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  <Article>
    <Journal>
      <PublisherName>ijesm</PublisherName>
      <JournalTitle>International Journal of Engineering, Science and</JournalTitle>
      <PISSN>I</PISSN>
      <EISSN>S</EISSN>
      <Volume-Issue>volume 14,issue 4,</Volume-Issue>
      <PartNumber/>
      <IssueTopic>Multidisciplinary</IssueTopic>
      <IssueLanguage>English</IssueLanguage>
      <Season>April 2025</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>2025</Year>
        <Month>04</Month>
        <Day>16</Day>
      </PubDate>
      <ArticleType>Engineering, Science and Mathematics</ArticleType>
      <ArticleTitle>QUANTUM INSPIRED AI FOR REAL TIME TRAFFIC FLOW OPTIMIZATION</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>36</FirstPage>
      <LastPage>49</LastPage>
      <AuthorList>
        <Author>
          <FirstName>Ridhi Sharma</FirstName>
          <LastName/>
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
          <FirstName>Palak Dhall</FirstName>
          <LastName/>
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>Y</CorrespondingAuthor>
          <ORCID/>
        </Author>
      </AuthorList>
      <DOI/>
      <Abstract>The exponential rise in urban vehicular density has posed significant challenges to real-time traffic management systems. Conventional Artificial Intelligence (AI) techniques, while effective, often struggle with the dynamic, uncertain, and complex nature of real-time traffic flow. This study explores a novel approach — Quantum-Inspired Artificial Intelligence (QIAI) — to optimize traffic flow in real-time environments. By leveraging principles derived from quantum computing, such as superposition and entanglement, QIAI algorithms offer enhanced parallelism, probabilistic reasoning, and faster convergence in decision-making processes. The proposed framework integrates QIAI with traffic signal control systems and vehicular network data to predict congestion points, dynamically allocate green light intervals, and reroute traffic in real time. Simulation results conducted on urban traffic datasets demonstrate that QIAI outperforms traditional AI models in terms of response time, adaptability, and overall reduction in traffic congestion.</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords>Quantum-Inspired Artificial Intelligence, Smart Transportation Systems, Quantum Computing, Traffic Flow Prediction, Intelligent Traffic Control, Urban Mobility, Quantum Algorithms, Adaptive Signal Control.</Keywords>
      <URLs>
        <Abstract>https://ijesm.co.in/ubijournal-v1copy/journals/abstract.php?article_id=15669&amp;title=QUANTUM INSPIRED AI FOR REAL TIME TRAFFIC FLOW OPTIMIZATION</Abstract>
      </URLs>
      <References>
        <ReferencesarticleTitle>References</ReferencesarticleTitle>
        <ReferencesfirstPage>16</ReferencesfirstPage>
        <ReferenceslastPage>19</ReferenceslastPage>
        <References/>
      </References>
    </Journal>
  </Article>
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