Xia Hao, Qiao Yanyou, Jian Jun, Chang Yuanfei
The Institute of Remote Sensing and Digital Earth, No.20 Datun Road, Chaoyang District, Beijing 100101, China.
Sensors (Basel). 2014 Nov 4;14(11):20843-65. doi: 10.3390/s141120843.
The proliferation of mobile smart devices has led to a rapid increase of location-based services, many of which are amassing large datasets of user trajectory information. Unfortunately, current trajectory information is not yet sufficiently rich to support classification of user transportation modes. In this paper, we propose a method that employs both the Global Positioning System and accelerometer data from smart devices to classify user outdoor transportation modes. The classified modes include walking, bicycling, and motorized transport, in addition to the motionless (stationary) state, for which we provide new depth analysis. In our classification, stationary mode has two sub-modes: stay (remaining in the same place for a prolonged time period; e.g., in a parked vehicle) and wait (remaining at a location for a short period; e.g., waiting at a red traffic light). These two sub-modes present different semantics for data mining applications. We use support vector machines with parameters that are optimized for pattern recognition. In addition, we employ ant colony optimization to reduce the dimension of features and analyze their relative importance. The resulting classification system achieves an accuracy rate of 96.31% when applied to a dataset obtained from 18 mobile users.
移动智能设备的激增导致基于位置的服务迅速增加，其中许多服务正在积累大量用户轨迹信息数据集。不幸的是，当前的轨迹信息还不够丰富，无法支持对用户交通方式的分类。在本文中，我们提出了一种方法，该方法利用来自智能设备的全球定位系统和加速度计数据来对用户的户外交通方式进行分类。除了静止（不动）状态外，分类的交通方式还包括步行、骑自行车和机动交通，对此我们提供了新的深度分析。在我们的分类中，静止模式有两个子模式：停留（在同一地点停留较长时间；例如，在停放的车辆中）和等待（在一个地点停留较短时间；例如，在红灯处等待）。这两个子模式在数据挖掘应用中呈现出不同的语义。我们使用为模式识别优化了参数的支持向量机。此外，我们采用蚁群优化来降低特征维度并分析其相对重要性。当应用于从18个移动用户获得的数据集时，所得的分类系统实现了96.31%的准确率。
