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

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
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 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
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
Census/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 Cost
1994 to 2014 retail gasoline
2045 distribution of fuel costs was simulated by growing the 2010 prices

19. Probability distribution for 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 Key sources of uncertainty in two case studies
Land 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 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
Model
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 Insights
Understanding 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 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 jason.chen@rsginc.com 802.359.6431 Vince Bernardin, PhD
CONSULTANT
Vince Bernardin, PhD
DIRECTOR