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Donnelly & Koppelman

Donnelly & Koppelman. Travel Model Credibility as a Criteria for Design and Implementation. TRB Transportation Planning Applications Conference Columbus OH | 7 May 2013. Rick Donnelly – 28 years experience Senior Modeler at PB and Senior Fellow at University of Melbourne

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Donnelly & Koppelman

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  1. Donnelly & Koppelman Travel Model Credibilityas a Criteria for Designand Implementation TRB Transportation Planning Applications ConferenceColumbus OH | 7 May 2013
  2. Rick Donnelly – 28 years experience Senior Modeler at PB and Senior Fellow at University of Melbourne Co-chair, TRB Committee on TravelForecasting Resources (ADB45) Handful of papers, books, and presentations Frank Koppelman – 38 years experience Professor emeritus, Northwestern University Emeritus member, TRB Committee on Travel Demand Forecasting (ADB40) Hundreds of papers, books, and presentations
  3. Research questions Do high-level decision-makers view model validity different than we do? If so, what can we learn from them? Is there any difference between the views held by public and private sector executives?
  4. How we view model validity Entirely a statistical exercise Heavy focus on matching observed traffic counts Starts when development is over Matching preconceived ideas about accuracy Comparison with competing models DOT guidelines Accepted norms
  5. “Better”?
  6. Data points Investment bankers (3) State legislators (3) State DOT directors (2) HSR board member HSR executive director Governor State Transportation Commissioner (2) Congressional legal counsel CEO of infrastructure consulting firm
  7. Data collection Two questions: “How do you assess the validity of a travel model or forecast derived from one?” “What could forecasters do to increase your confidence in their work?” Administration: Relaxed social setting After interacting with forecasts No advance warning Ad hoc analysis
  8. Assessing validity (Q1)
  9. Assessing validity (Q1)
  10. But do they really believe us? Unexpected changes in exogenous information Optimism bias Poor data Political influences Methodological issues “Irrational behavior” Deception B Flyvbjerg, N Bruzelius, & W Rothengatter, Megaprojects and risk: an anatomy of ambition, Cambridge University Press (2003)
  11. While we’re on the topic of the future…
  12. Solutions (Q2) Lose “replication tunnel vision” Making risk and uncertainty explicit Standards Transparency Delphi panel Open models (data + assumptions + tools) “Stakeholder access” Reference class forecasting (outside view) Independent review Crash testing Model alignment
  13. </story> Contacts Rick Donnelly [donnellyr@pbworld.com] Frank Koppelman [fkoppel@koppelman.pro]
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