A simultaneous decision-making approach to model the demand for freight transportation

1991 ◽  
Vol 18 (3) ◽  
pp. 515-520 ◽  
Author(s):  
W. M. Abdelwahab ◽  
M. A. Sargious
Keyword(s):  
Maximum Likelihood ◽  
Discrete Choice ◽  
Choice Model ◽  
Mode Choice ◽  
Freight Transportation ◽  
Choice Models ◽  
Ordinary Least Squares ◽  
Discrete Choice Models ◽  
Estimation Methods ◽  
Complete Model

The application of discrete choice models (e.g., logit, probit) to study modal choice in passenger transportation has had a wide acceptance in the literature. However, little success had been reported on the application of these models to study the demand for freight transportation. This is mainly because in freight transportation a model that merely attempts to explain the choice of mode without taking into consideration other related factors, such as shipment size, is only one part of a complete model. Another type of models known as inventory-based models, which takes these factors into consideration, has been developed and applied with a greater success. However, the data requirement of these inventory models has hampered their applicability, especially in situations with limited data on goods movement. This paper presents a new approach to study the demand for intercity freight transportation. The model proposed in this paper utilizes the strength of discrete choice models (e.g., probit) in explaining the process of mode choice as one part of a complete model. The complete model is presented as a joint discrete/continuous choice model for the choices of mode and shipment size. The model is practical in that it requires the same amount and quality of data that would be required to develop a standard disaggregate mode choice model, and it can be estimated using simple two-stage estimation methods which utilizes standard probit maximum likelihood and ordinary least squares estimation techniques. Key words: disaggregate, freight transportation, maximum likelihood, mode, model, probit, shipment.

scholarly journals Mode choice analysis using discrete choice model from transport user (Case study: Jakarta LRT, Indonesia)

2018 ◽  
Vol 181 ◽  
pp. 03001
Author(s):  
Dwi Novi Wulansari ◽  
Milla Dwi Astari
Keyword(s):  
Discrete Choice ◽  
Public Transportation ◽  
Choice Model ◽  
Stated Preference ◽  
Mode Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Light Rail ◽  
Choice Analysis ◽  
Mode Choice Model

Jakarta Light Rail Transit (Jakarta LRT) has been planned to be built as one of mass rail-based public transportation system in DKI Jakarta. The objective of this paper is to obtain a mode choice models that can explain the probability of choosing Jakarta LRT, and to estimate the sensitivity of mode choice if the attribute changes. Analysis of the research conducted by using discrete choice models approach to the behavior of individuals. Choice modes were observed between 1) Jakarta LRT and TransJakarta Bus, 2) Jakarta LRT and KRL-Commuter Jabodetabek. Mode choice model used is the Binomial Logit Model. The research data obtained through Stated Preference (SP) techniques. The model using the attribute influences such as tariff, travel time, headway and walking time. The models obtained are reliable and validated. Based on the results of the analysis shows that the most sensitive attributes affect the mode choice model is the tariff.

scholarly journals Deep Neural Network Design for Modeling Individual-Level Travel Mode Choice Behavior

2020 ◽  
Vol 12 (18) ◽  
pp. 7481
Author(s):  
Daisik Nam ◽  
Jaewoo Cho
Keyword(s):  
Neural Network ◽  
Discrete Choice ◽  
Deep Neural Network ◽  
Choice Behavior ◽  
Mode Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Travel Mode ◽  
Individual Level ◽  
Travel Mode Choice

Individual-level modeling is an essential requirement for effective deployment of smart urban mobility applications. Mode choice behavior is also a core feature in transportation planning models, which are used for analyzing future policies and sustainable plans such as greenhouse gas emissions reduction plans. Specifically, an agent-based model requires an individual level choice behavior, mode choice being one such example. However, traditional utility-based discrete choice models, such as logit models, are limited to aggregated behavior analysis. This paper develops a model employing a deep neural network structure that is applicable to the travel mode choice problem. This paper uses deep learning algorithms to highlight an individual-level mode choice behavior model, which leads us to take into account the inherent characteristics of choice models that all individuals have different choice options, an aspect not considered in the neural network models of the past that have led to poorer performance. Comparative analysis with existing behavior models indicates that the proposed model outperforms traditional discrete choice models in terms of prediction accuracy for both individual and aggregated behavior.

Unobserved Heterogeneity in Dynamic Discrete Choice Models

1993 ◽  
Vol 25 (4) ◽  
pp. 495-519 ◽  
Author(s):  
S Reader
Keyword(s):  
Discrete Choice ◽  
Simulation Analysis ◽  
Choice Model ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Estimation Procedure ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Dynamic Discrete Choice ◽  
Cross Sectional

Monte Carlo simulation methods are used to confirm the identifiability of discrete choice models in which unobserved heterogeneity is specified as a random effect and modelled using the nonparametric mass-points approach. This simulation analysis is also used to examine alternative strategies for the estimation of such models by using a quasi-Newton maximum-likelihood estimation procedure, given the apparent sensitivity of model identification to choice of starting values. A mass-point model approach is then applied to a dataset of repeated choice involving household shopping trips between three types of retail centre, and the results from this approach are compared with those obtained from a conventional cross-sectional multinomial logit choice model as well as to results from a model in which a parametric distribution (the Dirichlet) is used to model the unobserved heterogeneity.

Linear Demand Systems are Inconsistent with Discrete Choice

2010 ◽  
Vol 10 (1) ◽  
Author(s):  
Sonia Jaffe ◽  
E. Glen Weyl
Keyword(s):  
Discrete Choice ◽  
Choice Model ◽  
Discrete Choice Model ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Second Order ◽  
Demand Systems ◽  
Linear Demand

We show that with more than two options, a discrete choice model cannot generate linear demand. In doing so, we demonstrate a prediction of such discrete choice models that is falsifiable based on local second-order properties of demand.

scholarly journals Modeling enterprise location choice decision behavior

2021 ◽  
Vol 14 (1) ◽  
pp. 669-691
Author(s):  
Nguyen Cao Y
Keyword(s):  
Spatial Correlation ◽  
Discrete Choice ◽  
Choice Model ◽  
Location Choice ◽  
Mixed Logit ◽  
Multinomial Logit ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Spatial Effects ◽  
Modeling Framework

This study presents a location choice model that incorporates urban spatial effects for enterprises. A modeling framework is developed to analyze decisions regarding location choice for enterprises using a series of discrete choice models including multinomial logit without any urban spatial effects, multinomial logit incorporating urban spatial effects, and mixed logit incorporating urban spatial effects. In this framework, urban spatial effects, such as the urban spatial correlation among enterprises in deterministic terms and the urban spatial correlation among zones in the error term, are captured by mixed logit models in particular and discrete choice models in general. The results indicate that the urban spatial effects and the land prices in a given zone strongly affect the decision-making process of all the enterprises in the Tokyo metropolitan area. Moreover, the important role of urban spatial effects in the proposed model will be clarification through comparing the three above models. This comparison will be implemented on the basis of three types of indicators such as the log likelihood ratio, Akaike information indicator, and hit ratio of each model.

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