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Title:

Computational methods for estimating multinomial, nested, and cross-nested logit models that account for semi-aggregate data
Cover of Computational methods for estimating multinomial, nested, and cross-nested logit models that account for semi-aggregate data

Accession Number:

01622490

Record Type:

Component

Abstract:

The authors present a summary of important computational issues and opportunities that arise from the use of semi-aggregate data (where the explanatory data for choice scenarios are not necessarily unique for each decision-maker) in discrete choice models. This data is encountered with large transactional databases that have limited consumer information, a common feature in some transportation planning applications, such as airline itinerary choice modeling. The authors developed a freeware software package called Larch, written in Python and C++, to take advantage of this kind of data to greatly speed the estimation of discrete choice model parameters. Benchmarking experiments against Stata (a commonly used commercial package) and Biogeme (a commonly used freeware package) based on an industry dataset for airline itinerary choice modeling applications shows that the size of the input estimation files are 50 to 100 times larger in Stata and Biogeme, respectively. Estimation times are also much faster in Larch; e.g., for a small itinerary choice problem, a multinomial logit model estimated in Larch converged in less than one second whereas the same model took almost 15 seconds in Stata and more than three minutes in Biogeme.

Supplemental Notes:

This paper was sponsored by TRB committee AV040 Standing Committee on Aviation Economics and Forecasting.

Monograph Accession #:

01618707

Report/Paper Numbers:

17-03448

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Newman, Jeffrey P
Lurkin, Virginie
Garrow, Laurie A

Pagination:

17p

Publication Date:

2017

Conference:

Transportation Research Board 96th Annual Meeting

Location: Washington DC, United States
Date: 2017-1-8 to 2017-1-12
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Subject Areas:

Aviation; Data and Information Technology; Planning and Forecasting

Source Data:

Transportation Research Board Annual Meeting 2017 Paper #17-03448

Files:

PRP, TRIS, TRB, ATRI

Created Date:

Dec 8 2016 11:18AM