The initial condition problem with complete history dependency in learning models for travel choices
Yue Tang and
Transportation Research Part B: Methodological, 2018, vol. 117, issue PB, 850-861
Learning-based models that capture travelers’ day-to-day learning processes in repeated travel choices could benefit from ubiquitous sensors such as smartphones, which provide individual-level longitudinal data to help validate and improve such models. However, the common problem of missing initial observations in longitudinal data collection can lead to inconsistent estimates of perceived value of attributes in question, and thus inconsistent parameter estimates. In this paper, the stated problem is addressed by treating the missing observations as latent variables in an instance-based learning model that is estimated via maximum simulated likelihood (MSL). The MSL method is implemented in practice using random sampling and importance sampling. Monte Carlo experimentation based on synthetic data shows that both the MSL with random sampling (MSLrs) and MSL with importance sampling (MSLis) are effective in correcting for the endogeneity problem in that the percent error and empirical coverage of the estimators are greatly improved after the correction. Compared to the MSLrs method, the MSLis method is superior in both effectiveness and computational efficiency. Furthermore, MSLis passes a formal statistical test for the recovery of the population values up to a scale with a large number of missing observations, while MSLrs systematically fails due to the curse of dimensionality. The impacts of sampling size in MSLrs and number of high probability choice sequences in MSLis on the methods’ performances are investigated the methods are applied to an experimental route-choice dataset to demonstrate their empirical application. Hausman–McFadden tests show that the estimators after correction are statistically equal to the estimators of the full dataset without missing observations, confirming that the proposed methods are practical and effective for addressing the stated problem.
Keywords: Endogeneity; Initial condition problem; Learning model; Maximum simulated likelihood; Multiple imputation (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:transb:v:117:y:2018:i:pb:p:850-861
Ordering information: This journal article can be ordered from
https://shop.elsevie ... _01_ooc_1&version=01
Access Statistics for this article
Transportation Research Part B: Methodological is currently edited by Fred Mannering
More articles in Transportation Research Part B: Methodological from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().