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David Schmitt, AICP With very special thanks to Hongbo Chi

Garbage In…Garbage Out? Do Inaccurate Inputs Cause the Inaccuracy & Bias in Transit Demand Forecasts?. David Schmitt, AICP With very special thanks to Hongbo Chi. May 16, 2017. Overview. Optimistically biased forecasts fall below this line. Motivations.

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David Schmitt, AICP With very special thanks to Hongbo Chi

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  1. Garbage In…Garbage Out?Do Inaccurate Inputs Cause the Inaccuracy & Bias inTransit Demand Forecasts? David Schmitt, AICP With very special thanks to Hongbo Chi May 16, 2017

  2. Overview Optimistically biased forecasts fall below this line

  3. Motivations • Heard at transit forecaster gatherings... • "Our demand forecasts were wrong because the land use assumptions didn’t come true" • "Our forecasts are always too high because of those inputs they give us to use" • Implications: • Accurate inputs  accurate demand forecasts • Accurate inputs  unbiased demand forecasts • Model specification, validation or other issues are not problematic  Assess whether inaccurate & biased inputs resolve the inaccuracy & bias of demand forecasts Transit Forecasting Accuracy Database: Garbage In…Garbage Out?

  4. Methodology Begin with Transit Forecasting Accuracy Database: 66 projects, with forecasting inputs categorized by accuracy Process: • Quantify the level of inaccuracy for each input/assumption • Compute change in forecasted demand by apply elasticity to corrected assumption • Compute adjusted demand forecast • Compute the adjusted forecast accuracy ratio: • Ratio = actual / forecasted ridership • Ratio < 1.00, optimistically biased • Ratio > 1.00, conservatively biased Mean = 0.65 (n=66) Transit Forecasting Accuracy Database: Garbage In…Garbage Out?

  5. Quantifying Input Inaccuracy • Project service levels • Employment estimates • Project travel time • Population estimates • Project fare • Supporting transit network • Economic conditions • Competing transit network • Auto fuel price • Roadway congestion • Each of the 10 project inputs and exogenous forecasts are placed into 1 of 5 categories: • Also, any differences between the forecast and actual ridership year are adjusted using national historical ridership growth Transit Forecasting Accuracy Database: Garbage In…Garbage Out?

  6. Elasticities Shaded cells reflect selected values Transit Forecasting Accuracy Database: Garbage In…Garbage Out?

  7. Results • Average accuracy ratio: 0.65  0.74 (+14%) • Inaccurate inputs contribute to (0.35-0.26) / 0.35 = 26% of demand forecast inaccuracy Transit Forecasting Accuracy Database: Garbage In…Garbage Out?

  8. Test 1B • Weakness of methodology of Test 1: Exact values of elasticities & values of input inaccuracy are unknown • Perform additional test: randomly vary elasticities and input variability for 10,000 iterations Allowed to vary by ±15ppts Allowed to vary by ±0.3 Transit Forecasting Accuracy Database: Garbage In…Garbage Out?

  9. Mean: 33% Median: 31% Range: 9%, 78% AveDev: 8% Transit Forecasting Accuracy Database: Garbage In…Garbage Out?

  10. Test #2 • Hypothesis: inputs may explain ‘expansion’ project inaccuracy more than for ‘starter’ projects • Uncertain reactions to new modes lowers model’s ability to provide accurate demand forecasts  Input inaccuracies should more fully describe demand inaccuracy for ‘expansion’ projects • Projects divided into starter project (n=31) and expansion project (n=35) groups • Re-ran 10,000 simulations for each group allowing elasticities and input inaccuracy values to vary Transit Forecasting Accuracy Database: Garbage In…Garbage Out?

  11. Mean: 42% Median: 41% Range: 16%, 88% AveDev: 9% Transit Forecasting Accuracy Database: Garbage In…Garbage Out?

  12. Observations • No test confirmed the anecdotal explanations for demand forecast inaccuracy or bias • Input inaccuracies do not appear to explain demand forecast bias • Input inaccuracies ‘explain’ less than 50% of forecast demand inaccuracy • Evidence suggests that other causes of demand inaccuracy and bias exist • Knowledge of travel patterns on modes already in operation within the region seems to heighten the impact of inputs on demand forecast accuracy Transit Forecasting Accuracy Database: Garbage In…Garbage Out?

  13. Thank you! David Schmitt, AICP daves1997@gmail.com dschmitt@ctgconsult.com www.transportforecastaccuracy.com Transit Forecasting Accuracy Database: Garbage In…Garbage Out?

  14. References • Schmitt, David. “Beginning to Enjoy the 'Outside View': A First Glance at Transit Forecasting Uncertainty and Accuracy Using the Transit Forecasting Accuracy Database”. 2015. http://trbappcon.org/2015conf/presentations/143_2015-05-19%20Transit%20Forecasting%20Accuracy%20Database%20Summary%20v5%20-%20with%20script.pptx • Taleb, Nassim Nicholas. Fooled By Randomness: The Hidden Role of Chance in Life and in the Markets: Second Edition. Random House, 2005-08-23. iBooks. • Taleb, Nassim Nicholas. Antifragile: Things That Gain from Disorder. Random House, 2012-11-27. iBooks. • Taleb, Nassim Nicholas. The Black Swan: Second Edition. Random House Trade Paperbacks, 2010-05-11. iBooks. • Transportation Research Board. TCRP Report 95: Traveler Response to Transportation System Changes. 2004. • U.S. Department of Transportation: Federal Transit Administration. Before-and-After Studies of New Starts Projects [annual reports to Congress]. 2007-2016. • U.S. Department of Transportation: Federal Transit Administration. Predicted and Actual Impacts of New Starts Projects: Capital Cost, Operating Cost and Ridership Data. September 2003. • U.S. Department of Transportation: Federal Transit Administration. The Predicted and Actual Impacts of New Starts Projects - 2007: Capital Cost and Ridership. April 2008. • U.S. Department of Transportation: Transportation Systems Center. Urban Rail Transit Projects: Forecast Versus Actual Ridership and Costs. October 1989. • U.S. Department of Transportation: Federal Transit Administration. Travel Forecasting for New Starts: A Workshop Sponsored by the Federal Transit Administration. Phoenix and Tampa, 2009. • U.S. Department of Transportation: Travel Model Improvement Program Webinar: Shining a Light Inside the Black Box (Webinar I). February 14, 2008. • Victoria Transport Policy Institute. “Transit Price Elasticities and Cross-Elasticities”. 2004-2016. http://www.vtpi.org/tranelas.pdf. Transit Forecasting Accuracy Database: Garbage In…Garbage Out?

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