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Browsing by Author "Sohrabi, Soheil"

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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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    Quantifying the Benefits and Harms of Connected and Automated Vehicle Technologies to Public Health and Equity
    Dadashova, Bahar; Sohrabi, Soheil; Khreis, Haneen; Sener, Ipek; Zmud, Johanna (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-07)
    Automated Vehicles (AVs) have the potential to improve traffic safety by preventing crashes. The safety implications of AVs can vary across communities with different socioeconomic and demographic characteristics. In this study, we proposed a framework to quantify the potential safety implications of AVs in terms of preventable crashes and fatalities, accounting for some of the safety challenges of AV operation, including AV technologies’ safety effectiveness, system failure risk, and the risk of disengagement from the automated system to manual driving. We further defined an empirical study to examine the proposed framework and investigate inequity in AV potential safety implications. The empirical analysis was conducted using 2017 crash data from the Dallas-Fort Worth, Texas, United States area. The results showed that AVs could potentially prevent up to 50%, 46%, 23%, 6%, and 5% of crashes for automation Levels 5 to 1, respectively. Among advanced driver assistance systems, pedestrian detection, electronic stability control, and lane departure warning showed more significant potential in reducing fatal crashes. We found a U-shaped relationship between the AV-preventable fatalities and household median income and ethnically diverse communities. The findings of this study suggests that low-income and ethnically diverse communities can benefit from AV implementation. The policy recommendations of this research suggest that city and state planning and transportation agencies may consider implementing policies and strategies for making AVs available to low-income and ethnically diverse communities at a lower cost.