Modelling risk perception using a dynamic hybrid choice model and brain-imaging data: An application to virtual reality cycling

Road risk analysis is one of the key research areas in transport, where the impact of perceived risk on choices, especially in a dynamic setting, has been long recognised. However, due to the lack of dynamic data and the difficulty in capturing risk perception, existing studies typically resort to static and stated approaches to infer the experienced level of risk of individuals. In this paper, we aimed to address this research gap through developing a hybrid choice model that jointly employed dynamic data on cycling behaviour in virtual reality and neural data to evaluate how the fluctuations in momentary risk perception influence the behaviour of cyclists. The results of the developed model confirm our hypotheses, demonstrating that cyclists reduce their speed when approaching a junction as the potential for a collision with passing cars increases. Moreover, the latent component allowed us to establish a link between the neural data, the amplitude of alpha brainwaves, and objective risk measures. In line wi

Keywords

Virtual reality, EEG, Hybrid Choice Model, Cycling behaviour, Cycling simulator

Subject (DDC)

624 - Ingenieurbau und Umwelttechnik

Project

Event

Exhibition start date

Exhibition end date

Conference start date

Conference end date

Date of the last check

ISBN

ISSN

0968-090X
1879-2359

Language

English

Created during FHNW affiliation

No

Strategic action fields FHNW

Publication status

Published

Review

Peer review of the complete publication

Open access category

Closed

License

Citation

Bogacz, M., Hess, S., Calastri, C., Choudhury, C. F., Mushtaq, F., Awais, M., Nazemi, M., van Eggermond, M., & Erath, A. (2021). Modelling risk perception using a dynamic hybrid choice model and brain-imaging data: An application to virtual reality cycling. Transportation Research Part C: Emerging Technologies, 133, 103435. https://doi.org/10.1016/j.trc.2021.103435

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