When older and more recent datasets have large and small numbers of observations, respectively, then discrete choice modellers must decide whether to utilise both datasets with model updating (transfer scaling, joint context estimation, Bayesian updating, and combined transfer estimation) or only the more recent dataset. This study investigates the case when the data collection time points and the number of observations from each time point differ. Bootstrapping was applied to commuting mode choice models utilising datasets from Nagoya, Japan. The following criteria are proposed: (1) when the more recent time point has a large number of observations, use only the more recent data; (2) when the more recent time point has a smaller number of observations, use transfer scaling or joint context estimation based on the differences in the contexts of the two time points and the sample size from the older time point.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Criteria for selecting model updating methods for better temporal transferability


    Contributors:

    Published in:

    Publication date :

    2020-01-01


    Size :

    23 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown






    Effectiveness of Bayesian Updating Attributes in Data Transferability Applications

    Rashidi, Taha H. / Auld, Joshua / Mohammadian, Abolfazl (Kouros) | Transportation Research Record | 2013



    Spatial Transferability and Updating Analysis of Mode Choice Models in Developing Countries

    San Santoso, Djoen / Tsunokawa, Koji | Taylor & Francis Verlag | 2005