Abstract: This study explores how street characteristics affect perceived safety among older pedestrians in Shanghai, China, considering the influence of land-use patterns and urban sprawl. Using a mix of quantitative and qualitative methods, 68 elderly urban residents participated in a survey using simulated streetscape images. Findings from ordinal logit regression… read more 

Abstract: In recent years, dockless small vehicles have surged in progressive U.S. cities as a car-free travel alternative for short distances. However, little is known about the social impact of this influx on public space. This study examined 4,100 parking violation reports in Austin, Texas, sourced from the Austin 311… read more 

Abstract: Recent years have witnessed a rise in dockless shared electric scooters (e-scooters) in American cities, offering a solution for short-distance car trips. However, research on e-scooter usage patterns and their urban connection is lacking. This study addressed this gap by analyzing e-scooter ridership in Austin and Minneapolis. Results revealed… read more 

Abstract: The Longitudinal Social Impacts of HRI over Long-Term Deployments Workshop aims to unite researchers focusing on comprehensively understanding such deployments. This includes those studying longitudinal human-robot interaction, long-term autonomy, and real-world deployments. The workshop aims to advance the study of how deployed robot systems influence both individuals interacting with… read more 

Abstract: This study explores the impact of COVID-19 preventive measures, like telework, on individuals in the United States. Using mixed logit models, it analyzes the relationship between socio-economic factors and telework status. Results suggest a significant association between telework and various variables, including age, gender, education, marital status, financial strain,… read more 

Abstract: This study addresses challenges in measuring spatial accessibility to grocery stores accurately. By enhancing an existing radiation model with different transportation modes and time use diaries, we aim to provide a more realistic estimation of accessibility. We introduce a novel approach using survival analysis, specifically the Cox proportional hazard… read more 

Abstract: In this study, we utilized a random forest model to predict the “L” train’s daily ridership in the Chicago downtown area during the pandemic based on environmental, transportation, and COVID-19-related factors. The results indicated that the model accurately predicts ridership one month in advance. However, its accuracy degraded over… read more 

Abstract: This study examines how the COVID-19 pandemic affected public transit ridership in Austin, TX, utilizing data from the Capital Metropolitan Transportation Authority and the American Community Survey. Through multivariate clustering and geographically weighted regression, it identifies demographic and spatial factors influencing ridership declines. Results indicate that areas with older… read more 

Abstract: This study aims to identify factors influencing e-scooter injury accidents in Austin due to concerns about rising ridership and insufficient accident data. Using 2018 dockless e-scooter injury data, we employed zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models. Results indicate the ZIP model better fits the data. Significant… read more 

Abstract: This study investigates the impact of ride-hailing on transportation access, particularly in low-density areas. Using data from Austin, Texas, we analyze ride-hailing usage, transit availability, and vehicle ownership across neighborhoods with varying demographics. Our findings reveal that ride-hailing has become an alternative mode of transport for residents in low-income,… read more