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Incorporating Uncertainty Analysis into Forecasting

This document explores the importance of incorporating uncertainty analysis into travel forecasting techniques, as discussed in the AMPO Travel Modeling Work Group meeting held in Chicago on October 1, 2009. It highlights a new framework that focuses on deriving insights rather than just model mechanics, providing a range of possible outcomes to improve quality control and support decision-making. Practical examples, including the Honolulu Rail Study, demonstrate the significance of predicted versus actual outcomes, emphasizing the necessity for honest presentation of forecasts and the impact of varied demographic and network inputs.

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Incorporating Uncertainty Analysis into Forecasting

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  1. Incorporating Uncertainty Analysis into Forecasting Group Discussion AMPO Travel Modeling Work Group Chicago, Illinois October 1, 2009

  2. Why Bother? A new way of thinking about forecasting Insights, not just model mechanics Range of possible outcomes Another approach for quality control checks Information for decision makers Upper/lower bound, most likely Honesty in presentation of forecasts Predicted-versus-actual outcomes AMPO Travel Modeling Work Group Meeting 2

  3. Example: Forecast Build-up • Series of forecasts for: • Today • Plus future transit network • Plus new transit behaviors • Plus future trip tables • Plus future highway congestion • Plus future parking costs • Plus alternative land use (?) Choice riders Park/ride etc. Guideway effects AMPO Travel Modeling Work Group Meeting

  4. Example: Honolulu Rail Study Notes: - Transit demand ’05a is the 2005 on-board survey. - Bus speeds ’05+ are based on highway speeds from the assignment of 2005 person trips onto the 2030 highway network. - Highway speeds ’30- are from the assignment of 2030 person trips onto the 2005 highway network. AMPO Travel Modeling Work Group Meeting

  5. Example Specifications AMPO Travel Modeling Work Group Meeting

  6. Discussion Points Model documentation Assessment of model plausibility What it does/does not know well Markets, modes, behaviors Results of forecast tests Consistency with similar projects Key drivers of the forecasts Demographic/network inputs Alternative assumptions AMPO Travel Modeling Work Group Meeting 6

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