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<p>This study introduces a novel framework for enhancing traffic management systems through the integration of Machine learning and Deep Learning approaches. Leveraging both publicly available datasets and data generated through the SUMO simulator, this research presents the Hyper-Tuned Detrended XGBoost framework (HT-DXG) as a robust solution for accurate traffic flow and congestion prediction. The proposed framework incorporates advanced techniques such as detrending and hyperparameter optimization to improve predictive accuracy, focusing on critical metrics like wait time and travel time. The motivation behind this research stems from the need to create more responsive and resilient traffic management systems that can adapt to real-time conditions. Extensive experimentation using diverse traffic scenarios mapped on OpenStreetMap (OSM) highlights the superior performance of HT-DXG compared to baseline models. This work not only provides a comprehensive methodology for traffic prediction but also contributes to the field by generating a unique dataset tailored for real-time traffic analysis in urban environments. The findings offer valuable insights for urban planners and policymakers aiming to mitigate traffic-related challenges in rapidly growing cities.</p>

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Prediction of traffic time using XGBoost model with hyperparameter optimization

  • 期刊论文

  • 发表日期：

    2025年2月6日

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  • Deepika,
  • Gitanjali Pandove

摘要

This study introduces a novel framework for enhancing traffic management systems through the integration of Machine learning and Deep Learning approaches. Leveraging both publicly available datasets and data generated through the SUMO simulator, this research presents the Hyper-Tuned Detrended XGBoost framework (HT-DXG) as a robust solution for accurate traffic flow and congestion prediction. The proposed framework incorporates advanced techniques such as detrending and hyperparameter optimization to improve predictive accuracy, focusing on critical metrics like wait time and travel time. The motivation behind this research stems from the need to create more responsive and resilient traffic management systems that can adapt to real-time conditions. Extensive experimentation using diverse traffic scenarios mapped on OpenStreetMap (OSM) highlights the superior performance of HT-DXG compared to baseline models. This work not only provides a comprehensive methodology for traffic prediction but also contributes to the field by generating a unique dataset tailored for real-time traffic analysis in urban environments. The findings offer valuable insights for urban planners and policymakers aiming to mitigate traffic-related challenges in rapidly growing cities.