Investigating social media spatiotemporal transferability for transport

Social Media have increasingly provided data about the movement of people in cities making them useful in understanding the daily life of people in different geographies. Particularly useful for travel analysis is when Social Media users allow (voluntarily or not) tracing their movement using geotagged information of their communication with these online platforms. In this paper we use geotagged tweets from 10 cities in the European Union and United States of America to extract spatiotemporal patterns, study differences and commonalities among these cities, and explore the nature of user location recurrence. The analysis here shows the distinction between residents and tourists is fundamental for the development of city-wide models. Identification of repeated rates of location (recurrence) can be used to define activity spaces. Differences and similarities across different geographies emerge from this analysis in terms of local distributions but also in terms of the worldwide reach among the cities explored here. The comparison of the temporal signature between geotagged and non-geotagged tweets also shows similar temporal distributions that capture in essence city rhythms of tweets and activity spaces

Spatial analysis of access to and accessibility to surrounding train stations: a case study of accessibility for the elderly in Perth, Western Australia

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 (1946–1964). 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 anew 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

Active Travel Behavior

Physical inactivity has become a dominant feature of most American’s lives over the past quarter century. This has spurred an entire research domain straddling several different disciplines. Although model development within the field of travel behavior as a whole continues today with more momentum than ever, the focus on active mode choice has largely been overlooked and left to a small fragment of transportation and public health researchers. Research regarding active mode choice has been primarily conducted outside the field of travel behavior and has utilized research methods designed for other purposes. This leads to results which address behavioral causality in a superficial way while also neglecting the role of residential self-selection. This paper provides an overview of existing travel behavior analysis regarding active mode choice, presents potential threats to validity in this type of research, and critiques existing intervention methodologies. Additionally, a conceptual model of active travel behavior is presented and the roots of each component are discussed. By applying the rigor of travel behavior research to the subfield of active travel behavior research, and incorporating the conceptual model provided, great strides can be made relatively quickly in understanding animate mode choice and active travel behavior

EXPLORATORY LONGITUDINAL ANALYSIS OF SOLO AND JOINT TRIP MAKING USING THE PUGET SOUND TRANSPORTATION PANEL

The Puget Sound Transportation Panel is used to study the propensity of people to make solo and joint trips. The analysis consists of trip-based models in which solo versus joint trip making is explained in terms of person and household sociodemographics, daily activity and travel patterns, dwelling unit and workplace level of service and land use characteristics, and trip attributes. The analysis is repeated for all the days and all the waves (years) of the panel at hand. The analysis reveals that the major factor that determines joint trip making is the life-cycle stage of the household. It is also observed consistently across all the waves that, as the age of the person increases, the number of joint trips he or she makes also increases. Other factors that consistently affect joint trip making are household size, age of the household members, number of vehicles in the household, daily activity and travel patterns, and certain accessibility measures such as the access from transit to auto

Evaluating the Impact of Neighborhood Trail Development on Active Travel Behavior and Overall Physical Activity

Many studies have examined the impact that the built environment has on physical activity, and much of the 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 which have allowed them to establish correlations but not behavioral causality. In this pilot project a longitudinal design is used to evaluate the impact neighborhood trail development 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-one trail in their neighborhood using a preliminary household survey, individual activity diaries completed at three pre-assigned time points (before and twice after the trail’s construction), new resident surveys, and a trail user’s intercept survey. This intervention technique performs a more direct test of causality by looking at the same group of residents over time and analyzing if individual changes in behavior occur following the construction of the trail. In the paper we show that trail neighborhood residents did not use the facility after it was build, new residents did not move to the neighborhood because of the trail, and the users of the trail come from elsewhere. We also report trail amenities that appear to be more desirable

ACTIVE TRAVEL BEHAVIOR

Board only. This paper has been submitted for publication in Transportation Letters (July 2008) and therefore does not conform to the word count and citation requirements specified for Physical inactivity has become a dominant feature of most American’s lives over the past quarter century. This has spurred an entire research domain straddling several different disciplines. Although model development within the field of travel behavior as a whole continues today with more momentum than ever, the focus on active mode choice has largely been overlooked and left to a small fragment of transportation and public health researchers. Research regarding active mode choice has been primarily conducted outside the field of travel behavior and has utilized research methods designed for other purposes. This leads to results which address behavioral causality in a superficial way while also neglecting the role of residential self-selection. This paper provides an overview of existing travel behavior analysis regarding active mode choice, presents potential threats to validity in this type of research, and critiques existing intervention methodologies. Additionally, a conceptual model of active travel behavior is presented and the roots of each component are discussed. By applying the rigor of travel behavior research to the subfield of active travel behavior research, and incorporating the conceptual model provided, great strides can be made relatively quickly in understanding animate mode choice and active travel behavior.