Next Stop: Passenger Perspectives on Autonomous Trains

Publication date

2024

Authors

Arzer, Andrea
Beehler, LaurenORCID 0009-0008-2352-3935ISNI 0000000517774923
Vredenborg, MarloesORCID 0000-0001-5724-6851ISNI 0000000512654033

Editors

Krömker, Heidi

Advisors

Supervisors

Document Type

Part of book
Open Access logo

License

taverne

Abstract

While extensive research exists on autonomous cars for private use, there is a notable gap in understanding public opinions on autonomous trains. Understanding passengers’ views on a technology that might be available to them in the next decade could highly influence its success. This paper researches factors influencing potential passengers when deciding to ride fully autonomous trains and explores solutions to counteract negative perceptions. To complement the available research on this topic that has been conducted using quantitative methods, this paper describes a multi-method qualitative study combining focus groups and creative problem-solving sessions. Key findings include participants’ distrust in unfamiliar systems and hesitation about the absence of human staff onboard. Proposed ideas include the visible implementation of additional safety features available to the passengers, the adaptation of the train interior to make it more inviting, and the provision of information about the operation of the autonomous trains. This study uncovered different perspectives and concerns related to autonomous railway vehicles, along with solutions that can be implemented to increase passenger trust. However, it also emphasizes the complexity of the topic, illustrating the necessity for additional research.

Keywords

Taverne

Citation

Arzer, A, Beehler, L & Vredenborg, M 2024, Next Stop: Passenger Perspectives on Autonomous Trains. in H Krömker (ed.), HCI in Mobility, Transport, and Automotive Systems - 6th International Conference, MobiTAS 2024, Held as Part of the 26th HCI International Conference, HCII 2024, Proceedings : HCI in Mobility, Transport, and Automotive Systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14733 LNCS, Springer, pp. 3-25. https://doi.org/10.1007/978-3-031-60480-5_1