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Critical Issues in Estimating and Applying Nested Logit Mode Choice Models

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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Critical Issues in Estimating and Applying Nested Logit Mode Choice Models

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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

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