No abstract

“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.

This paper uses movement as a marker to study interactions in humans and animals to better understand their collective behaviors. Interaction is an important driving force in social and ecological systems. It can also play a significant role in the transmission of infectious diseases and viruses as witnessed during the ongoing COVID-19 pandemic. Although a number of approaches have been developed to analyze interaction using movement data sets, these methods mainly capture concurrent and dyadic interaction (i.e. when two individuals have direct contact or move synchronously in the spatial proximity of each other). Less work has been done on tracing interaction between multi-

Many studies have examined the impact that the built environment has on physical activity. Much existing research posits that if communities will provide and improve active infrastructure such as trails, sidewalks, and bike lanes, people will become more physically active. However, most of these studies have used cross-sectional methods that have allowed them to establish correlations but not behavioral causality. In this pilot project, a longitudinal design is used to assess a trail construction impact on active travel behavior and overall physical activity among suburban residents. A sample of suburban residents in West Valley City, Utah, was surveyed both before and after the construction of a Class 1 trail in their neighborhood by means of a preliminary household survey, individual activity diaries completed at three preassigned time points (before and twice after the trail's construction), new-resident surveys, and a trail user's intercept survey. This intervention technique (the intercept survey) performed a more direct test of causality by looking at the same group of residents over time and analyzing whether individual changes in behavior occurred following the construction of the trail. The paper shows that trail neighborhood residents did not use the facility after it was built, new residents did not move to the neighborhood because of the trail, and users of the trail came from elsewhere. It also discusses trail amenities that appear to be the more desirable ones.

Abstract：Approximately one-fifth of Perth's population is aged 60 or older. Projections suggest that this proportion will continue to increase as a result of the large number of children born after the World War II (1946)(1947)(1948)(1949)(1950)(1951)(1952)(1953)(1954)(1955)(1956)(1957)(1958)(1959)(1960)(1961)(1962)(1963)(1964). Access to and accessibility around train stations for the aging population is and will become a more important issue as the elderly population continues to grow. The aim of the paper is to develop and apply a new measure of accessibility to train stations at a fine spatial scale, justified by the special circumstance of the elderly using a case study in Perth, Western Australia. Intercept surveys are used to collect data on factors affecting train station accessibility for patrons aged 60 years or older, at seven highly dispersed train stations. Overall accessibility is measured separately using a composite index based on three travel modes (walk-and-ride, park-and-ride and bus-and-ride). The results illustrate that key variables, such as distance from an origin to a station, walking or driving route directness, land-use diversity, service and facility quality, bus connection to train stations, all affect the accessibility to train stations for the elderly. This implies that improvements to these factors will improve accessibility for this population group.

This paper develops and estimates a Multiple Discrete Continuous Extreme Value (MDCEV) model of household activity generation that jointly predicts the activity participation decisions of all individuals in a household by activity purpose and the precise combination of individuals participating. The model is estimated on a sample obtained from the Post Census Regional Household Travel Survey conducted by the South California Association of Governments (SCAG) in the year 2000. A host of household, individual, and residential neighborhood accessibility measures are used as explanatory variables. The results reveal that, in addition to household and individual demographics, the built environment of the home zone also impacts the activity participation levels and durations of households. A validation exercise is undertaken to evaluate the ability of the proposed model to predict participation levels and durations. In addition to providing richness in behavioral detail, the model can be easily embedded in an activity-based microsimulation framework and is computationally efficient as it obviates the need for several hierarchical sub-models typically used in extant activity-based systems to generate activity patterns.Keywords: Intrahousehold interactions, joint activity participation, multiple-discreteness, activity-based travel demand modeling 1 INTRODUCTIONThe emphasis of the activity-based approach to travel modeling is on activity participation and scheduling over a specified time period (usually a weekday in the U.S.), with travel being viewed as a derivative of out-of-home activity participation and scheduling decisions. While the detailed structures of activity-based models (ABMs) vary substantially, it is typical for ABMs to model "mandatory" activity decisions such as out-of-home work-related decisions (employed or not, duration of work, location of work, and timing of work) and education-related decisions (student or not, duration of study, location of study, and timing of study) as precursors to the generation of out-of-home non-work activity participations and the overall activity-travel schedules of individuals (including the scheduling of work and non-work episodes). Within the context of the generation of out-of-home non-work activity participation, while early activity-based travel studies ignored the interactions between individuals within a household (see, for example, Mannering et al., 1994, Lu andPas, 1999), more recent studies and models have emphasized the need to explicitly consider such interactions and model joint activity participations within a household. This is motivated by several considerations. First, individuals within a household usually do not make their activity engagement decisions in isolation. As articulated by Gliebe and Koppelman (2002) and Kapur and Bhat (2007), an individual's activity participation decisions are likely to be dependent on other members of the household because of the possible sharing of household maintenance responsibilities, joint activity participat...

Travel behavior, Immigrants, Latent class cluster analysis, Commuting,

Recent research suggests that, besides traditional sociodemographic and built environment attributes, the attitudes and perceptions of parents toward walking and bicycling play a crucial role in deciding which travel modes children take to school. However, little is known about the factors that shape these parental attitudes. The current study aims to investigate this unexplored avenue of research and to identify the influences on parental attitudes toward children walking and bicycling to school as part of a larger nationwide effort to make children more physically active and combat rising trends of childhood obesity in the United States. Through the use of a multivariate ordered response model (a model structure that allows different attitudes to be correlated), the current study analyzes five parental attitudes toward children walking and bicycling to school on the basis of data drawn from the California add-on sample of the 2009 National Household Travel Survey. In particular, the subsample from the Los Angeles–Riverside–Orange County area is used in this study to take advantage of a rich set of microaccessibility measures that are available for this region. It is found that school accessibility, work patterns, current mode use in the household, and sociodemographic characteristics shape parental attitudes toward children walking and bicycling to school. The study findings provide insights on policies, strategies, and campaigns that may help shift parental attitudes to be more favorable toward children walking and bicycling to school.