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Quantify Uncertainty in Travel Forecasts

Quantify Uncertainty in Travel Forecasts. May, 2017. Jason Chen, RSG Vince Bernardin, RSG Thomas Adler, RSG Nikhil Sikka, RSG Steven Trevino, RSG Steve Tuttle, RSG. Acknowledgement & Disclaimer. This project was funded as part of the TMIP Toolbox. Disclaimer

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Quantify Uncertainty in Travel Forecasts

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  1. Quantify Uncertainty in Travel Forecasts May, 2017 Jason Chen, RSG Vince Bernardin, RSG Thomas Adler, RSG Nikhil Sikka, RSG Steven Trevino, RSG Steve Tuttle, RSG

  2. Acknowledgement & Disclaimer This project was funded as part of the TMIP Toolbox Disclaimer The views and opinions expressed in this presentation are those of the presenters and do not represent the official policy or position of FHWA and do not constitute an endorsement, recommendation, or specification by FHWA.

  3. Introduction

  4. Reasons for Quantifying Uncertainty Benefits of quantifying uncertainty Providing comprehensive results Evaluating the risk to key stakeholders Describing how changes to key assumption could affect the outcome Accounting for highly uncertain assumptions

  5. Methods for Quantifying Uncertainty Historical / Retrospective Analytic Univariate sensitivity analyses Decomposition/Incremental Scenario testing Response surface simulation

  6. Overview

  7. Response Surface Simulation Steps Step 1: Quantify the key uncertainties or risks Land use Cost Travel Preferences/Behavior ETC.

  8. Response Surface Simulation Steps Step 2: Design and Conduct Experiments Experimental Design Many Travel Model Runs

  9. Response Surface Simulation Steps Step 3: Estimate and Apply Response Surface Model in Simulation Regression model to connect model output with uncertain inputs Monte Carlo Simulation to produce probability distributions of performance measures from model outputs

  10. Response surface simulation steps Model Parameters Forecasting Model Future Conditions Response Surface Analysis Historical Data Model review Synthesized Model Probability Distribution of Model Parameters Probability Distribution of Future Conditions Monte Carlo Simulation Forecasting Distribution

  11. Case Study 1 Toledo, Ohio Trip based model Cube Around 600K population

  12. Case Study 2 Chattanooga, Tennessee Activity based model TransCAD Around 450K population

  13. Step 1: Quantifying the Uncertainties

  14. Step 1: Quantify the Key Uncertainties Sources of Uncertainty Generational Modal Preferences Telecommuting Parking cost Transit fare Fuel costs Land Use More …

  15. Probability Distribution for Land Use Land Use • Three mutually exclusive areas: • Urban core • Boom city • Halo area • Distinct scenarios: • Default 2045 from agency • High growth • Low growth • Medium growth • Boom city

  16. Probability Distribution for Land Use

  17. Probability Distribution for Telecommuting Telecommuting

  18. Probability Distribution for Fuel Cost Fuel Cost • 1994 to 2014 retail gasoline • 2045 distribution of fuel costs was simulated by growing the 2010 prices

  19. Probability distribution for Transit Fare Transit fare • Policy driven • Weak relationship with economic factors • Assumed to be discrete distribution

  20. Step 2: Design and Conduct Experiments

  21. Computational Experiments Two Component Steps Experimental Design Run Travel Demand Forecasting Models

  22. Experimental Design Influencing factors for output Key sources of uncertainty in two case studies Land Use Telecommuting Parking Cost Transit Fare Fuel Cost Generational Modal Preferences Urban Core Population Growth Urban Core Employment Growth Halo Area Population Growth Halo Area Employment Growth Boom City Growth Generational Modal Preferences Telecommuting Parking cost Transit fare Fuel cost

  23. Experimental Design 20 Orthogonal fractional factorial experiments

  24. Step 3: Response Surface Modeling

  25. Response Surface Modeling Two Component Steps Response Surface Model Estimation Linear regression in SPSS Monte Carlo Simulation Used Excel add-in Performance Measures of Interest Vehicle Miles Traveled (VMT) Vehicle Hours Traveled (VHT) / Delay Auto and Truck Emissions Transit Ridership

  26. Estimated Coefficients for VMT

  27. Monte Carlo Simulation

  28. Output Probability Distribution: VMT

  29. Final Thoughts

  30. Translating Analysis into Insights Understanding the Impact of Uncertainties A few things (growth, particularly suburban) may drive uncertainty in outcomes (VMT) Other things may havelimited impact (on VMT)

  31. Conclusions Uncertainties inherent in demand forecasts Demand models not efficient for simulating the probability distributions of demand Simple sensitivity analyses do not provide robust information Response surface models can effectively simulate risks associated with much more complex travel demand forecasting models

  32. Jason Chen CONSULTANT jason.chen@rsginc.com 802.359.6431 Vince Bernardin, PhD DIRECTOR vince.bernardin@rsginc.com 812.200.2351

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