1. Robust Estimation Techniques for Trip Generation in Tennessee
Jason Chen; Sumit Bindra; Vince Bernardin
Presented in May, 2017 at TRB Applications Conference

2. Background

3. Overview
How are they different from traditional trip generation models?
Forms of the variables
Special variables tested
How we compared various model specifications

4. Home-Based Models

5. Dataset
10,343 households, 23,026 persons and 81,061 individual trips

6. Traditional Trip Generation Models
Cross-classification Trip Tables
Number of variables restricted
Possibly too few observations
OLS Regression
Skewness of trips
Negative predictions
Heteroscedasticity

7. Generalized Linear Models (GLM)
Alternative GLM approach for TSTM
Home Based Work (HBW)
Home Based Other (HBO)
Non-home Based(NHB)
Generalized linear model with Poisson distribution
Generalized linear model with Neg-Binomial distribution

8. Advantages of GLM Approach
Poisson/Negative-Binomial Regression Model
Good with count data with minimum value close to 0
No negative trips
Skewed model with long right-tail
Do not require the response variable to have a normal error distribution
Can include as many explanatory variables as needed
Vincent Zoonekynd, Creative Commons License

9. Explanatory Variables
Continuous vs Factors
HH Size (Factor or Continuous)
Number of Workers (Factor or Continuous)
Vehicle Count (Factor or Continuous)
Income Category (Factor)
Etc.
Interaction Terms
Vehicles per person
Vehicles per adult

10. And MORE Variables … Lifecycle / Lifestyle Indicators Has Children
Has Seniors
Has both Children and Seniors
Built Environment
Employment and Population Density
Accessibility to Destinations

11. Variables Correlation Matrix

12. Vehicle count Number of vehicle
Variables Selection
Vehicle count Number of vehicle
Ln (Number of vehicle)
Household size
Children
Elderly
# of non-workers
Pop density
Emp density
Access to retail
etc.
# of workers

13. Model Comparison
How do we decide which model is better than the other?
Test various forms of the same or similar variable without including all
Split the data in 90 / 10 training and testing data sets. Final decision was based on:
Minimum error in the rotating hold out sample
RMSE and AIC values
Professional judgment

14. Log of Vehicle per Person No. of Non workers >= 2
Variable
Var as Factors
Variable Name
Var as Cont
Intercept
0.267
0.035
No. of Vehicle = 2
0.281
No. of Vehicles
0.068
No. of Vehicle = 3
0.450
No. of Children
0.096
No. of Vehicle >= 4
0.708
No. of Workers
0.404
HH Size = 2
0.050
Activity Diversity
0.265
HH Size = 3
0.115
HH has Senior = TRUE
-0.178
HH Size >= 4
0.246
HH has Child = TRUE
-0.125
No. of Workers >=2
0.416
HH_w_Child_Senior1
0.264
0.269
Vehicles per Adult
-0.331
No. of Non workers = 1
-0.127
Log of Vehicle per Person
0.311
No. of Non workers >= 2
-0.413
-0.144
-0.167
HH has both Child and Senior = TRUE
0.287
Vehicles per person
0.298
Vehicles per adult
-0.560

15. Final HBW Model Variables Final HBO Model Variables Intercept
No. of Vehicle = 1
No. of Vehicle = 2
No. of Vehicle = 3
No. of Vehicle >= 4
No. of Workers = 2
No. of Workers = 1
No. of Workers >= 3
HH has Child = TRUE   
HH has Senior = TRUE
HH income bet. 25k and 50k
HH has both Child and Senior = TRUE
HH income bet. 50k and 75k
Activity Diversity in home TAZ
HH income bet. 75k and 100k
HH income over 100k
Near accessibility in home TAZ
HH size > 2 with a senior = TRUE

16. Non-Home-Based Models

17. Linked NHB Trips Advanced Trip-based Framework
NHB trips after and dependent on HB trips
Makes NHB destinations, modes, etc., more consistent with HB
Makes response properties more reasonable

18. NHB Trip Models 𝑌= 𝛼 𝐴 𝛾 𝛽𝑥
NHB trips as a function of HB trip making & accessibility
𝑌= 𝛼 𝐴 𝛾 𝛽𝑥
Model Form
Y: NHB trips
A: is nearby accessibility
x: are HB attractions
Alpha is the scaling factor, Beta and Gamma are parameters

19. Sequential Estimation
Y = Ax estimated in two steps:
1: Assume alpha = 1 and gamma = 0 to estimate betas
2: Hold betas constant to estimate the values of alpha and gamma from -- ln(Y) – ln(x) = lnA + ln
STEP 1
STEP 2
Variable
Coefficients
HBW_SOV
0.281
HBW_HOV
0.450
HBO_SOV
0.708
HBO_HOV
0.050
Variable
Coefficients
Gamma
0.18 (0.03)
Log(alpha)
0.62 (0.06)
Alpha
1.86

20. NHB Trip Making & Accessibility

21. Conclusions

22. Take Aways There are better generation models GLMs offer
Better statistical efficiency
More explanatory power and sensitivity to additional factors (urban form, etc.)
Linked NHB trip generation
Is as easy as traditional methods
Can provide better consistency
Can incorporate accessibility effects

23. Contacts www.rsginc.com Jason Chen jason.chen@rsginc.com 802.359.6431
CONSULTANT
Vince Bernardin, PhD
DIRECTOR