This research explores the factors influencing bike-share usage durations in the Detroit Metropolitan Area over two years, focusing on spatial, temporal, and COVID-19-related variables. Using a fully parametric hazard-based duration model with random parameters, we address data heterogeneity and uncover how different conditions affect bike-share trips. Our findings reveal that (a) intense environmental factors such as high traffic stress, poor weather, and high COVID-19 risk levels are associated with shorter trip durations; (b) in contrast, supportive initiatives like memberships, an affordable USD 5 Access Pass, a free one-month pass during the pandemic, and the introduction of new stations are more likely to encourage longer rides; (c) variables like gym closures due to the pandemic, evening hours, and the addition of new stations, which were set as random variables in our model, exhibit both positive and negative relationships with ride durations. A key finding is the 20-minute mark in ride durations, which helps understand user behaviors and trip purposes. This insight aids urban planning by suggesting strategic bike station placements to enhance bike-share system efficiency and meet diverse community needs. Moreover, the pandemic and related policy responses have clearly impacted user behaviors, showing the necessity for adaptable urban transportation strategies in response to external crises. This study not only deepens our understanding of urban mobility dynamics but also underscores the effectiveness of adaptive strategies in promoting sustainable urban transportation.

Active transportation plays a pivotal role in both economic vitality and daily life. Understanding the seamless integration of bikeshare and transit systems is crucial for optimizing urban mobility and promoting accessibility and equity in cities like Detroit which have long faced challenges related to limited access to critical destinations such as employment, education, and healthcare. This study focuses on evaluating user satisfaction with the current public transportation network within the Detroit Metropolitan area, while also identifying future development needs and priorities. To accomplish this, we conducted an extensive survey, estimating both fixed and random parameter ordered probit models for transit and bikeshare systems separately. The findings demonstrate that random parameter ordered probit models provide a superior fit for our dataset. Additionally, socio-demographic factors were found to significantly influence user perceptions regarding the placement of bus stops and bikeshare stations. Moreover, a lack of familiarity with bus stop locations, bikeshare stations, and extended travel times were identified as potential contributors to negative user perceptions. This research not only underscores the importance of active transportation in sustaining a robust economy but also sheds light on key factors influencing user perspectives for future planning and development.

[3:46 PM] Anahita Zahertar This research explores the factors influencing bikeshare usage durations in the Detroit Metropolitan Area over two years, focusing on spatial, temporal, and COVID-19-related variables. Using a fully parametric hazard-based duration model with random parameters, we address data heterogeneity and uncover how different conditions affect bikeshare trips. Our findings reveal that a) intense environmental factors such as high traffic stress, poor weather, and high COVID-19 risk levels are associated with shorter trip durations b) in contrast, supportive initiatives like memberships, an affordable $5 Access Pass, a free one-month pass during the pandemic, and the introduction of new stations have been more likely to encourage longer rides c) furthermore, the study examines how variables like gym closures due to the pandemic, evening hours, and the addition of new stations, which were set as random variables in our model, exhibit both positive and negative relationships with ride durations. A key finding is the 20-minute mark in ride durations, which helps understand user behaviors and trip purposes. This insight aids urban planning by suggesting strategic bike station placements to enhance bikeshare system efficiency and meet diverse community needs. This study not only deepens our understanding of urban mobility dynamics but also underscores the effectiveness of adaptive strategies in promoting sustainable urban transportation.