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首页> 外文期刊>ISPRS International Journal of Geo-Information >Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers
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Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers

机译：使用基于树的集成分类器从轨迹数据识别不同的运输方式

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摘要

Recognition of transportation modes can be used in different applications including human behavior research, transport management and traffic control. Previous work on transportation mode recognition has often relied on using multiple sensors or matching Geographic Information System (GIS) information, which is not possible in many cases. In this paper, an approach based on ensemble learning is proposed to infer hybrid transportation modes using only Global Position System (GPS) data. First, in order to distinguish between different transportation modes, we used a statistical method to generate global features and extract several local features from sub-trajectories after trajectory segmentation, before these features were combined in the classification stage. Second, to obtain a better performance, we used tree-based ensemble models (Random Forest, Gradient Boosting Decision Tree, and XGBoost) instead of traditional methods (K-Nearest Neighbor, Decision Tree, and Support Vector Machines) to classify the different transportation modes. The experiment results on the later have shown the efficacy of our proposed approach. Among them, the XGBoost model produced the best performance with a classification accuracy of 90.77% obtained on the GEOLIFE dataset, and we used a tree-based ensemble method to ensure accurate feature selection to reduce the model complexity.
机译：运输模式的识别可用于不同的应用程序，包括人类行为研究，运输管理和交通控制。先前有关运输模式识别的工作通常依赖于使用多个传感器或匹配的地理信息系统（GIS）信息，这在许多情况下是不可能的。在本文中，提出了一种基于集成学习的方法来仅使用全球定位系统（GPS）数据来推断混合运输模式。首先，为了区分不同的运输方式，我们使用了一种统计方法来生成全局特征，并在轨迹分割后在分类阶段将这些特征组合之前，从子轨迹中提取几个局部特征。其次，为了获得更好的性能，我们使用基于树的集成模型（随机森林，梯度提升决策树和XGBoost）代替了传统方法（K最近邻，决策树和支持向量机）对不同的交通进行分类模式。后面的实验结果表明了我们提出的方法的有效性。其中，XGBoost模型产生了最佳性能，在GEOLIFE数据集上获得了90.77％的分类精度，并且我们使用了基于树的集成方法来确保准确的特征选择以降低模型的复杂性。

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