“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.

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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.

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