Applications of Machine Learning in Transport

Machine learning can process large amounts of data more swiftly and efficiently than manual analysis, and can uncover previously undetected relationships between datasets. Because the management and analysis of large, complex datasets is increasingly important in TRL’s work, the TRL Academy is funding research to develop machine learning. Three potential applications of machine learning have so far been studied: (1) Analysis of the behavior of locomotive operators, using clustering to analyze a small dataset and yield useful conclusions; (2) Forecasting of road pavement conditions, showing the significant potential of existing datasets while providing a stable framework for further research; (3) Crack detection study showing that some tedious and labor-intensive processes can be automated, while obtaining useful data.

• Record URL:
  https://trl.co.uk/publications/applications-of-machine-learning-in-transport
• Corporate Authors:

  TRL Limited

  Crowthorne House, Nine Mile Ride
  Wokingham, Berkshire  United Kingdom  RG40 3GA

  TRL Academy

  Crowthorne House, Nine Mile Ride
  Wokingham,   United Kingdom  RG40 3GA
• Authors:
  • Nemas, K
  • Khatry, R
  • Smirnov, A
  • Peeling, D
  • Mistry, S
  • Crabtree, M
  • Reeves, S
• Publication Date: 2018-6-30

Language

• English

Media Info

• Media Type: Digital/other
• Features: Appendices; Figures; References;
• Pagination: 58p

Subject/Index Terms

• TRT Terms: Behavior; Case studies; Cracking; Data analysis; Image processing; Locomotive engineers; Machine learning; Mathematical prediction; Pavement performance
• Subject Areas: Data and Information Technology; Operations and Traffic Management; Transportation (General);

Filing Info

• Accession Number: 01714305
• Record Type: Publication
• Report/Paper Numbers: PPR863
• Files: TRIS
• Created Date: Aug 21 2019 9:35AM