration theory. The proposed method is tested in the city of Tallinn, Estonia, for which the calibration of 477 behavioral parameters is carried out. The calibration process results in a satisfactory performance for all the major indicators, the OD matrix average mismatch is equal to 15.92 vehicles per day while the error for the overall number of trips is equal to 4%.
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ISSN:2687-7813
2687-7813
DOI:10.1109/OJITS.2023.3321110