4 research outputs found
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Assessment of the Employment Accessibility Benefits of Shared Autonomous Mobility Services
The goal of this study is to assess and quantify the potential employment accessibility benefits of Shared Autonomous Mobility Service (SAMS) commute modes across a large diverse metropolitan region considering heterogeneity in the working population. To meet this goal, this study employs a welfare-based (i.e. logsum-based) measure of accessibility, obtained via estimating a hierarchical work destination-commute mode choice model. The employment accessibility logsum measure incorporates the spatial distribution of worker residences and employment opportunities, the attributes of the available commute modes, and the characteristics of individual workers. This research further captures heterogeneity of workers using latent class analysis (LCA). The LCA model inputs include the socio-demographic characteristics of workers to subsequently account for different worker clusters valuing different types of employment opportunities differently. The accessibility analysis results indicate: (i) the accessibility benefit differences across latent classes are modest but young workers and low-income workers do see higher benefits than high- and middle-income workers; (ii) there are substantial spatial differences in accessibility benefits with workers living in lower density areas benefiting more than workers living in high-density areas; (iii) nearly all the accessibility benefits come from the SAMS-only mode as opposed to the SAMS+Transit mode; and (iv) the SAMS cost per mile assumption significantly impacts the magnitude of the overall employment accessibility benefits
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What Drives Shared Micromobility Ridership?
Shared micromobility (e.g., e-scooters, bikes, e-bikes) offers moderate-speed, space-efficient, and “carbon-light” mobility, promoting environmental sustainability and healthy travel. While the popularity and use of shared micromobility has grown significantly over the past decade, it represents a small share of total trips in urban areas. To better understand shared micromobility ridership, researchers from across the U.S. and the world have analyzed statistical associations between shared micromobility usage and various explanatory factors, including socio-demographic and -economic attributes, land use and built environment characteristics, surrounding transportation options (e.g., public transit stations), geography (e.g., elevation), and micromobility system characteristics (e.g., station capacity). To understand what these studies collectively mean in terms of expanding shared micromobility usage, we conducted a meta-analysis of 30 empirical studies and then developed robust estimates of factors that encourage ridership across different markets
Recommended from our members
Assessment of the Employment Accessibility Benefits of Shared Autonomous Mobility Services
The goal of this study is to assess and quantify the potential employment accessibility benefits of Shared Autonomous Mobility Service (SAMS) commute modes across a large diverse metropolitan region considering heterogeneity in the working population. To meet this goal, this study employs a welfare-based (i.e. logsum-based) measure of accessibility, obtained via estimating a hierarchical work destination-commute mode choice model. The employment accessibility logsum measure incorporates the spatial distribution of worker residences and employment opportunities, the attributes of the available commute modes, and the characteristics of individual workers. This research further captures heterogeneity of workers using latent class analysis (LCA). The LCA model inputs include the socio-demographic characteristics of workers to subsequently account for different worker clusters valuing different types of employment opportunities differently. The accessibility analysis results indicate: (i) the accessibility benefit differences across latent classes are modest but young workers and low-income workers do see higher benefits than high- and middle-income workers; (ii) there are substantial spatial differences in accessibility benefits with workers living in lower density areas benefiting more than workers living in high-density areas; (iii) nearly all the accessibility benefits come from the SAMS-only mode as opposed to the SAMS+Transit mode; and (iv) the SAMS cost per mile assumption significantly impacts the magnitude of the overall employment accessibility benefits
Mental Health Problems and Onset of Tobacco Use Among 12- to 24-Year-Olds in the PATH Study
Objective: To examine whether mental health problems predict incident use of 12 different tobacco products in a nationally representative sample of youth and young adults.
Method: This study analyzed Wave (W) 1 and W2 data from 10,533 12- to 24-year-old W1 never tobacco users in the Population Assessment of Tobacco and Health (PATH) Study. Self-reported lifetime internalizing and externalizing symptoms were assessed at W1. Past 12-month use of cigarettes, electronic nicotine delivery systems (ENDS), traditional cigars, cigarillos, filtered cigars, pipe, hookah, snus pouches, other smokeless tobacco, bidis and kreteks (youth only), and dissolvable tobacco was assessed at W2.
Results:In multivariable regression analyses, high-severity W1 interalizing (adjusted odds ratio [AOR] = 1.5, 95% CI = 1.3 - 1.8) and externalizing (AOR=1.3, 95% CI=1.1-1.5) problems predicted W2 onset of any tobacco use compared to no/low/moderate severity. High-severity W1 internalizing problems predicted W2 use onset across most tobacco products. High-severity W1 externalizing problems predicted onset of any tabacco (AOR=1.6, 95% C1=1.3-1.8), cigarettes (AOR=1.4, 95% CI=1.0-2.0), ENDS (AOR=1.8, 95& CI=1.5-2.1), and cigarillos (AOR=1.5, 95% CI=1.0-2.1) among youth only.
Conclusion: Internalizing and externalizing problems predicted onset of any tobacco use. However, findings differed for internalizing and exter- nalizing problems across tobacco products, and by age, gender, and race/ethnicity. In addition to screening for tobacco product use, health care providers should screen for a range of mental health problems as a predictor of tobacco use. Interventions addressing mental health problems may prevent youth from initiating tobacco use