Ferjani Imen, Ali Alsaif Suleiman
Computer Department, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, Dammam, Kingdom of Saudi Arabia.
PeerJ Comput Sci. 2022 Apr 12;8:e941. doi: 10.7717/peerj-cs.941. eCollection 2022.
Road condition monitoring is essential for improving traffic safety and reducing accidents. Machine learning methods have recently gained prominence in the practically important task of controlling road surface quality. Several systems have been proposed using sensors, especially accelerometers present in smartphones due to their availability and low cost. However, these methods require practitioners to specify an exact set of features from all the sensors to provide more accurate results, including the time, frequency, and wavelet-domain signal features. It is important to know the effect of these features change on machine learning model performance in handling road anomalies classification tasks. Thus, we address such a problem by conducting a sensitivity analysis of three machine learning models which are Support Vector Machine, Decision Tree, and Multi-Layer Perceptron to test the effectiveness of the model by selecting features. We built a feature vector from all three axes of the sensors that boosts classification performance. Our proposed approach achieved an overall accuracy of 94% on four types of road anomalies. To allow an objective analysis of different features, we used available accelerometer datasets. Our objective is to achieve a good classification performance of road anomalies by distinguishing between significant and relatively insignificant features. Our chosen baseline machine learning models are based on their comparative simplicity and powerful empirical performance. The extensive analysis results of our study provide practical advice for practitioners wishing to select features effectively in real-world settings for road anomalies detection.
道路状况监测对于提高交通安全和减少事故至关重要。机器学习方法最近在控制路面质量这一实际重要任务中受到了广泛关注。由于智能手机中存在加速度计，且其具有可用性和低成本，因此已经提出了几种使用传感器的系统。然而，这些方法要求从业者从所有传感器中指定一组确切的特征，以提供更准确的结果，包括时间、频率和小波域信号特征。了解这些特征变化对机器学习模型处理道路异常分类任务性能的影响非常重要。因此，我们通过对支持向量机、决策树和多层感知器这三种机器学习模型进行敏感性分析来解决此类问题，以通过选择特征来测试模型的有效性。我们从传感器的所有三个轴构建了一个特征向量，提高了分类性能。我们提出的方法在四种类型的道路异常上实现了94%的总体准确率。为了对不同特征进行客观分析，我们使用了可用的加速度计数据集。我们的目标是通过区分重要特征和相对不重要的特征来实现道路异常的良好分类性能。我们选择的基准机器学习模型基于它们相对简单和强大的实证性能。我们研究的广泛分析结果为希望在实际环境中有效选择特征以进行道路异常检测的从业者提供了实用建议。
