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        <full_title>International Journal of Environmental Engineering and Development</full_title>
        <issn media_type="electronic">2945-1159</issn>
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      <journal_article>
        <titles>
          <title>“Smart Cities” and Green Kilometers in Motion: Technological Frameworks and IoT Integration for Urban Sustainability</title>
        </titles>
        <contributors>
          <person_name sequence="first" contributor_role="author">
            <given_name>Venelin</given_name>
            <surname>Terziev</surname>
            <affiliations>
              <institution>
                <institution_name>Black Sea Institute, Bourgas, BULGARIA</institution_name>
              </institution>
            </affiliations>
          </person_name>
          <person_name sequence="additional" contributor_role="author">
            <given_name>Teodora</given_name>
            <surname>Petrova</surname>
            <affiliations>
              <institution>
                <institution_name>Trakia University-Stara Zagora, BULGARIA</institution_name>
              </institution>
            </affiliations>
          </person_name>
          <person_name sequence="additional" contributor_role="author">
            <given_name>Marin</given_name>
            <surname>Georgiev</surname>
            <affiliations>
              <institution>
                <institution_name>Vitalis Ruse, Ruse, BULGARIA</institution_name>
              </institution>
            </affiliations>
          </person_name>
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        <jats:abstract>
          <jats:p>The rapid evolution of urban environments necessitates the implementation of sophisticated paradigms such as the 'Smart City' to mitigate the adverse effects of hyper-urbanization. This paper evaluates the synergy between Intelligent Transport Systems (ITS) and the Internet of Things (IoT) as a cornerstone for sustainable mobility and enhanced urban quality of life. Unlike conventional traffic management, our approach focuses on the deployment of decentralized intelligent architectures and Multi-Agent Systems (MAS) to facilitate real-time data processing. Through the application of reinforcement learning techniques, specifically Q-learning algorithms, the study demonstrates a significant optimization of traffic flows, leading to a measurable reduction in CO2 emissions and energy consumption. The findings validate the 'green kilometers in motion' framework, illustrating how 'Agents of Things' can transform urban infrastructure into a self-adaptive ecosystem. The results provide a robust empirical basis for integrating advanced IoT-driven solutions into modern urban planning strategies to achieve environmental and economic resilience.</jats:p>
        </jats:abstract>
        <publication_date media_type="print">
          <month>06</month>
          <day>09</day>
          <year>2026</year>
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        <publication_date media_type="online">
          <month>06</month>
          <day>09</day>
          <year>2026</year>
        </publication_date>
        <pages>
          <first_page>39</first_page>
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          <item_number item_number_type="article_number">4</item_number>
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          <doi>10.37394/232033.2026.4.4</doi>
          <resource>https://wseas.com/journals/ijeed/2026/a08ijeed-004(2026).pdf</resource>
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