Estimating passenger experience from vehicle data: Preconditions for using machine learning

Timo Laakko, Juho Kostiainen, Olli Pihlajamaa

Research output: Chapter in Book/Report/Conference proceedingConference abstract in proceedingsScientific


These days, there is a vast amount of data available from public transport vehicles. This data has potential to be used for estimating passenger experience and comfort. Automatic and continuous monitoring of parameters affecting user experience can help in, for example, designing and developing better vehicles, using the information for operator rewards or for training drivers. This paper looks at how machine learning can be applied for estimating (electric) bus passenger experience automatically by using commonly available vehicle and sensor data. The focus is on how to ensure that data is interpreted correctly. We compare data collected from electric buses with passenger survey results and passenger feedback to identify relevant data types. Along with promising parameters for evaluating passenger experience, we provide recommendations on what needs to be considered in collecting actual user experiences for validating causation and results.
Original languageEnglish
Title of host publicationProceedings of TRA2020, the 8th Transport Research Arena
Subtitle of host publicationRethinking transport – towards cleanand inclusive mobility
EditorsToni Lusikka
PublisherLiikenne- ja viestintävirasto Traficom
ISBN (Electronic)978-952-311-484-5
Publication statusPublished - 2020
MoE publication typeNot Eligible
Event8th Transport Research Arena, TRA 2020 - Conference cancelled - Helsinki, Finland
Duration: 27 Apr 202030 Apr 2020

Publication series

SeriesTraficom Research Reports


Conference8th Transport Research Arena, TRA 2020 - Conference cancelled
Abbreviated titleTRA 2020
Internet address


  • artificial intelligence
  • machine learning
  • user experience
  • public transport
  • big data


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