Dataset on passenger experience after riding an autonomous ferry

Area of Science:

• Maritime Technology
• Human-Computer Interaction
• Transportation Engineering

Background:

• Autonomous vehicles (AVs) are advancing rapidly in transportation.
• User experience (UX) research for AVs, especially in public transit, is limited.
• Public trials of autonomous ferries offer unique opportunities for UX data collection.

Purpose of the Study:

• To present a dataset of passenger experiences on an autonomous ferry.
• To investigate user experience, attitudes, and design feedback for autonomous maritime transport.
• To provide empirical data for the adoption of autonomous vehicles in public transportation.

Main Methods:

• A dataset of 146 questionnaire responses was collected from passengers on the "milliAmpere2" autonomous ferry.
• The survey included 46 items covering demographics, passenger experience, attitudes, and design feedback.
• Data were gathered via a digital survey during public trials from June 25 to August 24, 2024.

Main Results:

• The dataset provides empirical data on passenger experience and attitudes towards autonomous ferry travel.
• Identified key factors influencing user acceptance and satisfaction with autonomous ferry services.
• Collected feedback on specific design characteristics of the autonomous ferry.

Conclusions:

• The dataset is valuable for research in autonomous vehicle design, engineering, and urban planning.
• Findings contribute to understanding social science aspects of autonomous vehicle adoption in public transit.
• This research supports the development and integration of autonomous ferries into urban transportation networks.