Study on Residential Daily En-Route Travel Choice Behavior with Traffic Information: Application of RP/SP Joint Survey and Disaggregate Model
1
Citation
3
Reference
10
Related Paper
Abstract:
En-route travel choice behavior is one of the primary travel behavior over residential daily trips. Providing traffic information will exert an impact on en-route travel choice behavior. Through comparative studies, the paper reveals that RP/SP joint survey and the disaggregate model are two suitable methods. On the basis of that, the paper develops a scheme for RP/SP joint survey and a Multinomial Logit (MNL) Model for modeling and analysis. The aim of the paper is to provide theoretical and practical support for the research of en-route travel choice behavior with traffic information in China.Keywords:
Mixed logit
For over the last thirty years the multinomial logit model has been the standard in choice modeling. Development in econometrics and computational algorithms has led to the increasing tendency to opt for more flexible models able to depict more realistically choice behavior. This study compares three discrete choice models, the standard multinomial logit, the error components logit, and the random parameters logit. Data were obtained from two choice experiments conducted to investigate consumers’ preferences for fresh pears receiving several postharvest treatments. Model comparisons consisted of in-sample and holdout sample evaluations. Results show that product characteristics hence, datasets, influence model performance. We also found that the multinomial logit model outperformed in at least one of three evaluations in both datasets. Overall, findings signal the need for further studies controlling for context and dataset to have more conclusive cues for discrete choice models capabilities.
Discrete choice
Mixed logit
Sample (material)
Multinomial distribution
Cite
Citations (0)
For over the last thirty years the multinomial logit model has been the standard in choice modeling. Development in econometrics and computational algorithms has led to the increasing tendency to opt for more flexible models able to depict more realistically choice behavior. This study compares three discrete choice models, the standard multinomial logit, the error components logit, and the random parameters logit. Data were obtained from two choice experiments conducted to investigate consumers’ preferences for fresh pears receiving several postharvest treatments. Model comparisons consisted of in-sample and holdout sample evaluations. Results show that product characteristics hence, datasets, influence model performance. We also found that the multinomial logit model outperformed in at least one of three evaluations in both datasets. Overall, findings signal the need for further studies controlling for context and dataset to have more conclusive cues for discrete choice models capabilities.
Discrete choice
Mixed logit
Sample (material)
Multinomial distribution
Cite
Citations (0)
This paper introduces the logitr R package for fast maximum likelihood estimation of multinomial logit and mixed logit models with unobserved heterogeneity across individuals, which is modeled by allowing parameters to vary randomly over individuals according to a chosen distribution. The package is faster than other similar packages such as mlogit, gmnl, mixl, and apollo, and it supports utility models specified with
Mixed logit
Multinomial distribution
Multinomial probit
Cite
Citations (12)
Mixed logit
Cite
Citations (1)
Mixed logit
Discrete choice
Independence
Cite
Citations (1,652)
Abstract. The examination of car driver behavior deciding which parking space to choose. The application of various logit models has led to an insight of selecting between the available alternatives: free on-street parking, paid on-street parking and parking in an underground car park. Several logit models allowing for correlation between random taste parameters calculate coefficients using stated choice data. The main purpose of this paper is to extend a Mixed Multinomial Logit (M-MNL) model to similar models which can also implement the correlation between random parameters. This leads to the following: Nested Logit (NL), Nested Generalized Extreme Value (NGEV), Cross-Nested Logit (CNL) and Mixed-Mixed Multinomial Logit (MM-MNL) models to approach modeling parking choice models. The estimated coefficients are used to compute subjective-value of time (SVT) when looking for a parking space.
Mixed logit
Discrete choice
Nested logit
Value of time
Parking space
Value (mathematics)
Cite
Citations (1)
For over the last thirty years the multinomial logit model has been the standard in choice modeling. Development in econometrics and computational algorithms has led to the increasing tendency to opt for more flexible models able to depict more realistically choice behavior. This study compares three discrete choice models, the standard multinomial logit, the error components logit, and the random parameters logit. Data were obtained from two choice experiments conducted to investigate consumers’ preferences for fresh pears receiving several postharvest treatments. Model comparisons consisted of in-sample and holdout sample evaluations. Results show that product characteristics hence, datasets, influence model performance. We also found that the multinomial logit model outperformed in at least one of three evaluations in both datasets. Overall, findings signal the need for further studies controlling for context and dataset to have more conclusive cues for discrete choice models capabilities.
Discrete choice
Mixed logit
Sample (material)
Multinomial distribution
Cite
Citations (0)
mixmixlogit is a Stata command that implements the mixed-mixed multinomial logit model (MM-MNL) for binary dependent variable data. It was first proposed in Keane and Wasi (2013) and Greene and Hensher (2013), and applied recently in Keane et al. (2020). It generalises both 'mixed logit' and 'latent class logit' by allowing for multiple latent types in the underlying data that are each characterised by a distribution of random parameters (as opposed to latent class logit, which assumes a homogeneous coefficient vector for each latent type, and mixed logit that allows for a distribution of random parameters for a single type of consumer or agent).
Mixed logit
Multinomial distribution
Mixed model
Multinomial probit
Discrete choice
Cite
Citations (0)
The paper investigates the influence of different model specifications for interpreting the results of discrete choice experiments when investigating heterogeneous public landscape preferences. Comparing model specifications based on the Mixed Multinomial Logit and the Generalized Multinomial Logit Model reveals that the parameter estimates appear qualitatively comparable. Still, a more in-depth investigation of the conditional estimate distributions of the sample show that parameter interactions in the Generalized Multinomial Logit Model lead to different interpretations compared to the Mixed Multinomial Logit Model. This highlights the potential impact of common model specifications in the results in landscape preference studies.
Mixed logit
Multinomial distribution
Discrete choice
Multinomial probit
Cite
Citations (0)
This paper describes how, with the population aging in many countries, older people’s travel is recently getting more attention in the transportation literature. However the understanding of factors influencing their mode choice is still limited. In this research the focus is on mode choice for shopping trips as these are the most frequent trips of older people. The study is not limited to shopping trips, but also investigates the mode choice of the trips after shopping trips. Two types of models—the multinomial logit model and the nested logit model— are fitted to LATS data to estimate the effects of various factors on the mode choice of older people for shopping travel. The appropriateness of these models and the implications of the findings are discussed.
Mode choice
Nested logit
Mode (computer interface)
Cite
Citations (0)