Browsing by Author "Ye, Xinyue"

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    Autonomous Vehicles for Small Towns: Exploring Perception, Accessibility, and Safety
    Li, Wei; Ye, Xinyue; Li, Xiao; Dadashova, Bahar; Ory, Marcia G.; Lee, Chanam; Rathinam, Sivakumar; Usman, Muhammad; Chen, Andong; Bian, Jiahe; Li, Shuojia; Du, Jiaxin (Safe-D University Transportation Center, 2023-09)
    As of 2021, there were 18,696 small towns in the US with a population of less than 50,000. These communities typically have a low population density, few public transport services, and limited accessibility to daily services. This can pose significant challenges for residents trying to fulfill essential travel needs and access healthcare. Autonomous vehicles (AVs) have the potential to provide a convenient and safe way to get around without requiring human drivers, making them a promising transportation solution for these small towns. AV technology can become a first-line mobility option for people who are unable to drive, such as older adults and those with disabilities, while also reducing the cost of transportation for both individuals with special needs and municipalities. The report includes our research findings on 1) how residents in small towns perceive AV, including both positive and negative aspects; 2) the impacts of ENDEAVRide—a novel “Transport + Telemedicine 2-in-1” microtransit service delivered on a self-driving van in central Texas—on older adults’ travel and quality of life; and 3) the potential safety implications of AVs in small towns. This report will help municipal leaders, transportation professionals, and researchers gain a better understanding of how AV deployment can serve small towns.
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    Building Equitable Safe Streets for All: Data-Driven Approach and Computational Tools
    Dadashova, Bahar; Zhu, Chunwu; Ye, Xinyue; Sohrabi, Soheil; Brown, Charles; Potts, Ingrid (Safe-D University Transportation Center, 2023-09)
    Roadway safety in low-income and ethnically diverse U.S. communities has long been a major concern. This research was designed to address this issue by developing a data-driven approach and computational tools to quantify equity issues in roadway safety. This report employed data from Houston, Texas, to explore (1) the relationship between road infrastructure and communities’ socioeconomic and demographic characteristics and its association with traffic safety in low-income, ethnically diverse communities and (2) the type of driver behaviors and characteristics that affect crash risks in underserved communities. The team first built an inclusive road infrastructure inventory database by employing remote sensing and image processing techniques. Then, the relationship between communities’ socioeconomic and demographic characteristics and traffic safety was investigated through the lens of road infrastructure characteristics using data mining, deep learning tools, and statistical and econometric models. Clustering analysis was used to uncover the role in underserved communities of socioeconomic and demographic characteristics of drivers and victims involved in crashes. Structural equation models were then used to explore the association between neighborhood disadvantage, transportation infrastructure, and roadway crashes. Findings shed light on road safety inequity and sources of these disparities among communities using data-driven methods.
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    Developing AI-Driven Safe Navigation Tool
    Das, Subasish; Sohrabi, Soheil; Tsapakis, Ioannis; Ye, Xinyue; Weng, Yanmo; Li, Shoujia; Torbic, Darren (Safe-D University Transportation Center, 2023-09)
    Popular navigation applications such as Google Maps and Apple Maps provide distance-based or travel timebased alternative routes with no real-time risk scoring. There is a need for a real-time navigation system that can provide the data-driven decision on the safest path or route. By leveraging data from a diverse range of historical and real-time sources, this study successfully developed a user interface for a navigation tool or application that offers informed and data-driven decisions regarding the safest navigation options. The interface considers multiple scoring factors, including safety, distance, travel time, and an overall scoring metric. This study made a distinctive and valuable contribution by designing and implementing a robust safe navigation tool driven by artificial intelligence. Unlike existing navigation tools that offer multiple uninformed route options, this tool provides users with an informed decision on the safest route. By leveraging advanced AI algorithms and integrating various data sources, this navigation tool enhances the accuracy and reliability of route selection, thereby improving overall road safety and ensuring users can make informed decisions for their journeys.
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    The Implications of Human Mobility and Accessibility for Transportation and Livable Cities
    Sanchez, Thomas W.; Ye, Xinyue (MDPI, 2023-10-12)
    Understanding human movement and transportation accessibility has become paramount in shaping the very fabric of our communities [...]