A Recursive Logit Model with Choice Aversion and Its Application to Route Choice Analysis

2020 ◽  
Author(s):  
Austin Knies ◽  
Emerson Melo
Keyword(s):  
Logit Model ◽  
Route Choice ◽  
Choice Analysis

scholarly journals A nested recursive logit model for route choice analysis

2015 ◽  
Vol 75 ◽  
pp. 100-112 ◽  
Author(s):  
Tien Mai ◽  
Mogens Fosgerau ◽  
Emma Frejinger
Keyword(s):  
Logit Model ◽  
Route Choice ◽  
Choice Analysis

scholarly journals Route Choice Analysis in the Tokyo Metropolitan Area Using a Link-based Recursive Logit Model Featuring Link Awareness

2018 ◽  
Vol 34 ◽  
pp. 251-258 ◽  
Author(s):  
Noriko Kaneko ◽  
Hideki Oka ◽  
Makoto Chikaraishi ◽  
Henrik Becker ◽  
Daisuke Fukuda
Keyword(s):  
Metropolitan Area ◽  
Logit Model ◽  
Route Choice ◽  
Tokyo Metropolitan ◽  
Tokyo Metropolitan Area ◽  
Choice Analysis

scholarly journals Research: Discrete Choice Analysis in Hospitality Management Research

1997 ◽  
Vol 21 (1) ◽  
pp. 28-47 ◽  
Author(s):  
Rohit Verma ◽  
Gary M. Thompson
Keyword(s):  
Conjoint Analysis ◽  
Discrete Choice ◽  
Logit Model ◽  
Multinomial Logit Model ◽  
Theoretical Background ◽  
Hospitality Management ◽  
Discrete Choice Analysis ◽  
Management Research ◽  
Customer Preferences ◽  
Choice Analysis

This article focuses on discrete choice analysis (DCA), which offers an effective approach for incorporating customer preferences into operating decisions in hospitality businesses. First the theoretical background of DCA is presented, including a discussion of how DCA compares to conjoint analysis. The authors then present a guide to designing and conducting a DCA study. Conducting a discrete choice study involves identifying the attributes relevant to customers'choices and the appropriate levels of these attributes, designing an experiment, collecting data and estimating parameters using a multinomial logit model. Finally, the strategic implications of DCA in hospitality management research are discussed.

Solving the Overlapping Problem in Route Choice with Paired Combinatorial Logit Model

2003 ◽  
Vol 1857 (1) ◽  
pp. 65-73 ◽  
Author(s):  
Anthony Chen ◽  
Panatda Kasikitwiwat ◽  
Zhaowang Ji
Keyword(s):  
Mathematical Programming ◽  
Logit Model ◽  
Choice Model ◽  
Route Choice ◽  
Similarity Index ◽  
User Equilibrium ◽  
Transportation Systems ◽  
Programming Formulation ◽  
Stochastic User Equilibrium ◽  
Mathematical Programming Formulation

Recently, there has been renewed interest in improving the logit-based route choice model because of the importance of the route choice model in intelligent transportation systems applications, particularly the applications of advanced traveler information systems. The paired combinatorial logit (PCL) model and its equivalent mathematical programming formulation for the route choice problem have been studied. An algorithm based on the partial linearization method is presented for solving the PCL stochastic user equilibrium problem. Detailed examples are provided to explain how this hierarchical logit model resolves the overlapping problem through the similarity index while still accounting for both congestion and stochastic effects in the mathematical programming formulation.

Path Size Modeling in Multimodal Route Choice Analysis

2005 ◽  
Vol 1921 (1) ◽  
pp. 27-34 ◽  
Author(s):  
Sascha Hoogendoorn-Lanser ◽  
Rob van Nes ◽  
Piet Bovy
Keyword(s):  
Route Choice ◽  
Transport Networks ◽  
Main Mode ◽  
Choice Analysis ◽  
Multimodal Transport ◽  
Choice Sets ◽  
Error Terms ◽  
Multimodal Networks ◽  
Definition Of ◽  
Size Variable

Travelers in multimodal networks make many choices (e.g., main mode, access modes, egress modes, boarding nodes, transfer nodes, and egress nodes). One way to address this complexity of choices is to analyze choice sets of multimodal routes. However, choice sets for multimodal networks are large, and overlap of routes within choice sets is substantial. This paper focuses on overlap in multimodal transport networks. An overview of the topic of overlap and route choice modeling is given and is followed by an analysis of how overlap might be defined in the context of multimodal networks. Three definitions of “overlap” are proposed, based on number of legs, time, or distance. The different definitions are analyzed using path size logit estimations, which show that path size must be accounted for. Furthermore, the definition of “path size” for multimodal transport networks should be different from that used for road networks: for multimodal transport networks, a definition using number of legs yields substantially better results. Estimation results suggest that the weighting parameter corresponding with the path size variable should be equal to 1, implying that the path size variable based on number of legs accounts for the correlation of error terms of overlapping parts.

scholarly journals Structural Choice Analysis with Nested Logit Models

2002 ◽  
Vol 2 (3) ◽  
pp. 227-252 ◽  
Author(s):  
Florian Heiss
Keyword(s):  
Logit Model ◽  
Utility Maximization ◽  
Nested Logit ◽  
Random Utility ◽  
Logit Models ◽  
Random Utility Maximization ◽  
Structural Choice ◽  
Choice Analysis ◽  
Nested Logit Models ◽  
The Empirical Analysis

The nested logit model has become an important tool for the empirical analysis of discrete outcomes. There is some confusion about its specification of the outcome probabilities. Two major variants show up in the literature. This paper compares both and finds that one of them (called random utility maximization nested logit, RUMNL) is preferable in most situations. Since the command nlogit of Stata 7.0 implements the other variant (called non-normalized nested logit, NNNL), an implementation of RUMNL called nlogitrum is introduced. Numerous examples support and illustrate the differences between both specifications.

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