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

2 Acknowledgement & DisclaimerThis 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 UncertaintyBenefits 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 UncertaintyHistorical / Retrospective Analytic Univariate sensitivity analyses Decomposition/Incremental Scenario testing Response surface simulation

6 Overview

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

8 Response Surface Simulation StepsStep 2: Design and Conduct Experiments Experimental Design Many Travel Model Runs

9 Response Surface Simulation StepsStep 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 stepsModel Parameters Forecasting Model Future Conditions Response Surface Analysis Model review Historical Data 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 TransCADAround 450K population

13 Step 1: Quantifying the Uncertainties

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

15 Probability Distribution for Land UseThree 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 TelecommutingCensus/ACS/SIPP Annual Growth 2045 Telecommuting 𝑃𝑐𝑡 2045 = 𝑃𝑐𝑡 2010 ∗ 1+ 𝐺 10−11 ∗ 1+ 𝐺 11−12 ∗ 1+ 𝐺 12−13 ∗…(1+ 𝐺 44−45 )

18 Probability Distribution for Fuel Cost1994 to 2014 retail gasoline 2045 distribution of fuel costs was simulated by growing the 2010 prices

19 Probability distribution for Transit FarePolicy driven Weak relationship with economic factors Assumed to be discrete distribution

20 Step 2: Design and Conduct Experiments

21 Computational ExperimentsTwo Component Steps Experimental Design Run Travel Demand Forecasting Models

22 Experimental Design Key sources of uncertainty in two case studiesLand Use Telecommuting Parking Cost Transit Fare Fuel Cost Generational Modal Preferences Influencing factors for output 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 ModelingTwo 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 VMTModel Trip Based Model (Toledo) Activity Based Model (Chattanooga) Coefficients Beta Std. Error Constant Auto Cost 5343.5 6312.8 Telecommute 6765.1 Transit Cost Parking Cost 2793.4 6132.7 -612.0 7188.1 Non Auto Preference Urban Population Growth 2074.9 Urban Employment Growth 2271.5 Halo Zones Population Growth 6028.0 8587.5 2035.7 Boom City Halo Population Growth Square 878.7 350.3 Adjusted R Square 0.98 0.89

27 Monte Carlo Simulation

28 Output Probability Distribution: VMT

29 Final Thoughts

30 Translating Analysis into InsightsUnderstanding the Impact of Uncertainties A few things (growth, particularly suburban) may drive uncertainty in outcomes (VMT) Other things may have limited impact (on VMT)

31 Conclusions Uncertainties inherent in demand forecastsDemand 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 [email protected] 802.359.6431 Vince Bernardin, PhDCONSULTANT Vince Bernardin, PhD DIRECTOR