01151502

Component

https://doi.org/10.3141/2136-04

http://scholar.google.com...ring&publication_year=2009

Statisticians along with other scientists have made significant computational advances that enable the estimation of formerly complex statistical models. The Bayesian inference framework combined with Markov chain Monte Carlo estimation methods such as the Gibbs sampler enable the estimation of discrete choice models such as the multinomial logit (MNL) model. MNL models are frequently applied in transportation research to model choice outcomes such as mode, destination, or route choices or to model categorical outcomes such as crash outcomes. Recent developments allow for the modification of the potentially limiting assumptions of MNL such as the independence from irrelevant alternatives (IIA) property. However, relatively little transportation-related research has focused on Bayesian MNL models, the tractability of which is of great value to researchers and practitioners alike. This paper addresses MNL model specification issues in the Bayesian framework, such as the value of including prior information on parameters, allowing for nonlinear covariate effects, and extensions to random parameter models, so changing the usual limiting IIA assumption. This paper also provides an example that demonstrates, using route-choice data, the considerable potential of the Bayesian MNL approach with many transportation applications. This paper then concludes with a discussion of the pros and cons of this Bayesian approach and identifies when its application is worthwhile.

01147883

09-3389

English

pp 28-36

2009

Transportation Research Record: Journal of the Transportation Research Board

9780309142656

Print

References (50) ; Tables (5)

Choice models; Multinomial logits; Route choice

Bayesian approach; Bayesian multinomial logits

Data and Information Technology; Highways; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Mar 3 2010 2:24PM

• Analyzing the Duration and Prolongation of Performance-Based Contracts Through Hazard-Based Duration and Zero-Inflated Random Parameters Poisson Models
• Application and Integration of Lattice Data Analysis, Network K-Functions, and Geographic Information System Software to Study Ice-Related Crashes
• Application of Bayesian Statistics to Identify Highway Sections with Atypically High Rates of Median-Crossing Crashes
• Application of Dynamic Spatial Ordered Probit Model: Patterns of Ozone Concentration in Austin, Texas
• Bayesian Mixture Model for Estimating Freeway Travel Time Distributions from Small Probe Samples from Multiple Days
• Bayesian Multivariate Poisson Lognormal Models for Crash Severity Modeling and Site Ranking
• Examining Application of Aggregated and Disaggregated Poisson–Gamma Models Subjected to Low Sample Mean Bias
• Investigating the Variability in Daily Traffic Counts Through Use of ARIMAX and SARIMAX Models: Assessing the Effect of Holidays on Two Site Locations
• Method of Selection of Poisson-Based Wavelet Shrinkage Sites
• Spatial Autocorrelation and Bayesian Spatial Statistical Method for Analyzing Intersections Prone to Injury Crashes