Civil and Environmental Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA.
Electrical and Computer Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA.
Sci Data. 2019 Aug 12;6(1):146. doi: 10.1038/s41597-019-0148-9.
We present DR-Train, the first long-term open-access dataset recording dynamic responses from in-service light rail vehicles. Specifically, the dataset contains measurements from multiple sensor channels mounted on two in-service light rail vehicles that run on a 42.2-km light rail network in the city of Pittsburgh, Pennsylvania. This dataset provides dynamic responses of in-service trains via vibration data collected by accelerometers, which enables a low-cost way of monitoring rail tracks more frequently. Such an approach will result in more reliable and economical ways to monitor rail infrastructure. The dataset also includes corresponding GPS positions of the trains, environmental conditions (including temperature, wind, weather, and precipitation), and track maintenance logs. The data, which is stored in a MAT-file format, can be conveniently loaded for various potential uses, such as validating anomaly detection and data fusion as well as investigating environmental influences on train responses.
我们提出了 DR-Train，这是第一个长期开放获取的记录在役轻轨车辆动态响应的数据集。具体来说，该数据集包含安装在宾夕法尼亚州匹兹堡市 42.2 公里轻轨网络上的两辆在役轻轨车辆上多个传感器通道的测量数据。该数据集通过加速度计采集的振动数据提供了在役列车的动态响应，这使得以更低的成本更频繁地监测轨道成为可能。这种方法将为监测轨道基础设施提供更可靠和经济的方式。该数据集还包括列车的相应 GPS 位置、环境条件（包括温度、风、天气和降水）以及轨道维护日志。数据以 MAT 文件格式存储，可方便地加载用于各种潜在用途，例如验证异常检测和数据融合以及研究环境对列车响应的影响。
