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LiRA-CD: An open-source dataset for road condition modelling and research.

Asmus Skar1, Anders M Vestergaard1, Thea Brüsch2

  • 1Environmental and Resource Engineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark.

Data in Brief
|July 31, 2023
PubMed
Summary

This study introduces the Live Road Assessment Custom Dataset (LiRA-CD), an open-source resource for road condition research. It provides extensive sensor data from electric vehicles for modeling road parameters and pavement engineering.

Keywords:
Internet-of-vehiclesLive road assessmentMachine-learningPavement analysisRoad damage detectionRoad energy consumptionRoad frictionVehicle dynamics

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Area of Science:

  • Civil Engineering
  • Transportation Engineering
  • Data Science

Background:

  • Accurate road condition assessment is crucial for infrastructure maintenance and vehicle performance.
  • Existing datasets may lack comprehensive sensor data or real-world road coverage.
  • Developing advanced road condition models requires diverse and high-quality data.

Purpose of the Study:

  • To introduce the Live Road Assessment Custom Dataset (LiRA-CD).
  • To provide a valuable resource for road condition modeling and research.
  • To facilitate benchmarking of algorithms for road parameter prediction.

Main Methods:

  • Collected GPS trajectories and time-series sensor data from a fleet of electric vehicles.
  • Acquired data from 50 onboard sensors across 230 km of highway and urban roads in Copenhagen.
  • Utilized standard survey vehicles for high-quality reference road condition measurements.
  • Employed Internet-of-Things (IoT) devices for in-vehicle data collection and transmission.

Main Results:

  • The LiRA-CD dataset comprises comprehensive in-vehicle sensor data and reference measurements.
  • The dataset covers diverse road types, including highways and urban environments.
  • Data includes GPS trajectories, time-series sensor readings, and road condition parameters.

Conclusions:

  • LiRA-CD is a valuable, open-source dataset for road condition research and prediction modeling.
  • The dataset can be used to model surface friction, texture, roughness, damages, and energy consumption.
  • Researchers and pavement engineers can leverage LiRA-CD for algorithm benchmarking and future studies.