Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh
journal contributionposted on 31.10.2019 by Jingxiao Liu, Siheng Chen, George Lederman, David B. Kramer, Hae Young Noh, Jacobo Bielak, James Garrett, Jelena Kovacevic, Mario Berges
Any type of content formally published in an academic journal, usually following a peer-review process.
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.