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:
  https://doi.org/10.1016/j.trpro.2017.03.007
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2352146517301412
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
• Supplemental Notes:
  • © 2017 Sehnaz Cenani et al. Published by Elsevier B.V.
• Authors:
  • Cenani, Sehnaz
  • Arentze, Theo A
  • Timmermans, Harry J P
• 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
• Serial:
  • Transportation Research Procedia
  • Volume: 22
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2352-1465
  • Serial URL: http://www.sciencedirect.com/science/journal/23521465/
• 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 17 2017 4:49PM