Statistically Accurate Synthetic Data Generation for Enhanced Traffic Predictive Modeling Using Generative Adversarial Networks and Long Short-Term Memory

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Data Generation for Enhanced Traffic Predictive Modeling Using Generative Adversarial Networks and Long Short-Term Memory</title> <meta name="description" content="Statistically Accurate Synthetic Data Generation for Enhanced Traffic Predictive Modeling Using Generative Adversarial Networks and Long Short-Term Memory"> <meta name="keywords" content="GAN, long short-term memory, synthetic data generation, traffic management"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> 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class="card"> <div class="card-body"><strong>Paper Count:</strong> 87354</div> </div> </div> </div> <div class="card publication-listing mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Statistically Accurate Synthetic Data Generation for Enhanced Traffic Predictive Modeling Using Generative Adversarial Networks and Long Short-Term Memory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Srinivas%20Peri">Srinivas Peri</a>, <a href="https://publications.waset.org/abstracts/search?q=Siva%20Abhishek%20Sirivella"> Siva Abhishek Sirivella</a>, <a href="https://publications.waset.org/abstracts/search?q=Tejaswini%20Kallakuri"> Tejaswini Kallakuri</a>, <a href="https://publications.waset.org/abstracts/search?q=Uzair%20Ahmad"> Uzair Ahmad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Effective traffic management and infrastructure planning are crucial for the development of smart cities and intelligent transportation systems. This study addresses the challenge of data scarcity by generating realistic synthetic traffic data using the PeMS-Bay dataset, improving the accuracy and reliability of predictive modeling. Advanced synthetic data generation techniques, including TimeGAN, GaussianCopula, and PAR Synthesizer, are employed to produce synthetic data that replicates the statistical and structural characteristics of real-world traffic. Future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is planned to capture both spatial and temporal correlations, further improving data quality and realism. The performance of each synthetic data generation model is evaluated against real-world data to identify the best models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are utilized to model and predict complex temporal dependencies within traffic patterns. This comprehensive approach aims to pinpoint areas with low vehicle counts, uncover underlying traffic issues, and inform targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study supports data-driven decision-making that enhances urban mobility, safety, and the overall efficiency of city planning initiatives. <iframe src="https://publications.waset.org/abstracts/191235.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GAN" title="GAN">GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory" title=" long short-term memory"> long short-term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic%20data%20generation" title=" synthetic data generation"> synthetic data generation</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20management" title=" traffic management"> traffic management</a> </p> <a href="https://publications.waset.org/abstracts/191235/statistically-accurate-synthetic-data-generation-for-enhanced-traffic-predictive-modeling-using-generative-adversarial-networks-and-long-short-term-memory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191235.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">25</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET 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Statistically Accurate Synthetic Data Generation for Enhanced Traffic Predictive Modeling Using Generative Adversarial Networks and Long Short-Term Memory