Calibration and validation of person-based trip production models of optional trip purposes

Jihye Kim, Ikki Kim and Jaeyeob Shim

Transportation Planning and Technology, 2021, vol. 44, issue 5, 470-486

Abstract: Trip generation models are typically calibrated using zone-based aggregated data, despite some theoretical drawbacks pertaining to the use of aggregated data. This study demonstrates that models calibrated with disaggregated data not only have a better theoretical basis, but also forecast more accurate trip productions. In particular, optional trip purposes are focused on the calibration of trip production models, of which trips tend to be irregularly generated, rendering it difficult to build more reliable models. There are three models calibrated: an aggregate model with zone-level data, a disaggregate model with household-level data, and with household and person-level data using household survey data for two years. The prediction accuracy and stability of the models are compared and verified using statistical methods. It is discovered that calibrating a trip production model using household and person-level data jointly yields a better calibration method.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03081060.2021.1927302 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:transp:v:44:y:2021:i:5:p:470-486

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GTPT20

DOI: 10.1080/03081060.2021.1927302

Access Statistics for this article

Transportation Planning and Technology is currently edited by Dr. David Gillingwater

More articles in Transportation Planning and Technology from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().