Micromobility systems have now become fixtures in the transportation landscape of major cities in the United States. Bike-share and scooter-share micromobility are an important part of this new shared mobility revolution. Micromobility systems can serve the transportation needs of individuals with low incomes, non-car owners, and people of color – all these groups who are historically disadvantaged and considered to be equity priority groups. Micromobility can also contribute to environmental sustainability in the transportation sector, especially if it disrupts auto-dominated travel behavior. This dissertation consists of six studies that explore the environmental sustainability and social equity implications of micromobility services, separately and together. The studies compiled in this dissertation examine the implications of travel behavior for environmental sustainability through the lens of equity.
Whether micromobility substantially contributes to environmental sustainability and social equity depends in part on who uses these services and who does not. The first study focuses on who adopts micromobility services using data collected from household surveys and bike-share user surveys from the Sacramento, California (US) region. Although previous studies have already documented that socio-demographics influence the adoption of micromobility services, little is known about the effects of mode-related attitudes. Apart from attitudes, the extent of service availability to the individual may also influence adoption. To understand the influence of attitudes on bike-share adoption and use frequency, I used integrated choice and latent variable (ICLV) models. The models show that latent attitudes such as bike affinity and bike social environments significantly and positively influence bike-share adoption with large effect sizes, whereas the car necessity attitude significantly and negatively influences the use frequency with a large effect size. The availability of bike-share in locations frequently visited by an individual significantly and positively influences adoption with a large effect size. However, the availability of micromobility in frequently visited locations does not influence the service use frequency. This study suggests that developing programs to promote more favorable attitudes towards bicycling at the population level in combination with proper rebalancing of shared bikes to ensure equitable availability and access, especially for equity priority groups, can be effective strategies to increase the adoption of bike-share as part of advancing social equity and environmental sustainability efforts.
Another important question is whether micromobility services enable car-light lifestyles by replacing driving. To understand how micromobility replaces car use on daily travel, I conducted the second study using GPS-based travel diary data collected from micromobility users in 48 American cities. From the nationwide data, 34,047 trip chains were extracted from 1,829 survey participants. I conducted descriptive analyses of the trip chains and developed several models to examine the extent to which micromobility services could enable car-light lifestyles. The results suggest that for a subset of users, micromobility services can enable a car-free or car-light day of travel, despite having a car available. In most types of complex chains that include both work-based and non-work-based trips and in super complex chains that have more than seven trips and start with a car trip, the share of car trips is approximately 50% less in chains that include micromobility compared to chains without micromobility. In simple work and simple non-work chains, the share of car trips is 100% less in chains with micromobility compared to chains without micromobility. I developed three models with the following dependent variables: trip-chain mode composition, trip-chain complexity, and choice of car-free chains. Results from the trip-chain mode composition choice model indicate that micromobility was a sizeable component of simple work-based chains, complex chains, and super-complex chains. Results from the trip chain complexity model and the car-free chain model reveal that micromobility users perform longer complex chains and super-complex chains without being entirely reliant on cars. This study demonstrates the importance of considering trip chains rather than individual trips as a unit of analysis to understand the sustainability potential of micromobility services. This study provides further evidence that expanding the promotion of micromobility can contribute to achieving the goal of environmental sustainability in transportation.
Continuing from my second study showing that micromobility systems have the potential to reduce car use for daily travel, I conducted a third study in which I hypothesize that micromobility services may have impacts on long-term car ownership decisions. I used a structural equation model (SEM) in the form of path analysis to empirically examine the impact of micromobility use on car ownership decisions and the relationship between micromobility use and car use. I used individual-level survey data from a large project on micromobility- use that collected data across multiple cities in the United States. The study findings show that individuals living in big and dense cities are more likely than individuals in smaller cities to reduce their car use due to their use of micromobility. The SEM model shows that micromobility use is associated with reduced car use, which in turn translates into decisions to forgo car ownership, although the size of this effect varies by the built environment context. This study provides insights into the environmental consciousness and financial implications (tied to auto ownership) of using micromobility services as the result shows that two main factors that significantly influence micromobility users to reduce car use are environmental concerns and the financial burden of travel. I also found that the effect of micromobility is more prominent in carless households, who are likely to delay the purchase of a household vehicle or not purchase a household vehicle at all. This study suggests that the availability of micromobility can minimize transportation costs, which are the second largest expenditure of American households. By reducing car ownership and associated costs, micromobility serves as a viable mode for addressing long-term environmental sustainability as well as social equity in transportation.
The influence of micromobility on public transit is another important question, as micromobility has the potential both to replace transit and to improve connections to transit. Whether micromobility ultimately hurts or helps transit ridership remains uncertain. I conducted the fourth study to provide insights into this question through analyses of the connection between bike-share use and transit use using data from two sources: an intercept survey of bike-share users and system-level bike-share trips, and transit ridership data from the Sacramento region before the COVID pandemic. The results of the individual-level analysis suggest that people in the Sacramento region are more likely to replace their transit use with bike-share than to use bike-share as a first- or last-mile transit connector. Analysis of the system-level data shows that the number of bike-share trips that begin or end near transit stops is positively associated with transit ridership at those stops, conditional on variables known to directly influence transit ridership. Individual- and system-level analyses lead to different conclusions about the relationship between bike-share and transit, suggesting that reliance on one source of data alone may not provide an accurate assessment of the relationship between bike-share and transit use.
The fifth study looks at micromobility use for different trip purposes and the impact of micromobility on travel behaviors and mode choices by different demographic groups through a social equity lens. I examined how equity priority groups incorporate bike-share into their travel patterns for different travel purposes using data from a two-wave survey of bike-share users and a parallel household survey of residents in the Sacramento region. I found that low-income individuals are less likely to adopt bike-share, but they use the service more frequently than other income groups when they do adopt it. Low-income users, people of color, and non-auto owners are more likely than other groups to frequently use bike-share for many trip purposes. In addition, low-income users, people of color, and non-auto owners would be more severely impacted if the bike-share services were to stop. This study expands our current understanding of bike-share use by equity priority groups and identifies the need for more equitable availability, access, and adoption of bike-share.
Analyzing the different segments of micromobility users within a market can provide cities and micromobility operators with information useful in developing strategies that are tailored to the needs of each segment. In the final (sixth) study, I used data from bike-share user surveys in the Sacramento region to perform market segmentation based on perceptions of the bike-share service, mode use patterns, and bike-share use. The findings of market segmentation analysis show that bike-share is generally adopted by all mode user groups but is used at a higher rate by super multimodal and active multimodal groups. The occasional users of bike-share services are mainly those with higher incomes and individuals who have access to a personal car. Non- and infrequent-personal bike users use bike-share services at a greater rate for different purposes than regular bicyclists, suggesting that bike-share may act as a lever for increasing bike travel for some users. The results also suggest that different segments of bike-share users may have different needs and that they use bike-share for different purposes. With this knowledge, bike-share operators can tailor their marketing strategy for each segment and target segments with similar characteristics who are not yet using the service for future user recruitment to substantially increase overall bike-share use. The method from this study can be applied to analyze the market segment of scooter-share users and to develop tailored policies for growing the service sustainably and equitably.
