A MICRO-SIMULATION MODEL SYSTEM OF DEPARTURE TIME USING A PERCEPTION UPDATING MODEL UNDER TRAVEL TIME UNCERTAINTY

This paper proposes a microscopic traffic model that considers departure time change as a possible response to congestion. It also includes a formal model of how travelers base their daily travel decisions on the accumulated experience gathered from repetitively traveling through the transportation network. In the proposed model, a micro-simulation approach is applied, in which individuals base their consecutive departure time decisions on a mental model. The mental model is the outcome of a continuous process of perception updating according to principles of reinforcement learning. Individuals' daily travel decisions are linked to a traffic simulator to create a simulation system in which both individual decision-making and system performance (and interactions between these two levels) are adequately represented. An application of the proposed model system to a small case study illustrates the potential of the model in predicting the responses to congestion. Although the learning and adaptation procedure appeared to be realistic in the sense that travelers gradually improved their perception of the travel circumstances, the simulation results suggest that the outcomes of the learning and adaptation process may not be straightforward.

• Record URL:
  http://www.sciencedirect.com/science/article/B6VG7-4F8TVWV-3/1/3769d755b63534bea375b2cf425f0aac
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09658564
• Corporate Authors:

  Elsevier

  The Boulevard, Langford Lane
  Kidlington, Oxford  United Kingdom  OX5 1GB
• Authors:
  • Ettema, D
  • Tamminga, G
  • Timmermans, H
  • Arentze, T
• Publication Date: 2005-5

Language

• English

Media Info

• Features: Figures; References; Tables;
• Pagination: p. 325-344
• Serial:
  • Transportation Research Part A: Policy and Practice
  • Volume: 39
  • Issue Number: 4
  • Publisher: Elsevier
  • ISSN: 0965-8564
  • Serial URL: http://www.sciencedirect.com/science/journal/09658564

Subject/Index Terms

• TRT Terms: Case studies; Decision making; Departure time; Machine learning; Microscopic traffic flow; Traffic congestion; Traffic models; Traffic simulation; Travel time; Uncertainty
• Subject Areas: Highways; Operations and Traffic Management; I71: Traffic Theory;

Filing Info

• Accession Number: 00989121
• Record Type: Publication
• Files: TRIS, ATRI
• Created Date: Apr 30 2005 12:00AM