TY - JOUR
T1 - LiRA-CD: An open-source dataset for road condition modelling and research
AU - Skar, Asmus
AU - Vestergaard, Anders M.
AU - Brüsch, Thea
AU - Pour, Shahrzad
AU - Kindler, Ekkart
AU - Alstrøm, Tommy Sonne
AU - Schlotz, Uwe
AU - Larsen, Jakob Elsborg
AU - Pettinari, Matteo
PY - 2023
Y1 - 2023
N2 - This data article presents the details of the Live Road Assessment Custom Dataset (LiRA-CD), an open-source dataset for road condition modelling and research. The dataset captures GPS trajectories of a fleet of electric vehicles and their time-series data from 50 different sensors collected on 230 km of highway and urban roads in Copenhagen, Denmark. Additionally, road condition measurements were collected by standard survey vehicles, which serve as high-quality reference data. The in-vehicle measurements were collected onboard with an Internet-of-Things (IoT) device, then periodically transmitted before being saved in a database. Researchers can use the dataset for prediction modelling related to standard road condition parameters such as surface friction and texture, road roughness, road damages, and energy consumption. Furthermore, researchers and pavement engineers can use the dataset as a template for future studies and projects, benchmarking the performance of different algorithms and solving problems of the same type. LiRA-CD is freely available and can be accessed at https://doi.org/10.11583/DTU.c.6659909.
AB - This data article presents the details of the Live Road Assessment Custom Dataset (LiRA-CD), an open-source dataset for road condition modelling and research. The dataset captures GPS trajectories of a fleet of electric vehicles and their time-series data from 50 different sensors collected on 230 km of highway and urban roads in Copenhagen, Denmark. Additionally, road condition measurements were collected by standard survey vehicles, which serve as high-quality reference data. The in-vehicle measurements were collected onboard with an Internet-of-Things (IoT) device, then periodically transmitted before being saved in a database. Researchers can use the dataset for prediction modelling related to standard road condition parameters such as surface friction and texture, road roughness, road damages, and energy consumption. Furthermore, researchers and pavement engineers can use the dataset as a template for future studies and projects, benchmarking the performance of different algorithms and solving problems of the same type. LiRA-CD is freely available and can be accessed at https://doi.org/10.11583/DTU.c.6659909.
KW - Live road assessment
KW - Pavement analysis
KW - Road damage detection
KW - Road friction
KW - Road energy consumption
KW - Internet-of-vehicles
KW - Machine-learning
KW - Vehicle dynamics
UR - https://doi.org/10.11583/DTU.c.6659909
U2 - 10.1016/j.dib.2023.109426
DO - 10.1016/j.dib.2023.109426
M3 - Journal article
C2 - 37520654
SN - 2352-3409
VL - 49
JO - Data in Brief
JF - Data in Brief
M1 - 109426
ER -
