Discrete Choice Model Estimation with Revealed Preference Data and Aggregate Service Level Variables

2021 ◽  
Author(s):  
Marco Antonio Batarce
Keyword(s):  
Discrete Choice ◽  
Choice Model ◽  
Revealed Preference ◽  
Discrete Choice Model ◽  
Service Level ◽  
Model Estimation ◽  
Preference Data

scholarly journals Railway and road discrete choice model for foreign trade freight between Antioquia and the Port of Cartagena

2016 ◽  
Vol 36 (3) ◽  
pp. 22 ◽  
Author(s):  
Juan Diego Pineda Jaramillo ◽  
Iván Reinaldo Sarmiento Ordosgoitia ◽  
Jorge Eliécer Córdoba Maquilón
Keyword(s):  
Qualitative Research ◽  
Discrete Choice ◽  
Logit Model ◽  
Choice Model ◽  
Stated Preference ◽  
Revealed Preference ◽  
Freight Transportation ◽  
Multinomial Logit Model ◽  
Discrete Choice Model ◽  
The Cost

Most Colombian freight is transported on roads with barely acceptable conditions, and although there is a speculation about the need for a railway for freight transportation, there is not a study in Colombia showing the variables that influence the modal choice by the companies that generate freight transportation. This article presents the calculation of demand for a hypothetical railway through a discrete choice model. It begins with a qualitative research through focus group techniques to identify the variables that influence the choice of persons responsible for the transportation of large commercial companies in Antioquia (Colombia). The influential variables in the election were the cost and service frequency, and these variables were used to apply a Stated Preference (SP) and Revealed Preference (RP) survey, then to calibrate a Multinomial Logit Model (MNL), and to estimate the influence of each of them. We show that the probability of railway choice by the studied companies varies between 67% and 93%, depending on differences in these variables.

scholarly journals Identifying Free-riding in Home Renovation Programs Using Revealed Preference Data

2013 ◽  
Vol 233 (5-6) ◽  
Author(s):  
Peter Grösche ◽  
Christoph M. Schmidt ◽  
Colin Vance
Keyword(s):  
Discrete Choice ◽  
Energy Savings ◽  
Choice Model ◽  
Revealed Preference ◽  
Free Riding ◽  
Preference Data ◽  
Moderate Energy ◽  
Conservation Activity ◽  
Program Intervention ◽  
Subsidy Program

SummaryIdentifying free-ridership is significant to several issues relevant to program evaluation, including the calculation of net program benefits and assessments of political acceptability. Despite the potential of free-ridership to seriously undermine the economic efficiency of a program intervention, for instance to foster energy efficiency, the issue remains largely absent from contemporary environmental and energy policy discussions in Europe. One reason for this neglect is the inherent difficulty of assessing which households would have undertaken the energy conservation activity even without the program. This paper proposes a procedure to calculate the free-rider share using revealed preference data on home renovations from Germany’s residential sector.We employ a discrete-choice model to analyze the effect of grants on renovation choices, the output from which is used to assess the extent of free-ridership under a subsidy program very akin to an implemented grants program in Germany. Our empirical results suggest only very moderate energy savings induced by the program, making free-riding a problem of outstanding importance.

Discrete choice model for air-cargo mode selection

2014 ◽  
Vol 25 (3) ◽  
pp. 656-672 ◽  
Author(s):  
Subhro Mitra ◽  
Steven M. Leon
Keyword(s):  
Discrete Choice ◽  
Choice Model ◽  
Mode Choice ◽  
Mode Selection ◽  
Discrete Choice Model ◽  
Air Cargo ◽  
Demand Forecast ◽  
Content Type ◽  
Preference Data ◽  
Factors Influencing

Purpose – The purpose of this paper is to develop a better understanding of the factors that influence a shipper's decision to choose air cargo as a mode of shipment. Design/methodology/approach – A disaggregate multinomial discrete choice model is developed using freight shipment survey data to identify critical factors influencing air cargo mode choice. Disaggregate revealed preference data is obtained from surveying 347 manufacturers, freight forwarders, and other third-party service providers. Findings – The empirical model developed in this research shows that the rate of shipment, time of transit, cost-per-pound shipped, quantity shipped, perishability and delay rate of the mode are significant factors that influence mode choice. Research limitations/implications – The discrete choice model developed can be improved by taking into account logistics costs not considered in this research. Perhaps more in-depth surveys of the shippers and freight forwarders are needed. Additionally, improving the mode choice model by including stated preference data and subsequently incorporating service quality latent variables would be beneficial. Practical implications – Identifying the sensitivity of the shippers to various factors influencing mode selection enables transportation planners make better demand forecast for each mode of transportation. Originality/value – This paper extends previous mode choice studies by analyzing mode selection between air cargo and other modes. Better forecasting is achieved by replacing the logit model with probit, heteroscedastic extreme value and mixed logit models.

scholarly journals Pre-calibration of a Discrete Choice Model and Evaluation of Cycling Mobility for Île-de-France

2021 ◽  
Vol 184 ◽  
pp. 172-177
Author(s):  
Guoxi Feng ◽  
Maxime Jean ◽  
Alexandre Chasse ◽  
Sebastian Hörl
Keyword(s):  
Discrete Choice ◽  
Choice Model ◽  
Discrete Choice Model

Analysis of decision-making for air conditioning users based on the discrete choice model

2021 ◽  
Vol 131 ◽  
pp. 106963
Author(s):  
Jingjie Wang ◽  
Hongbin Wu ◽  
Shihai Yang ◽  
Rui Bi ◽  
Junhua Lu
Keyword(s):  
Decision Making ◽  
Discrete Choice ◽  
Air Conditioning ◽  
Choice Model ◽  
Discrete Choice Model

scholarly journals Calibration and Test of a Discrete Choice Model with Endogenous Choice Sets

2010 ◽  
Vol 30 (2) ◽  
pp. 95-118 ◽  
Author(s):  
Jian Dai
Keyword(s):  
Discrete Choice ◽  
Choice Model ◽  
Discrete Choice Model ◽  
Choice Sets
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