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A Bayesian Optimiza...
A Bayesian Optimization Approach for Calibrating Large-Scale Activity-Based Transport Models
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- Agriesti, Serio (författare)
- Aalto University, Espoo, Finland; Tallinn University of Technology Tallinn, Estonia
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- Kuzmanovski, Vladimir (författare)
- Aalto University, Espoo, Finland; Tallinn University of Technology Tallinn, Estonia
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- Hollmén, Jaakko (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap,Aalto University Espoo, Finland
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- Roncoli, Claudio (författare)
- Aalto University, Espoo, Finland
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- Nahmias-Biran, Bat-Hen (författare)
- Tel Aviv University Tel Aviv, Israel
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(creator_code:org_t)
- 2023
- 2023
- Engelska.
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Ingår i: ieee open journal of intelligent transportation systems. - 2687-7813. ; 4, s. 740-754
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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%.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)
Nyckelord
- Activity-based transport modeling
- model calibration
- machine learning
- Bayesian optimization
- surrogate model
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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