Abstract
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 language | English |
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Title of host publication | Proceedings of TRA2020, the 8th Transport Research Arena |
Subtitle of host publication | Rethinking transport – towards cleanand inclusive mobility |
Editors | Toni Lusikka |
Publisher | Liikenne- ja viestintävirasto Traficom |
ISBN (Electronic) | 978-952-311-484-5 |
Publication status | Published - 2020 |
MoE publication type | Not Eligible |
Event | 8th Transport Research Arena, TRA 2020 - Conference cancelled - Helsinki, Finland Duration: 27 Apr 2020 → 30 Apr 2020 https://traconference.eu/overview/ |
Publication series
Series | Traficom Research Reports |
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Number | 7 |
ISSN | 2660-8781 |
Conference
Conference | 8th Transport Research Arena, TRA 2020 - Conference cancelled |
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Abbreviated title | TRA 2020 |
Country/Territory | Finland |
City | Helsinki |
Period | 27/04/20 → 30/04/20 |
Internet address |
Keywords
- artificial intelligence
- machine learning
- user experience
- public transport
- big data