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

Pengpeng Jiao,Jin Guo,Meiqi Liu

doi:10.1155/2015/251781

Abstract

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.

Highlights

In most Chinese cities, with the rapid development of urbanization and motorization, the density of urban land-use is increasing very fast, and the spatial distribution of residence location and workplace is turning to be unbalanced
With rather flexible formulation in the structure, it mainly has the following advantages: (1) there is no independence of irrelevant alternatives (IIA) property in the model; (2) the error item of the utility function can be subject to any random distribution, which removes the constraint of Gumbel distribution in logit model or normal distribution in probit model; (3) the estimated parameters are subject to some kind of random distribution, which incorporates the taste variations of different decision makers
Parameter estimation of latent class model is usually implemented using two kinds of iterative algorithms based on maximum likelihood method: expectation maximization algorithm and Newton-Raphson algorithm

Summary

Introduction

In most Chinese cities, with the rapid development of urbanization and motorization, the density of urban land-use is increasing very fast, and the spatial distribution of residence location and workplace is turning to be unbalanced. To analyze the different residence location and workplace choice characteristics according to household types, one key feature of this paper is to formulate a latent class model and to extract the inherent household groups. Another key feature is to further combine the residence location and workplace together as the choice alternatives and present the joint residence-workplace location choice models for each latent class using mixed logit methods. The integrated model of joint residenceworkplace location choice is put forward in Section 4 based on the combined choice alternatives

General Model Framework

Latent Class Model for Household Clustering

Integrated Model of Joint Residence-Workplace Location Choice

Estimation Results

Conclusions