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

2021 ◽  
Author(s):  
Marco Antonio Batarce

2016 ◽  
Vol 36 (3) ◽  
pp. 22 ◽  
Author(s):  
Juan Diego Pineda Jaramillo ◽  
Iván Reinaldo Sarmiento Ordosgoitia ◽  
Jorge Eliécer Córdoba Maquilón

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.



Author(s):  
Peter Grösche ◽  
Christoph M. Schmidt ◽  
Colin Vance

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.



2014 ◽  
Vol 25 (3) ◽  
pp. 656-672 ◽  
Author(s):  
Subhro Mitra ◽  
Steven M. Leon

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.



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


Author(s):  
Jingjie Wang ◽  
Hongbin Wu ◽  
Shihai Yang ◽  
Rui Bi ◽  
Junhua Lu




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