Projects

Abstract: The study analyzes travel patterns in Houston, Texas during COVID-19 using an autoregressive distributed lag model. Findings reveal that visit patterns and changes in COVID-19 cases from the previous week heavily influence behaviors in the following week. Factors such as unemployment claims, median minimum dwell time, and workplace visit… read more 

Abstract: The SMADS project at the University of Texas at Austin develops software for autonomous robot deliveries on campus. It integrates various subsystems like autonomy, scheduling, interface, and a customer app. The study reports on delivering lemonade outdoors to campus buildings and highlights advancements in integrating end-users and robot autonomy… read more 

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 

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