Intention to use light-rail transit in Houston, Texas, United States: Findings from the Travel-Related Activity in Neighborhoods study.

Using data from the Houston Travel-Related Activity in Neighborhoods (TRAIN) study, this study examined how various factors affect whether individuals intend to use newly opened light-rail transit (LRT) lines in Houston. The Houston TRAIN study is a natural experiment on the effect of new LRT lines on both transit use and physical activity. A mixed binary logit model was developed based on a dichotomous dependent variable and rich set of variables, including sociodemographic factors, health status, travel behavior and technology, and attitudes and perceptions. The mixed model also allowed accounting for the unobserved heterogeneity across individuals in their sensitivity to observed variables. The results indicated the important role of various factors influencing the decision on intent to use the new LRT lines. In general, demographics mattered but to a lower extent than psychological or personality-related variables. For example, attitudes and perceptions toward the public transit system and consciousness of physical activities derived by using public transit were important factors. Personal health constraints negatively influenced intention to use, while experience with the public transport system was among the positive indicators. The findings show the potential of future interventions in this community to promote use of the new system, such as educational campaigns that improve perceptions of public transit use and clarify the benefits of being active. While providing growing evidence that cognitive variables are important in measuring intention to use public transit, the results emphasize the positive role of efforts integrating transportation and health to develop effective and sustainable solutions.

A Bayesian spatial Poisson-lognormal model to examine pedestrian crash severity at signalized intersections.

Reducing nonmotorized crashes requires a profound understanding of the causes and consequences of the crashes at the facility level. Generally, existing literature on bicyclists and pedestrian crash models suffers from two distinct problems: lack of exposure/volume data and inadequacy in capturing potential correlations across various crash aspects. To develop a robust framework for pedestrian crash analysis, this research employed a multivariate model across multiple pedestrian crash severities incorporating a crucial piece of information: pedestrian exposure. A multivariate spatial (conditional autoregressive) Poisson-lognormal model in a Bayesian framework was developed to examine the significant factors influencing the fatal, incapacitating injury (or suspected serious injury), and non-incapacitating injury pedestrian crashes at 409 signalized intersections in the Austin area. Various explanatory variables were used to examine the pedestrian crashes, including traffic characteristics, road geometry, built environment features, and pedestrian exposure volume at intersections, which was estimated through a direct demand model as part of the study. Model results revealed valuable insights. The superior performance of the multivariate model over the univariate model emphasized the need to jointly model multiple pedestrian crash severities. The results showed the significant positive influence of speed limit on fatal pedestrian crashes and revealed that both incapacitating and non-incapacitating injury crashes increase with increasing motorized traffic volume. Bus stop presence was found to have a negative influence on incapacitating injury crashes and a positive influence on non-incapacitating injury crashes. Moreover, the pedestrian volume at intersections positively influences non-incapacitating injury crashes. The difference in influence across crash types warrants careful and focused policy design of intersections to reduce pedestrian crashes of all severity types.

Foot-based audit of streets adjacent to new light rail stations in Houston, Texas: measurement of health-related characteristics of the built environment for physical activity research.

BACKGROUND: Active travel to and from a transit station may provide significant amounts of physical activity and improve health. The ease with which people can traverse the distance to the transit station may impede or support active travel. Therefore, transit stations that have features that are supportive of utilitarian physical activity would be desirable. This study aimed to characterize the built environment surrounding new light rail transit (LRT) stations in the City of Houston, Texas. METHODS: In 2014, we used a series of systematic protocols and a standardized environmental audit instrument, the Analytic Audit Tool, to collect data on segments (streets) that surround 22 LRT stations that were being newly built. Using Geographic Information System (GIS), we assembled all the segments that intersect a 0.25-mile circular buffer around each station for the audit exercise. Several 3- to 4-member teams of trained auditors completed the audit exercise on a subset of these identified segments. Our analysis were descriptive in nature. We provided the frequency distributions of audited features across the study area. We also followed an original algorithm to produce several composite index scores for our study area. The composite index score is indicative of the prevalence of physical activity friendly/unfriendly features in the study area. RESULTS: In all, we audited a total of 590 segments covering a total of 218 US Census blocks, and eight City of Houston super neighborhoods. Findings suggest the environment around the new LRT stations may not be supportive of physical activity. In general, the audited segments lacked land use integration; had abandoned buildings, had uneven sidewalks; were not bike-friendly, had minimal presence of public-recreational facilities that would support physical activity; and had significant physical disorder. Notably, certain attractive and comfort features were frequently to usually available. CONCLUSIONS: Current findings, which will be compared to follow-up audit data, can be useful for future researchers and practitioners interested in the built environment around LRT stations.

Understanding Potential Exposure of Bicyclists on Roadways to Traffic-Related Air Pollution: Findings from El Paso, Texas, Using Strava Metro Data.

As bicycling on roadways can cause adverse health effects, there is an urgent need to understand how bicycle routes expose bicyclists to traffic emissions. Limited resources for monitoring reveal that bicycle travel patterns may constrain such understanding at the network level. This study examined the potential exposure of bicyclists to traffic-related air pollution in El Paso, Texas, using Strava Metro data that revealed bicycle patterns across the city networks. An initial spatial mapping analysis was conducted to explore the spatial patterns of bicycling and traffic pollutant emission, followed by exploratory descriptive statistics. A spatial bicycle model was then developed to explore factors influencing bicycling activity in El Paso. Analysis results indicated significant associations between greater bicycle volume and both higher levels of particulate matter (PM2.5) emissions and more frequent bus services, implying adverse health concerns related to traffic-related air pollution. The results also indicated significant effects of various environmental characteristics (e.g., roadway, bicycle infrastructure, topography, and demographics) on bicycling. The findings encourage extending this study to provide guidance to bicyclists whose regular trips take place on heavily trafficked roads and during rush hours in this region and to evaluate the net health impacts of on-road bicycling for the general population.

Active Travel Behavior in a Border Region of Texas and New Mexico: Motivators, Deterrents, and Characteristics.

BACKGROUND: Active travel has been linked with improved transportation and health outcomes, such as reduced traffic congestion and air pollution, improved mobility, accessibility, and equity, and increased physical and mental health. The purpose of this study was to better understand active travel characteristics, motivators, and deterrents in the El Paso, TX, region. METHODS: A multimodal transportation survey brought together elements of transportation and health, with a focus on attitudinal characteristics. The analysis consisted of an initial descriptive analysis, spatial analysis, and multivariate binary and ordered-response models of walking and bicycling behavior. RESULTS: The motivators and deterrents of active travel differed for walkers, bicyclists, and noncyclists interested in bicycling. The link between active travel and life satisfaction was moderated by age, with a negative association for older travelers. This effect was stronger for bicycling than it was for walking. CONCLUSIONS: Based on the findings, several interventions to encourage walking and bicycling were suggested. These included infrastructure and built environment enhancements, workplace programs, and interventions targeting specific subpopulations.

Transportation Planning and Quality of Life: Where Do They Intersect?

Policy makers and researchers are increasingly recognizing the connections between public health and transportation, but health improvements are typically framed from a physical health perspective rather than considering broader quality of life (QOL) impacts. Currently, there is a limited understanding of the ways in which transportation and QOL intersect, and little is known about how metropolitan planning organizations (MPOs) in the United States are addressing QOL outcomes. This study addressed these gaps by developing a conceptual framework holistically linking transportation to QOL. The proposed framework identified four transportation-related QOL dimensions-physical, mental, social, and economic well-being-which are predominantly influenced by three components of the transportation system: mobility/accessibility, the built environment, and vehicle traffic. This framework then formed the basis for a content analysis of 148 long-range transportation plans in the United States to evaluate the extent to which QOL is being considered in the planning process. The results of the analysis and a follow-up examination of 13 plans revealed that MPOs are inconsistently addressing QOL. Plans primarily targeted QOL enhancement from the perspective of physical well-being, while mental and social well-being were rarely considered. Policy recommendations were provided to more comprehensively integrate QOL into the transportation planning process.

The Association of Trip Distance With Walking To Reach Public Transit: Data from the California Household Travel Survey.

INTRODUCTION: Use of public transit is cited as a way to help individuals incorporate regular physical activity into their day. As a novel research topic, however, there is much we do not know. The aim of this analysis was to identify the correlation between distance to a transit stop and the probability it will be accessed by walking. We also sought to understand if this relation was moderated by trip, personal or household factors. METHODS: Data from the 2012 California Household Travel Survey was used for this cross-sectional analysis. 2,573 individuals were included, representing 6,949 transit trips. Generalized estimating equations modeled the probability of actively accessing public transit as a function of distance from origin to transit stop, and multiple trip, personal and household variables. Analyses were conducted in 2014 and 2015. RESULTS: For each mile increase in distance from the point of origin to the transit stop, the probability of active access decreased by 12%. With other factors held equal, at two miles from a transit stop there is a 50% chance someone will walk to a stop versus non-active means. The distance-walking relation was modified by month the trips were taken. CONCLUSIONS: Individuals appear to be willing to walk further to reach transit than existing guidelines indicate. This implies that for any given transit stop, the zone of potential riders who will walk to reach transit is relatively large. Future research should clarify who transit-related walkers are, and why some are more willing to walk longer distances to transit than others.

The Effect of Light Rail Transit on Physical Activity: Design and Methods of the Travel-Related Activity in Neighborhoods Study.

BACKGROUND: Use of mass transit has been proposed as a way to incorporate regular physical activity into daily life because transit use typically requires additional travel to access and depart the stop or station. If this additional travel is active, a small but potentially important amount of physical activity can be achieved daily. Although prior research has shown that transit use is associated with physical activity, important questions remain unanswered. Utilizing a major expansion of the Houston, TX, USA light-rail system as a natural experiment, the Houston Travel-Related Activity in Neighborhoods (TRAIN) Study was developed to address these unanswered questions. PURPOSE: The purpose of the TRAIN Study is to determine if the development of light-rail lines in Houston, TX, USA will prospectively affect both transit use and physical activity over 4 years. We also aim to understand how contextual effects (i.e., moderators or interaction effects), such as the neighborhood built environment and socioeconomic factors, affect the primary relations under study. METHODS: The TRAIN Study is a longitudinal cohort design, in which participants are recruited at baseline from a 3-mile buffer around each of the three new lines and measured annually four times. Recruitment is accomplished via telephone contact, ads in newspapers and advertising circulars, and targeted community outreach. Data are collected via mail and include questionnaire-assessed factors, such as perceived neighborhood characteristics, attitudes about transportation, demographics, and reported physical activity; a travel diary; and accelerometry. Additionally, field-based neighborhood audits are conducted to capture micro-scale environmental features. To assess macro-scale environmental characteristics, we utilize GIS mapping and spatial analyses. Statistical analyses will be conducted using latent growth curve modeling and discrete choice models, with a focus on identifying moderating factors (i.e., statistical interaction effects). Selection bias will be controlled via propensity score analysis. CONCLUSION: The TRAIN study is a unique opportunity to study how a multi-billion dollar investment in mass transit can simultaneously affect transportation needs and physical activity behavior. This comprehensive evaluation will provide needed evidence for policy makers, and can inform health impact assessments of future transportation projects around the world.

Potential Health Implications and Health Cost Reductions of Transit-Induced Physical Activity.

Transit has the potential to increase an individual's level of physical activity due to the need to walk or bike at the beginning and end of each trip. Consideration of these health benefits would allow transit proponents to better demonstrate its true costs and benefits. In light of transit's potential health-related impacts, this study contributes to the growing discussion in the emerging field of health and transportation by providing a review of the current level of understanding and evidence related to the physical activity implications of transit use and its associated health cost benefits. Findings from the review revealed that transit use is associated with increased levels of physical activity and improved health outcomes, but the magnitude of these effects is uncertain. There were few studies that estimated the health care cost savings of transit systems, and those that did tended to be imprecise and simplistic. Objective physical activity measures and frequency-based transit measures would allow for greater consistency across studies and help more directly attribute physical activity gains to transit ridership. Additionally, research in this area would benefit from disaggregate estimation techniques and more robust health datasets that can be better linked with existing transit data.