Cognitive Maps for Route Choice Modeling

 

External link: http://utc.mit.edu/uploads/UMAR24-20-FP.pdf

Publication date: August 24, 2015

Capsule summary: This report discusses the potential for incorporating cognitive maps into models of users’ route choices.

Author(s): Gao, Song

Publisher(s): New England University Transportation Center: Massachusetts Institute of Technology, Research and Innovative Technology Administration


Definition: Data analysis techniques for transportation planning
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Definition: Report published by a governmental body or other organization (e.g. FHWA, NAS, etc.)
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Description: Building on recent research and the current body of knowledge, this report explores cognitive maps in the context of route choice modeling. The research develops a methodology for accounting for individuals’ network perceptions through analysis of GPS taxi data from Stockholm and Sweden.

Objectives: The objective of this research is to address the underlying assumptions in travel behavior modeling that users hold homogeneous perceptions of available route choices. The research team developed modeling choice sets to include users who choose static routes, and those who spontaneously choose alternative routes for factors such as distance, cost-effectiveness, and traffic patterns.

Findings: The research team found that the choice sets based on users who altered their routes (“routing policy” users) generated data that more closely matched the observed data. The report demonstrates that the assumption that users follow the shortest available path limits the functionality of route choice models.

Recommendations: Based on the findings, the use of route policy choice sets in route choice modeling can advance the practice. The research is indicative of the value of incorporating behavioral research into models of travel behavior.

Research Theme: Innovative Data Analysis

Community Type:

Transportation Mode:

Planning Scope:

Organization Type:

Planning Subject: Analysis, Evaluation and Assessment, Forecasting, Planning Methods