scholarly journals Integrated Model of Joint Residence-Workplace Location Choice and Commute Behavior Using Latent Class and Mixed Logit Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Pengpeng Jiao ◽  
Meiqi Liu ◽  
Jin Guo
Keyword(s):  
Latent Class ◽  
Choice Model ◽  
Location Choice ◽  
Rapid Development ◽  
Latent Class Model ◽  
Mixed Logit ◽  
Integrated Model ◽  
Model Framework ◽  
Class Model ◽  
Residence Location

With the rapid development of urbanization and motorization, urban commute trips are becoming increasingly serious due to the unbalanced distribution of residence and workplace land-use types in most Chinese cities. To explore the inherent interrelations among residence location, workplace, and commute trip, an integrated model framework of joint residence-workplace location choice and commute behavior is put forward based on the personal trip survey data of Beijing in 2005. First, to extract households’ different choice characteristics, this paper presents a latent class model, clusters all households into several groups, and analyzes the conditional probability of each group. Second, the paper integrates the residence location and workplace together as the joint choice alternative, employs the socioeconomic factors, individual attributes, household attributes, and trip characteristics as explanatory variables, and formulates the joint residence-workplace location choice model using mixed logit method. Estimations of the latent class model show that four latent groups fit the data best. Further results of the joint residence-workplace location choice model indicate that there exist significantly different choice characteristics in each latent group. Generally, the integrated model framework outperforms traditional location choice methods.

scholarly journals POSITIONING VIETNAM’S PANGASIUS CATFISH IN THE FRENCH MARKET USING DISCRETE CHOICE MODEL

2014 ◽  
Vol 17 (4) ◽  
pp. 96-111
Author(s):  
Thong Tien Nguyen ◽  
Hung Manh Nguyen
Keyword(s):  
Discrete Choice ◽  
Low Income ◽  
Latent Class ◽  
Choice Model ◽  
Latent Class Model ◽  
Random Parameter ◽  
Discrete Choice Model ◽  
Class Model ◽  
Additional Information ◽  
Random Parameter Model

The study used discrete choice model to investigate the position of Vietnam’s Pangasius catfish in the French market. Data was collected via a choice experiment designed for 12 aquaculture species familiar to French consumers. The random parameter model was estimated and used to calculate the share elasticity. The market position of the aquaculture products in this study was calculated based on the competitive clout, vulnerability scores, and ranked-order implicit values. The results show that Vietnam’s Pangasius has a low competitive clout, high vulnarability score, and low ranked-order implicit value. A latent class model was also estimated for comparison and acquisition of additional information. A strong segment of Pangasius (11.9%) is described by low income and education consumers, women at mid-age dominated, and family with children. To improve the Pangasius position and image in the international market, Vietnam needs promotional and marketing campaigns at global level for the product.

scholarly journals Decisión de localización del hogar en España. Modelo de clases latentes vs. modelo mixto.

2019 ◽  
Vol 37 (3) ◽  
pp. 214
Author(s):  
Anastasia Hernández Alemán
Keyword(s):  
Latent Class ◽  
Choice Model ◽  
Latent Class Model ◽  
Mixed Logit ◽  
Noise Pollution ◽  
Individual Characteristics ◽  
Mixed Logit Models ◽  
Or Groups ◽  
Household Preference ◽  
Unobservable Heterogeneity

In this work the unobservable heterogeneity respect to the choice of household’s residential location is analyzed, considering three alternatives: urban area, interurban area and rural area. It is employed latent class choice model to represent this behavior and the results are compared with the multinomial and mixed logit models. The more flexible structure of the latent class model allows us more deeply into the analysis of unobservable heterogeneity with respect to the more limited analysis of the randomness of the parameters of the mixed logit model. The empirical results indicate two classes or groups of households with differentiated behaviors regarding to the decision of location and associated to different lifestyles. Thus, certain attributes of the location of the dwelling, such as noise, pollution or delinquency, have an effect on household preference in a different way according to the class of belonging. Also, individual characteristics of the household’s head, such as age, gender or educational level, have a different impact on the preference for location according to the class of belonging. The results allow us to distinguish two lifestyles associated with each class or group of preference: suburban and urban.

Applications of Computational and Model-Based Statistical Methodologies in Archaeology

2011 ◽  
pp. 1-26
Author(s):  
Ioulia Papageorgiou
Keyword(s):  
Statistical Analysis ◽  
Latent Class ◽  
Rapid Development ◽  
Latent Class Model ◽  
Class Model ◽  
Normal Distributions ◽  
Model Based Clustering ◽  
Model Based ◽  
Bayesian Mixture ◽  
Mixture Of Normal Distributions

Quantitative Archaeology had a rapid development in the past few decades due to the parallel development of methodologies in Physics, Chemistry and Geology that can be implemented in archaeological findings and produce measurements on a number of variables. Those measurements form the data, the basis for a statistical analysis, which in turn can provide us with objective results and answers, within the prediction or estimation framework, about the archaeological findings. Exploratory statistical analysis was almost exclusively used initially for analyzing such data mainly because of their simplicity. The simplicity originates from the fact that exploratory techniques do not rely on any distribution assumption and conduct a non-parametric statistical analysis. However the recent development of the statistical methodology and the computing software allows us to make use of more sophisticated statistical techniques and obtain more informative results. We explore and present applications of three such techniques. The finite mixture approach for model based clustering, the latent class model and the Bayesian mixture of normal distributions with unknown number of components. All three methods can be used for identifying sub-groups in the sample and classify the items.

scholarly journals Accounting for Attribute Non-Attendance and Common-Metric Aggregation in the Choice of Seat Belt Use, a Latent Class Model with Preference Heterogeneity

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 84 ◽  
Author(s):  
Mahdi Rezapour ◽  
Khaled Ksaibati
Keyword(s):  
Goodness Of Fit ◽  
Latent Class ◽  
Latent Class Model ◽  
Seat Belt ◽  
Mixed Logit ◽  
Continuous Distribution ◽  
Preference Heterogeneity ◽  
Class Model ◽  
Seat Belt Use ◽  
The Right

A choice to use a seat belt is largely dependent on the psychology of the vehicles’ occupants, and thus those decisions are expected to be characterized by preference heterogeneity. Despite the importance of seat belt use on the safety of the roadways, the majority of existing studies ignored the heterogeneity in the data and used a very standard statistical or descriptive method to identify the factors of using a seatbelt. Application of the right statistical method is of crucial importance to unlock the underlying factors of the choice being made by vehicles’ occupants. Thus, this study was conducted to identify the contributory factors to the front-seat passengers’ choice of seat belt usage, while accounting for the choice preference heterogeneity. The latent class model has been offered to replace the mixed logit model by replacing a continuous distribution with a discrete one. However, one of the shortcomings of the latent class model is that the homogeneity is assumed across a same class. A further extension is to relax the assumption of homogeneity by allowing some parameters to vary across the same group. The model could still be extended to overlay some attributes by considering attributes non-attendance (ANA), and aggregation of common-metric attributes (ACMA). Thus, this study was conducted to make a comparison across goodness of fit of the discussed models. Beside a comparison based on goodness of fit, the share of individuals in each class was used to see how it changes based on various model specifications. In summary, the results indicated that adding another layer to account for the heterogeneity within the same class of the latent class (LC) model, and accounting for ANA and ACMA would improve the model fit. It has been discussed in the content of the manuscript that accounting for ANA, ACMA and an extra layer of heterogeneity does not just improve the model goodness of fit, but largely impacts the share of class allocation of the models.

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