Ferjani Imen, Alsaif Suleiman Ali
Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
Sensors (Basel). 2024 Dec 19;24(24):8112. doi: 10.3390/s24248112.
Effective monitoring of road conditions is crucial for ensuring safe and efficient transportation systems. By leveraging the power of crowd-sourced smartphone sensor data, road condition monitoring can be conducted in real-time, providing valuable insights for transportation planners, policymakers, and the general public. Previous studies have primarily focused on the use of pre-trained machine learning models and threshold-based methods for anomaly classification, which may not be suitable for real-world scenarios that require incremental detection and classification. As a result, there is a need for novel approaches that can adapt to changing data environments and perform effective classification without relying on pre-existing training data. This study introduces a novel, real-time road condition monitoring technique harnessing smartphone sensor data, addressing the limitations of pre-trained models that lack adaptability in dynamic environments. A hybrid anomaly detection method, combining unsupervised and supervised learning, is proposed to effectively manage concept drift, demonstrating a significant improvement in accuracy and robustness with a 96% success rate. The findings underscore the potential of incremental learning to enhance model responsiveness and efficiency in distinguishing various road anomalies, offering a promising direction for future transportation safety and resource optimization strategies.
有效监测道路状况对于确保安全高效的交通系统至关重要。通过利用众包智能手机传感器数据的力量，可以实时进行道路状况监测，为交通规划者、政策制定者和公众提供有价值的见解。以往的研究主要集中在使用预训练的机器学习模型和基于阈值的方法进行异常分类，这可能不适用于需要增量检测和分类的实际场景。因此，需要新的方法来适应不断变化的数据环境，并在不依赖预先存在的训练数据的情况下进行有效的分类。本研究引入了一种利用智能手机传感器数据的新型实时道路状况监测技术，解决了预训练模型在动态环境中缺乏适应性的局限性。提出了一种结合无监督和监督学习的混合异常检测方法，以有效管理概念漂移，在准确率和鲁棒性方面有显著提高，成功率达96%。研究结果强调了增量学习在增强模型区分各种道路异常的响应能力和效率方面的潜力，为未来的交通安全和资源优化策略提供了一个有前景的方向。
