Abstract: The paper introduces a practical method for estimating e-scooter flow patterns using open datasets that track trip origins and destinations. By leveraging this data, the authors demonstrate how their models can assist cities in optimizing support for shared micromobility services. Additionally, the generated information can enhance the analysis of… read more
The impact of street characteristics on older pedestrians’ perceived safety in Shanghai, China
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
From shared micro-mobility to shared responsibility: Using crowdsourcing to understand dockless vehicle violations in Austin, Texas
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
Dockless E-scooter usage patterns and urban built Environments: A comparison study of Austin, TX, and Minneapolis, MN
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
Longitudinal Social Impacts of HRI over Long-Term Deployments
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
Socio-economic Factors and Telework Status in the US during the COVID-19 Pandemic
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
Measuring accessibility to grocery stores using radiation model and survival analysis
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
Toward Equitable Micromobility: Lessons from Austin E-Scooter Sharing Program
Abstract: This study examines the societal impacts of E-scooters on disadvantaged populations in Austin, Texas. Through a population distribution analysis, it compares E-scooter use opportunities and space intrusion burdens among four vulnerable groups. Minority populations experienced fewer E-scooter use opportunities, with a disproportionate wait time for disturbance resolution. Ten percent… read more
Prediction of “L” Train’s Daily Ridership in Downtown Chicago During the COVID-19 Pandemic
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
Disparities in the Impacts of the COVID-19 Pandemic on Public Transit Ridership in Austin, Texas, U.S.A.
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
