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Title:

Estimating Generalized Extreme Value Models with Targeted Sampling

Accession Number:

01593344

Record Type:

Component

Abstract:

Endogenously stratified sampling can lead to difficulties in estimating parameters for generalized extreme value models. This endogeneity can be created through choice-based samples (e.g., from an intercept survey) and through targeted samples that stratify based on closely related choices (e.g. recruiting entire households to be surveyed based on a mode choice of one household member on one trip of a prior day). The weighted conditional maximum likelihood estimator can be used for choice-based samples, and the authors propose an extension to this tool so that it can be used for targeted samples as well as choice-based samples. The authors then demonstrate an application of this estimator for a home-based work mode choice model for the Thurston Regional Planning Council, in Washington state.

Supplemental Notes:

This paper was sponsored by TRB committee ADB40 Standing Committee on Transportation Demand Forecasting.

Monograph Accession #:

01584066

Report/Paper Numbers:

16-3141

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Newman, Jeffrey
Ayvalik, Cemal
Scott, Clyde
Black, Thera
Grimes, Aaron

Pagination:

11p

Publication Date:

2016

Conference:

Transportation Research Board 95th Annual Meeting

Location: Washington DC, United States
Date: 2016-1-10 to 2016-1-14
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Geographic Terms:

Washington (State)

Subject Areas:

Planning and Forecasting; Transportation (General)

Source Data:

Transportation Research Board Annual Meeting 2016 Paper #16-3141

Files:

TRIS, TRB, ATRI

Created Date:

Jan 12 2016 5:24PM