Skip to content

Recorded Real Driving Data and Computed Track-Wise Metadata

License

Notifications You must be signed in to change notification settings

TUMFTM/dt-cargo

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

DT-CARGO dataset

Dataset of Trucks' Anonymized Recorded Driving and Operation

Getting started

A working installation of conda package manager is required.

First, install the provided environment.

conda env create -f environment.yml

Activate the environment

conda activate dt_cargo

Add the environment as a jupyter kernel.

python -m ipykernel install --user --name=dt_cargo

fleet.csv

The overview of the analyzed fleets.

Column Data Type Unit Description
vehicle_id int - Unique serial id of each vehicle
fleet_test_id int - Unique serial id of the fleet the vehicle belongs to
gross_vehicle_weight int - Gross Vehicle Weight Rating (without trailer)
total_mass_with_trailer int kg Gross Combination Weight Rating
(with trailer,equals gross_vehicle_weight
if no trailer can be attached)
axle_class int - Vehicle class according to [3]

tracks.csv

The overview of track-wise measured and computed data.

Column Data Type Unit Description
track_id int - Unique serial id of each recorded track
(ordered by vehicle_id and start_time)
vehicle_id int - Unique serial id of each vehicle
tour_id int - Serial id of each tour, assigned to 1..N tracks
start_time timestamptz - Start time of the recording with time zone at time of recording
stop_time timestamptz - Stop time of the recording with time zone at time of recording
distance int m Dis tance driven during track
track_gap float m Dis tance gap to following track
avg_speed float m/s Average speed
max_speed int m/s Maximum speed within track
n_signal_loss int - Number of signal loss events during recording
d_signal_loss float m Dis tance covered during signal losses
r_signal_loss float - Ratio of signal loss distance to recorded distance
avg_hdop float m Average horizontal degree of precision during recording
home_base bool - End of recording is at home base of fleet operator
long_haul bool - End of recording is more than 150 km away from home bases
rest_area bool - End of recording is at an unserviced rest area
service_area_fuel bool - End of recording is at a service area
industrial_area bool - End of recording is in an industrial area
cid int - Cluster id of last location in recording.

speed.csv

Speed and precision data of each individual track with 10Hz sampling. Speed data are stored in speed.zip in the folder speed/{vehicle_id}/{track_id}.csv In this repository only the Example track is provided, please refer to [1] for the full dataset.

Column Data Type Unit Description
epoch int s Unix timestamp of measurement
in time zone “Europe/Berlin”
speed int m/s Speed at measurement in m/s
hdop int m Horizontal degree of precision during recording [2]

[1] Zenodo Dataset TODO

[2] u-blox, “MAX-M8 series u-blox M8 concurrent GNSS modules Data Sheet”, UBX-15031506-R05 [Revised May 2019]

[3] Bundesanstalt für Straßenwesen, „Datensatzformat der Achslast- Jahresauswertungen (ALJA)“, 2018. Accessed: 22 nd Jan 2023 [Online]. Available: https://www.bast.de/DE/Statistik/Achslast/Daten/Daten-Beschreibung.pdf

Contributing and Support

For contributing to the code please contact:

Georg Balke Institute of Automotive Technology
Technical University of Munich

mail: [email protected]

Versioning

V1.1

Authors

Georg Balke, Lennart Adenaw

License

DT-CARGO is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/

A human-readable summary can be found under https://opendatacommons.org/licenses/odbl/summary/. Disclaimer: This is not a license. It is simply a handy reference for understanding the ODbL 1.0 — it is a human-readable expression of some of its key terms. This document has no legal value, and its contents do not appear in the actual license. Read the full ODbL 1.0 license text for the exact terms that apply.

Associated Article

Balke,G. and Adenaw, L. "Heavy commercial vehicles' mobility: Dataset of trucks' anonymized recorded driving and operation (DT-CARGO)" https://doi.org/10.1016/j.dib.2023.109246