Abstract
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.
Highlights
Background & SummaryThe private freight rail industry in the U.S makes $9.7 billion capital investment in maintaining the network, which is comprised of almost 140,000 miles of track and over 100,000 bridges in 20151
Module responses using accelerometers; in addition, our dataset includes environmental data as the trains were running on the track and the track maintenance logs from the light-rail operator
We present environmental conditions and maintenance schedules during this monitoring period
Summary
The private freight rail industry in the U.S makes $9.7 billion capital investment in maintaining the network, which is comprised of almost 140,000 miles of track and over 100,000 bridges in 20151. Researchers have proposed many indirect track inspection methods using sensors, such as accelerometers and GPS, installed on in-service trains for track geometry monitoring and change detection[3,4,5,6,7,8,9,10,11] since it can be more reliable than visual inspection and costs less than inspection using a track-geometry car. Module responses using accelerometers; in addition, our dataset includes environmental data as the trains were running on the track and the track maintenance logs from the light-rail operator. There are some acceleration datasets for structure vibration testing[12], human activity recognition[13], senior fall detection[14] and gait recognition[15], at the time of writing, the DR-Train dataset is the only one to include multi-channel and high-frequency acceleration signals and GPS positions of light rail vehicles.
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