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Planners and policymakers are often faced with the need to make decisions about issues for which there is uncertainty and limited data. For example, transportation planners are now faced with the prospect that new transportation technologies such as autonomous vehicles could greatly alter future transportation system needs. Decisions about these types of issues are difficult to reason about and consequently are likely to be ignored or made on the basis of simplistic logic. Although modeling could be helpful, especially for issues involving complex systems, it is rarely used because models usually require large amounts of data and and handle uncertainty poorly.
This presentation is about how a fuzzy systems dynamic model (FSDM) may be used to model policy issues involving uncertainty and limited data. The FSDM is a type of fuzzy cognitive map (FCM) which is a directed graph that represents concepts of concern as nodes in the graph and causal relationships as edges.
The presentation will cover:
- Background on FCMs and their usefulness for modeling issues involving uncertainty;
- The mathematical formulation of an FSDM and how it differs from common FCM models;
- Open source software for building and running an FSDM; and
- Results of research with ODOT and OSU on modeling the potential effects of new transportation technologies and services using an FSDM.
Brian Gregor has over 35 years of experience in transportation and land use analysis, planning, and policy development in Oregon. He has developed several innovative models including the GreenSTEP model. Since retiring from the Oregon DOT, he operates his own consulting firm, Oregon Systems Analytics, which specializes in the development of strategic planning models to address transportation, land use, and related environmental issues.
System analysis, Fuzzy systems, Decision making, Technology -- Management
Transportation Engineering | Urban Studies and Planning
Gregor, Brian, "Using Fuzzy Cognitive Maps to Model Policy Issues in the face of Uncertainty and Limited Data" (2017). TREC Friday Seminar Series. 113.