There is a newer version of the record available.

Published October 2, 2018 | Version 1.0.0
Dataset Open

The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh

Description

Note: Downloading the large data file could have a timeout issue. If you cannot directly download it here, please use the following link as a complementary method for getting the data. 

https://drive.google.com/drive/folders/1oKn7IN7zznQuhwjDCDdjq8r9wHJYBEhj?usp=sharing

 

This dataset contains the dynamic responses (acceleration records) of two passenger trains with corresponding GPS positions, environmental conditions and track maintenance schedules for a light rail network in the city of Pittsburgh, Pennsylvania in the United States of America.

In particular, two light rail vehicles were instrumented (identified as LRV4306 and LRV4313): 
LRV 4306 has 5 acceleration channels, corresponding to the two uni-axial accelerometers inside the train and the three channels of the tri-axial accelerometer on the wheel truck.

- The last digit of each acceleration file: 1, 2, 3, 4, 5
- Corresponding sensor channels: tri-axial x, tri-axial y, tri-axial z, front cabinet uni-axial, back cabinet uni-axial


LRV 4313 has 8 acceleration channels, corresponding to the two uni-axial accelerometer and the two tri-axial accelerometers inside the train.

- The last digit of each acceleration file: 1, 2, 3, 4, 5, 6, 7, 8
- Corresponding sensor channels: front cabinet uni-axial, back cabinet uni-axial, front tri-axial x, front tri-axial y, front tri-axial z, back tri-axial x, back tri-axial y, back tri-axial z.
- x longitudinal (vehicle moving direction); y-axis, transverse; z-axis, vertical.

The dataset contained in this repository is a condensed version of the original raw data. While the accelerometers on the train were sampled continuously, this dataset contains only those measurements for when the train was actually moving along the track (i.e. not idling at a terminal).

The data is stored in binary MAT-files (a MATLAB/Octave data format). These files contain MATLAB objects of the class "pass", which is defined in the file pass.m that can be found in the "code" folder. Specifically, two MAT-files named "obj_dic.mat", and found in the "LRV4306" and "LRV4313" folders, contain the "pass" objects of the two trains, respectively.

Each category is described in detail. For more detail on the regions of the track, refer to the 'region.fig' file in this folder. The track was divided into distinct regions so that the data over specific sections of track could be compared. These regions were chosen for two reasons: 
(1) within a region, the train always followed the same track and 
(2) there are no tunnels in them so the GPS data is relatively consistent. 

To get started, using MATLAB or Octave try running "main_script.m" in the "code" folder.

A data descriptor paper with details of the data collection process was published.

Please cite as

Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. Dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Scientific Data, 6, 146. https://doi.org/10.1038/s41597-019-0148-9(2019)

Liu, J., Chen, S., Lederman, G., Kramer, D. B., Noh, H. Y., Bielak, J., Garrett, J. H., Kovačević, J., & Berges, M. The DR-Train dataset: dynamic responses, GPS positions and environmental conditions of two light rail vehicles in Pittsburgh. Zenodo, https://doi.org/10.5281/zenodo.1432702(2018).

For questions or suggestions please e-mail Jingxiao Liu <liujx@stanford.edu>

Notes

This material is also based on work supported by a University Transportation Center grant (DTRT12-G-UTC11) from the US Department of Transportation.

Files

code.zip

Files (138.8 GB)

Name Size Download all
md5:de05a144f9568ad178e993ac31e43bbf
8.2 kB Preview Download
md5:4162d8050b97c3b0a9618676d87e2e6e
138.8 GB Preview Download
md5:832c24e7ebfd76d21579bdd64a56074c
5.6 kB Preview Download
md5:0f803c019d30819c54257e3c48a41627
4.6 MB Download

Additional details

Funding

CIF: Small: Theory of Multiresolution Classification with Bases and Frames 1017278
National Science Foundation
Graduate Research Fellowship Program 0946825
National Science Foundation
Indirect Bridge Health Monitoring Using Moving Vehicles 1130616
National Science Foundation