Cognitive Learning Approach for Travel Demand Modeling: Estimation Results
The paper reports progress in the development of an agent-based model of cognitive learning, which simulates spatial perception updating in connection with daily travel behavior based on the principle of Bayesian perception updating. This model is embedded in a multi agent-based model of activity-travel scheduling and choice behavior. The aim of this paper is to empirically estimate the proposed model using data on individuals’ landmark recognition in a field survey. The main findings of the study show that the model fits the data satisfactorily and results are reasonable. The comparison between the proposed Bayesian model and a more basic binary logit model shows that the model improves when prior probabilities are taken into account, which provides evidence for the proposed Bayesian model.
- Record URL:
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
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Supplemental Notes:
- © 2017 Sehnaz Cenani et al. Published by Elsevier B.V.
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Authors:
- Cenani, Sehnaz
- Arentze, Theo A
- Timmermans, Harry J P
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Conference:
- 19th EURO Working Group on Transportation Meeting "Simulation and Optimization of Traffic and Transportation Systems", EWGT 2016
- Location: Istanbul , Turkey
- Date: 2016-9-5 to 2016-9-7
- Publication Date: 2017
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References;
- Pagination: pp 55-64
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Serial:
- Transportation Research Procedia
- Volume: 22
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 2352-1465
- Serial URL: http://www.sciencedirect.com/science/journal/23521465/
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Choice models; Cognition; Learning; Logits; Simulation; Traffic estimation; Travel behavior; Travel demand
- Uncontrolled Terms: Bayesian models
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01636093
- Record Type: Publication
- Files: TRIS
- Created Date: May 26 2017 11:30AM