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首页> 外文期刊>Journal of Engineering & Applied Sciences >Classification of Road Surface Conditions Using Vehicle Positional Dynamics
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Classification of Road Surface Conditions Using Vehicle Positional Dynamics

机译：使用车辆位置动力学进行路面条件的分类

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The objective of this research is to collect and analyze road surface conditions in Malaysia and develop a classification model that can identify road surface conditions from the collected data. Data is collected through a mobile application that collects positional dynamics of vehicles on the road. Features considered include statistical measures such as minimum, maximum, standard deviation, median, average, skewness and kurtosis. Selection of the extracted features is performed using Ranker, Tabu search and Particle Swarm Optimization (PSO) followed by classification using k-Nearest Neighborhood (k-NN) Random Forest (RF) and Support Vector Machine (SVM) with linear, Radial Basis Function (RBF) and polynomial kernels. The classification model that gave the highest accuracy is SVM (RBF) with a Correct Classification Rate (CCR) of 91.71%. Trailing closely was RF at 91.17%. Although not as accurate as SVM, the difference was negligible and its computational time was much lower than the former. In the feature selection process, features which provide positive contribution to the classification process were chosen and the best performances were produced by PSO with an average CCR of 89.88%. Tabu selected 11 features while PSO selected 13 features where the extra two features made a difference in the results. Ranker selected every single feature but has the lowest average CCR. This is attributed to a subset of features that were selected were ineffectively impeding the classification. The features and classification model employed were able to effectively classify road surface conditions from vehicle positional dynamics. Using only 3D positional readings of the vehicle and standard statistical measures, road surface conditions can be effectively identified for the prioritisation and facilitation of road maintenance.
机译：本研究的目的是收集和分析马来西亚的道路表面条件，并开发一种可以从收集的数据中识别道路表面条件的分类模型。通过移动应用程序收集数据，该应用程序收集道路上车辆的位置动态。考虑的特征包括统计措施，如最低，最大，标准偏差，中位数，平均，偏斜和峰氏症。选择提取的特征是使用Ranker，Tabu搜索和粒子群优化（PSO）进行的，然后使用K-Figcle邻域（K-NN）随机林（RF）和带有线性的径向基函数的向量机（SVM）进行分类（RBF）和多项式核。给出了最高精度的分类模型是SVM（RBF），正确的分类率（CCR）为91.71％。尾随紧密率为RF为91.17％。虽然与SVM没有准确，但差异可以忽略不计，其计算时间远低于前者。在特征选择过程中，选择为分类过程提供积极贡献的特征，并且PSO生产的最佳性能，平均CCR为89.88％。禁忌选中了11个功能，而PSO选择了13个功能，额外两个功能在结果中取得了差异。排名夹选择每个特征，但平均值最低。这归因于所选功能的子集无效地阻碍了分类。所采用的特征和分类模型能够有效地将路面条件与车辆位置动力学有效地分类。仅使用车辆的3D位置读数和标准统计措施，可以有效地确定道路表面条件以优先排序和便利的道路维护。

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