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              <text>Revolutionizing Road Traffic Management and Enforcement: Harnessing AI, ML, and Geospatial Techniques</text>
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              <text>Artificial Intelligence; Data Quality; Ethical Considerations; Geospatial Technologies; Machine Learning; Traffic Management; Urban Mobility</text>
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              <text>This study investigates the synergistic application of Artificial Intelligence (AI), Machine Learning (ML), and Geospatial Technologies in optimizing traffic management systems. Through a mixed-methods research design, it evaluates the potential of these technologies to enhance urban traffic flow and reduce congestion. The research emphasizes the critical importance of data quality, ethical considerations, and the selection of appropriate technological solutions based on specific urban traffic scenarios. Findings highlight the significant role of integrated AI and geospatial analyses in improving traffic predictions and operational efficiency. Future work will focus on developing more sophisticated models that ensure privacy, equity, and adaptability to new transportation trends.   2024 IEEE.</text>
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              <text>Salunke S.J.; Bang S.S.</text>
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              <text>TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/TQCEBT59414.2024.10545190" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/TQCEBT59414.2024.10545190&lt;/a&gt;
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              <text>ISBN: 979-835038427-7</text>
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              <text>Salunke S.J., Christ University, Bengaluru, India; Bang S.S., Christ University, Bengaluru, India</text>
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