Browsing by Author "Farag, Mohamed"

Now showing 1 - 4 of 4
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    Electric versus Gasoline Vehicle Particulate Matter and Greenhouse Gas Emissions: Large-scale Analysis
    Rakha, Hesham A.; Farag, Mohamed; Foroutan, Hosein (2024-07-31)
    This study addresses the contentious issue of non-exhaust particulate matter (PM) emissions from battery electric vehicles (BEVs) compared to internal combustion engine vehicles (ICEVs) by developing models to quantify tire and brake PM emissions and incorporate them in a microscopic traffic simulation environment. Furthermore, exhaust greenhouse gas (GHG) emissions are quantified to develop a comprehensive picture of vehicle network emissions. The key findings are: 1) BEVs emit more tire and less brake PM emissions, thus necessitating a comprehensive analysis to avoid erroneous conclusions. 2) If at least 15% of travel is city driving, BEVs produce less non-exhaust PM emissions. 3) For the freeway section analyzed, a volume-to-capacity ratio of at least 0.25 is required for BEVs to produce less non-exhaust PM emissions. By incorporating these detailed models into traffic simulations, the study provides a tool for policymakers to better understand and manage vehicle emissions at a city level.
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    Evaluating Factors Contributing to Crash Severity Among Older Drivers: Statistical Modeling and Machine Learning Approaches
    Alrumaidhi, Mubarak S. M. S. (Virginia Tech, 2024-02-23)
    Road crashes pose a significant public health issue worldwide, often leading to severe injuries and fatalities. This dissertation embarks on a comprehensive examination of the factors affecting road crash severity, with a special focus on older drivers and the unique challenges introduced by the COVID-19 pandemic. Utilizing a dataset from Virginia, USA, the research integrates advanced statistical methods and machine learning techniques to dissect this critical issue from multiple angles. The initial study within the dissertation employs multilevel ordinal logistic regression to assess crash severity among older drivers, revealing the complex interplay of various factors such as crash type, road attributes, and driver behavior. It highlights the increased risk of severe crashes associated with head-on collisions, driver distraction or impairment, and the non-use of seat belts, specifically affecting older drivers. These findings are pivotal in understanding the unique vulnerabilities of this demographic on the road. Furthermore, the dissertation explores the efficacy of both parametric and non-parametric machine learning models in predicting crash severity. It emphasizes the innovative use of synthetic resampling techniques, particularly random over-sampling examples (ROSE) and synthetic minority over-sampling technique (SMOTE), to address class imbalances. This methodological advancement not only improves the accuracy of crash severity predictions for severe crashes but also offers a comprehensive understanding of diverse factors, including environmental and roadway characteristics. Additionally, the dissertation examines the influence of the COVID-19 pandemic on road safety, revealing a paradoxical decrease in overall traffic crashes accompanied by an increase in the rate of severe injuries. This finding underscores the pandemic's transformative effect on driving behaviors and patterns, heightening risks for vulnerable road users like pedestrians and cyclists. The study calls for adaptable road safety strategies responsive to global challenges and societal shifts. Collectively, the studies within this dissertation contribute substantially to transportation safety research. They demonstrate the complex nature of factors influencing crash severity and the efficacy of tailored approaches in addressing these challenges. The integration of advanced statistical methods with machine learning techniques offers a profound understanding of crash dynamics and sets a new benchmark for future research in transportation safety. This dissertation underscores the evolving challenges in road safety, especially amidst demographic shifts and global crises, and advocates for adaptive, evidence-based strategies to enhance road safety for all, particularly vulnerable groups like the older drivers.
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    Impacts of Vehicle-to-Everything Enabled Applications: Literature Review of Existing Studies
    Du, Jianhe; Ahn, Kyoungho; Farag, Mohamed; Rakha, Hesham A. (Universal Wiser Publisher, 2023-03-10)
    As communication technology is developing at a rapid pace, connected vehicles (CVs) can potentially enhance vehicle safety while reducing vehicle energy consumption and emissions via data sharing. Many researchers have attempted to quantify the impacts of such CV applications and vehicle-to-everything (V2X) communication, or the instant and accurate communication among vehicles, devices, pedestrians, infrastructure, network, cloud, and grid. Cellular V2X (C-V2X) has gained interest as an efficient method for this data sharing. In releases 14 and 15, C-V2X uses 4G LTE technology, and in release 16, it uses the latest 5G new radio (NR) technology. Among its benefits, C-V2X can function even with no network infrastructure coverage; in addition, C-V2X surpasses older technologies in terms of communication range, latency, and data rates. Highly efficient information interchange in a CV environment can provide timely data to enhance the transportation system's capacity, and it can support applications that improve vehicle safety and minimize negative impacts on the environment. Achieving the full benefits of CVs requires rigorous investigation into the effectiveness, strengths, and weaknesses of different CV applications. It also calls for deeper understanding of the communication protocols, results with different CV market penetration rates (MPRs), CV- and human-driven vehicle interactions, integration of multiple applications, and errors and latencies associated with data communication. This paper includes a review of existing literature on the safety, mobility, and environmental impacts of CV applications; gaps in current CV research; and recommended directions for future research. The results of this paper will help shape future research for CV applications to realize their full potential.
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    Parking Spaces Occupancy Prediction
    Farag, Mohamed; Marcelin, Josh; Hanumaiah, Adi; Lau, Antonio; He, Kevin; Kwon, Eugene (2023-11-30)
    Across Virginia Tech’s campus, finding parking is consistently a source of frustration for students and faculty. During peak hours, locating free parking spots becomes a challenging task; leading to significant delays and increased traffic around campus. Leveraging modern data-driven technologies such as Smart City infrastructure and Intelligent Transportation, we can alleviate some of the school’s congestion and enhance the parking experience for Virginia Tech residents. The proposed solution is a web app that users can integrate into their daily commute. With the help of live data, the app will give real-time parking recommendations as well various other helpful insights. It will analyze the live data at each of the garages, to predict the occupancy of the garages at a given time of arrival. Machine learning will allow us to estimate the occupancy of each of the garages a given time into the future, depending on the distance to each garage, and provide a recommendation for which garage to target. The application will also allow for more effective collection of data for parking services and could eventually take into account more factors such as schedules and live traffic.