Normalization of network generalized extreme value models

Jeffrey P. Newman

Transportation Research Part B: Methodological, 2008, vol. 42, issue 10, 958-969

Abstract: Generalized extreme value (GEV) models provide a convenient way to model choice behavior that is consistent with utility maximization theory, but the development of specific new models within the GEV family has been slow, due to the difficulty of ensuring new formulations comply with all the GEV rules. The network GEV structure introduced by Daly and Bierlaire [Daly, A., Bierlaire, M., 2006. A general and operational representation of generalised extreme value models. Transportation Research Part B 40, 285-305] provides a tool to quickly generate new models in the GEV family, without the burden of complex analysis of the new model to ensure its properties. This paper further develops the network GEV tool, describing several methodologies for correctly normalizing the allocation parameters in such models, to ensure unbiasedness. These methods vary depending on the structure of the underlying network.

Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0191-2615(08)00043-X
Full text for ScienceDirect subscribers only

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:eee:transb:v:42:y:2008:i:10:p:958-969

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
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 Catherine Liu ().