Analysing the acceptance of autonomous buses in real-life traffic environments: pilot project with tourists in Malaga, Spain

ABSTRACT

Despite the significant technological progress with Autonomous Buses (ABs), there is still a limited understanding how users perceive and experience large, mass transit ABs in real-life traffic environments. This paper follows a pilot project on AB use by tourists in the city of Malaga, Spain. Discrete choice analysis was employed to study their willingness to use the AB and the passengers’ actual satisfaction on board the AB. The obtained results reveal higher willingness levels to adopt ABs among individuals more open to new technologies and more environmentally conscious. Higher levels of satisfaction on board are reported by older passengers, women, employed, private vehicle users, and people with environmental consciousness and openness to new technologies. Finally, the research discusses how socio-demographics and personality-related barriers affect the further implementation of autonomous mobility in public transportation systems, adding new reflections on the capacity of ABs to support or even replace conventional buses.

KEYWORDS:

• Autonomous vehicles
• self-driving buses
• public transport
• adoption intention
• passenger satisfaction
• personality traits

Acknowledgements

This paper has been developed in the context of the following research projects: (i) “AUTOMOST: Automated driving for dual-Mode System Transport”, funded the Spanish Government under the Strategic Program CIEN. Grant Agreement no. 00093152/TIC-20160172; (ii) “VIR2ALL: Using virtual reality for simulating a new generation of collaborative future scenarios to integrate land use and transport”, funded by the Spanish Government under National R+D Plan (2022-2025). Grant Agreement no. PID2021-123954NB-C21; (iii) “PLAN4LEZ: Diseño óptimo de estrategias de eco-movilidad urbana basadas en el impacto de Zonas de Bajas Emisiones utilizando datos en tiempo real”, funded by the Spanish Government under National R+D Plan (2022-2024). Grant Agreement no. TED2021-130673B-C22. The authors would like to thanks AVANZA for involving the UPM's team in the AUTOMOST project.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://automost.avanzagrupo.com/automost.