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Estimating a mixed-profile MDCEV: case of daily activity type and duration

Shamshiripour, A ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1080/19427867.2017.1337266
  3. Publisher: Taylor and Francis Ltd , 2019
  4. Abstract:
  5. Multiple Discrete Continuous Extreme Value (MDCEV) has become popular in the past years. Yet, the model suffers from an ‘empirical identification’ issue that is mainly due to inter-relations between two of its parameters, α and γ. This paper presents a hybrid optimization paradigm (named HELPME) to address this issue in a basic MDCEV formulation and take full advantage of the model by estimating a ‘mixed-profile.’ HELPME benefits from a coarse-to-fine search strategy, in which a customized Electromagnetism-like meta-heuristic precedes a gradient-based approach. The Atlanta Regional Travel Survey (2011) is used to empirically analyze performance of HELPME as well as significance of the accuracy gap between the mixed-profile, and α and γ profiles. As part of the results, it is observed that in-sample fit is significantly improved, percentage error of out-of-sample prediction is reduced up to 97% in a 90% confidence level, and bias of out-of-sample predictions are reduced up to 67%. © 2017, © 2017 Informa UK Limited, trading as Taylor & Francis Group
  6. Keywords:
  7. Electromagnetism ; HELPME ; Maximum-likelihood estimation ; MDCEV ; Civil engineering ; Transportation ; Coarse-to-fine searches ; Confidence levels ; Daily activity ; Discrete-continuous extreme values ; Hybrid optimization ; Percentage error ; Maximum likelihood estimation
  8. Source: Transportation Letters ; Volume 11, Issue 6 , 2019 , Pages 289-302 ; 19427867 (ISSN)
  9. URL: https://www.tandfonline.com/doi/abs/10.1080/19427867.2017.1337266?journalCode=ytrl20