Shared Autonomous Mobility Services Show Promise for Increasing Access to Employment in Southern California

Workers in Southern California currently face transportationrelated challenges accessing employment opportunities, including but not limited to high parking costs and/or limited parking availability in dense employment and residential areas; long commute distances between residential areas and employment opportunities; and poor transit service quality in many areas. These challenges are particularly burdensome for low-income households that may not have access to a personal vehicle and/or live in jobpoor neighborhoods, as having a personal vehicle may be the only viable way to get to work

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

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

Quantifying the employment accessibility benefits of shared automated vehicle mobility services: Consumer welfare approach using logsums

The goal of this study is to assess and quantify the potential employment accessibility benefits of shared-use automated vehicle (AV) mobility service (SAMS) modes across a large diverse metropolitan region considering heterogeneity in the working population. To meet this goal, this study proposes employing 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. The study further captures heterogeneity of workers using a latent class analysis (LCA) approach to account for different worker clusters valuing different types of employment opportunities differently, in which the socio-demographic characteristics of workers are the LCA model inputs. The accessibility analysis results in Southern California 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

Shared Autonomous Mobility Services Show Promise for Increasing Access to Employment in Southern California

Workers in Southern California currently face transportationrelated challenges accessing employment opportunities, including but not limited to high parking costs and/or limited parking availability in dense employment and residential areas; long commute distances between residential areas and employment opportunities; and poor transit service quality in many areas. These challenges are particularly burdensome for low-income households that may not have access to a personal vehicle and/or live in jobpoor neighborhoods, as having a personal vehicle may be the only viable way to get to work

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