Seo Youn Yoon

Development of Indicators of Opportunity-Based Accessibility

“Accessibility,” defined as the ease (or difficulty) with which opportunities for activity can be reached from a given location, can be measured with the cumulative amount of opportunities from an origin within a given amount of travel time. These indicators can be used in regional planning and modeling efforts to integrate land use and travel demand, and an attempt should be made to calculate these indicators for the smallest geographic area. The primary objective of this paper is to illustrate the creation of realistic space-sensitive and time-sensitive block-level accessibility indicators to track the availability of opportunities. These indicators support the development of an activity-based travel demand model by Southern California Association of Governments to provide second-by-second and parcel-by-parcel modeling and simulation. The indicators also provided the base information for mapping opportunities of access to 15 types of industries at different times during a day. The indicators and their maps were defined for the entire region of Southern California through largely available data that included the Census Transportation Planning Package, Dun & Brad-street postprocessed data, detailed highway networks and travel times from the four-step regional models, and arrival and departure times of workers by industry.

Impact of time-space prism accessibility on time use behavior and its propagation through intra-household interaction

Abstract Accessibility influences a variety of behavioral facets in time use, resource allocation, and activity/task allocation of household members. In spite of the recognition of the importance of accessibility in behavioral modeling, appropriate measures that account for individual heterogeneity in the net accessibility are rarely used in time use and activity participation models. We fill this gap by first measuring time-space prism accessibility for each individual based on their spatio-temporal constraints. The time-space prism accessibility measures are included in a system of behavioral models and the relationship between accessibility and time use behavior is assessed in the context of intra-household interaction. Time-space prism accessibility measures account for the temporal constraints and spatial settings around important activity pegs for each individual, and therefore provide a unique opportunity to assess temporal and spatial dimensions of human behavior together. These approaches also solve the issues of endogenous relationships between time-space prism accessibility and time use that should be accounted for in regression-based analysis. In the analysis reported in this paper, Structural Equation Model (SEM) with latent variables is used to model the complex correlation between individual accessibility and time use with a proper representation of the endogeneity of time-space prism accessibility. The SEM is applied to ten different life cycle stages and the results are compared. As expected, the impact of parenthood on the association between the level of time-space prism accessibility and time use behavior is prominent.

Linking Elderly Transport Mobility and Subjective Well-being: A Multivariate Latent Modeling Approach

Mobility is critical for social integration in a complex urban society and essential to the maintenance of life satisfaction and well-being. Subjective well-being has recently become a topic of interest within the transportation research community. This paper aims to understand the fundamental linkages between subjective well-being or happiness and transport mobility–travel behavior of the elderly population. The research here is based on data from the Supplement on Disability and Use of Time for 2009, which specifically targeted senior couples with an average age of 68. With the use of scores to a set of satisfaction questions about life, health, memory, finances, and marriage, latent class clusters are estimated. This estimate leads to four distinct clusters of respondents that depend on the degree of happiness in each of the satisfaction questions. With the membership of each cluster as a dependent variable, ordered probit and multinomial logistic regression models are estimated to study the relationship between clusters and individual characteristics, including sociodemographics, activity patterns, time use, and use of active modes (walking–bicycling). The results show that respondents who engage in activities out of the home, socialize, and enjoy better mobility also report higher levels of subjective well-being leading to a better quality of life. The model findings also show that illness and pain are related to lower well-being and that quality of life in older age is correlated to mobility.

Spatial Analysis of Propensity to Escort Children to School in Southern California

The spatial distribution of childrens school commute behavior is analyzed from three perspectives: commuting to school independently of parents, commuting to school by active modes, and allocation of escorting tasks for children between mothers and fathers. Accessibility measures and population density are introduced in the propensity regression models to account for the impact of spatial characteristics around school locations and to identify the spatial distribution of behavioral patterns. The results from the models are presented as maps combining the impacts of all the significant spatial variables to display the spatial patterns of behavior and intrahousehold interaction. These patterns can identify and pinpoint the impact of barriers, with the paper offering the example of the negative impact of a park area in the middle of the city of Los Angeles, California, on childrens independent and active commutes to school. Similarly, barriers create significantly different intra-household interaction patterns at different locations in the region. The results of this study show that an opportunity exists to expand the micro-analysis to a more comprehensive treatment of travel behavior in space and to contribute to the development of models integrating land use and transportation.

Exploring Daily Rhythms of Interpersonal Contacts

This study used activity-travel diary data to analyze the time-of-day dynamics of interpersonal contacts and examined their complex relationship with other activity-travel time allocation and personal accessibility dynamics. In total, 2,942 activity-travel diaries from 1,471 participants were used to identify five unique patterns of daily human interaction, with latent class analysis. Latent classes for the time-of-day dynamics of time allocation to activities and the time-of-day dynamics of experienced business employment density are also estimated independently and correlated with the five human interaction patterns. The analysis used a form of multinomial regression model (also called a three-step model in latent class analysis) to examine these relationships and to test the association of human interaction patterns and external explanatory variables simultaneously. Strong correlation was found between interpersonal contact patterns and activity participation patterns, along with day of the week, gender, and age. Other sociodemographic indicators and business employment density only partially explain these dynamics.