Public transit in the U.S. has an unsettled future. The onset of the COVID-19 pandemic saw a dramatic decline in transit ridership, with agency operations, and user perceptions of safety changing significantly. However, one new factor beyond the control of agencies is playing an outsized role in transit ridership: the shifting employment patterns in the hybrid work era. Indeed, a lasting and widespread adoption of telework has emerged as a key determinant of individual transit behaviors. This study investigates the impact of teleworking on public transit ridership changes across the different transit services in the Chicago area during the pandemic, employing a random forest machine learning approach applied to large-scale survey data (n = 5637). The use of ensemble machine learning enables a data-driven investigation that is tailored for each of the three main transit service operators in Chicago (Chicago Transit Authority, Metra, and Pace). The analysis reveals that the number of teleworking days per week is a highly significant predictor of lapsed ridership. As a result, commuter-centric transit modes—such as Metra—saw the greatest declines in ridership during the pandemic. The study's findings highlight the need for transit agencies to adapt to the enduring trend of teleworking, considering its implications for future ridership and transportation equity. Policy recommendations include promoting non-commute transit use and addressing the needs of demographic groups less likely to telework. The study contributes to the understanding of how telework trends influence public transit usage and offers insights for transit agencies navigating the post-pandemic world.

The logistics and delivery industry is undergoing a technology-driven transformation, with robotics, drones, and autonomous vehicles expected to play a key role in meeting the growing challenges of last-mile delivery. To understand the public acceptability of automated parcel delivery options, this U.S. study explores customer preferences for four innovations: autonomous vehicles, aerial drones, sidewalk robots, and bipedal robots. We use an Integrated Nested Choice and Correlated Latent Variable (INCLV) model to reveal substitution effects among automated delivery modes in a sample of U.S. respondents. The study finds that acceptance of automated delivery modes is strongly tied to shipment price and time, underscoring the importance of careful planning and incentives to maximize the trialability of innovative logistics options.

Microtransit provides, dynamic rider-driver matching to serve demand with fewer vehicles and design optimal routes if riders accept to wait to board vehicles at curbside boarding locations. The shift to microtransit calls for new research on user behavior, motivations, and acceptability to understand demand and its role in existing mobility systems. The COVID-19 pandemic context adds an additional layer of complexity. This study investigates the potential demand for microtransit options against the background of the pandemic. We use a pivoted efficient choice experiment to study the decision-making of Israeli public transit and car commuters when offered to use novel microtransit options (sedan vs. passenger van). By estimating commuter group-specific Integrated Choice and Latent Variable models with error component terms for the microtransit alternatives, we investigate the tradeoffs related to traditional fare and travel time attributes, along with microtransit features: walking time to the pickup location, vehicle sharing, waiting time, minimum advanced reservation time, and shelter at designated boarding locations.

This paper studies bike share adoption decisions as a dynamic change process from early contemplation to consolidated user status. This runs counter to the typical representation of mode adoption decisions as an instantaneous shift from pre to post usage. A two-level nested logit model that draws from the stage-of-change framework posited by the Transtheoretical Model is developed to study the adoption process. Using survey data collected from an online U.S. sample (n = 910), the model illustrates how personal, psychosocial, and community-oriented factors influence the probability of transitioning between different levels of readiness to participate in a bike share scheme. The findings suggest that encouraging forward movement in the contemplation-use ladder requires tailored, stage-specific interventions that are likely be overlooked if instead a one-size-fits-all psychological theory is applied to investigate travel behavior. In particular, the intermediate stages encapsulate more flexible (i.e. less habitual) orientation among respondents. The findings are translated to practical interventions, from operations to design and community-initiatives to guide practitioners seeking to promote bike share. The stage-based adoption representation helps to align interventions across the spectrum of user readiness to translate intention into behavior.

Encouraging sustainable mobility patterns is at the forefront of policymaking at all scales of governance as the collective consciousness surrounding climate change con- tinues to expand. Not every community, however, possesses the necessary economic or socio-cultural capital to encourage modal shifts away from private motorized ve- hicles towards active modes. The current literature on ‘soft’ policy emphasizes the importance of tailoring behavior change campaigns to individual or geographic con- text. Yet, there is a lack of insight and appropriate tools to promote active mobility and overcome transport disadvantage from the local community perspective. The current study investigates the promotion of walking and cycling adoption using a series of focus groups with local residents in two geographic communities, namely Chicago’s (1) Humboldt Park neighborhood and (2) suburb of Evanston. The re- search approach combines traditional qualitative discourse analysis with quantitative text-mining tools, namely topic modeling and sentiment analysis. The aim of the analysis is to uncover the local mobility culture, embedded norms and values associ- ated with acceptance of active travel modes in different communities. The analysis uncovers that underserved populations within diverse communities view active mo- bility simultaneously as a necessity and as a symbol of privilege that is sometimes at odds with the local culture. Thereby, this research expands on the walking and cycling literature by providing novel insights regarding the perceived benefits of, and barriers to, equitable promotion of these modes. The mixed methods approach to analyzing community member discourses is translated into policy findings that are either tailored to local context or broadly applicable to curbing automobile domi- nance.