A Spatial Learning Model for the Micro-Simulation of Travel Dynamics

The objective of this project is to develop and calibrate a computational process model of spatial learning through navigation, for the micro-simulation of travel dynamics where individual travelers' decision-making is simulated. Travel decisions are usually made in large spatial environment, and therefore spatial knowledge is an important moderator in the decision-making process. Prior research in environmental psychology, geography and artificial intelligence has shown that spatial knowledge is usually incomplete, distorted, and idiosyncratic depending on personal experience. The assumption of complete and precise spatial knowledge in all current travel micro-simulation models is thus problematic. This project aims at closing the gap between theory and practice by enhancing and calibrating an existing computational process model of spatial learning (Gopal et al., 1989) using smartphone tracking data over a multi-month period. The model, once incorporated in an overall travel micro-simulation framework, can potentially improve the realism and policy sensitivity of the simulation.

Language

  • English

Project

  • Status: Completed
  • Funding: $120073.00
  • Contract Numbers:

    DTRT12-G-UTC01

    UMAR24-19

  • Sponsor Organizations:

    Research and Innovative Technology Administration

    Department of Transportation
    1200 New Jersey Avneue, SE
    Washington, DC  United States  20590
  • Performing Organizations:

    New England University Transportation Center

    Massachusetts Institute of Technology
    77 Massachusetts Avenue, Room 40-279
    Cambridge, MA  United States  01239
  • Start Date: 20120101
  • Expected Completion Date: 0
  • Actual Completion Date: 20160131
  • Source Data: RiP Project 33378

Subject/Index Terms

Filing Info

  • Accession Number: 01489791
  • Record Type: Research project
  • Source Agency: New England University Transportation Center
  • Contract Numbers: DTRT12-G-UTC01, UMAR24-19
  • Files: UTC, RiP
  • Created Date: Aug 15 2013 1:01AM