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Outline. IntroductionMotivationNon-uniqueness in model estimationChoice of utility scaling methodNumerical exampleConclusionReferences. Introduction. Nested Logit (NL): popular for mode choiceCaptures unobserved shared effects across modesRequires estimation from disaggregate dataUnknowns:
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1. Critical Issues in Estimating and Applying Nested Logit Mode Choice Models Ramachandran Balakrishna
Srinivasan Sundaram
Caliper Corporation
12th TRB National Transportation Planning Applications Conference, Houston, Texas
19th May, 2009
2. Outline Introduction
Motivation
Non-uniqueness in model estimation
Choice of utility scaling method
Numerical example
Conclusion
References
3. Introduction
4. Motivation: Highlight critical NL issues
5. Non-Uniqueness in Model Estimation (I)
6. Non-Uniqueness in Model Estimation (II) Model selection checks and guidelines
Final log-likelihood need not be only criterion
Coefficient magnitudes, signs
Relevant ratios (e.g. value of time)
Elasticities (within and across nests)
Must re-estimate with various starting thetas
Pick the best possible model
Detailed multi-dimensional search
One option: grid search
Implemented in TransCAD 5.0
7. Utility Scaling Basic NL formulation
q effects built into utilities
Difficult to compare utilities across nests
Counter-intuitive direct, cross elasticities
Inconsistent with utility maximization
Solution: scale utilities to remove q effects
Two scaling approaches
8. Utility Scaling Methods (I)
9. Utility Scaling Methods (II)
10. Utility Scaling Methods (III) Choice of scaling method impacts mode shares
Identical only for models with two levels of nests
Estimation
Utility maximization requires scaling by parent q
Model application
Critical to know how model was estimated!
TransCAD 5.0
Estimation options: no scaling, scale by parent q
Application options: all three methods
11. Numerical Example (I)
12. Numerical Example (II)
13. Numerical Example (III)
14. Numerical Example (IV)
15. Numerical Example (V)
16. Conclusion
17. References