Modelling risk perception using a dynamic hybrid choice model and brain-imaging data: Application to virtual reality cycling
Modelling risk perception using a dynamic hybrid choice model and brain-imaging data: Application to virtual reality cycling
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使用动态混合选择模型和大脑成像数据对风险感知进行建模：在虚拟现实自行车中的应用
DOI：
10.1016/j.trc.2021.103435
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
A. Erath
中科院分区：
文献类型：
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作者：
Martyna Bogacz;S. Hess;C. Calastri;C. Choudhury;F. Mushtaq;M. Awais;M. Nazemi;M. V. van Eggermond;A. Erath
道路风险分析是交通运输领域的关键研究领域之一，人们早就认识到感知风险对选择的影响，特别是在动态环境中。然而，由于缺乏动态数据和难以捕捉的风险感知，现有的研究通常采取静态和陈述的方法来推断个人的风险水平。在本文中，我们旨在通过开发一个混合选择模型来解决这一研究空白，该模型联合使用虚拟现实中骑自行车行为的动态数据和神经数据来评估瞬时风险感知的波动如何影响骑自行车者的行为。所开发的模型的结果证实了我们的假设，表明骑自行车的人降低他们的速度时，接近一个路口的潜在的碰撞与通过汽车增加。此外，潜在成分使我们能够在神经数据、阿尔法脑电波的振幅和客观风险度量之间建立联系。与我们的假设一致，我们发现α振幅降低与更高的感知风险相关，这反过来又增加了刹车的可能性。我们的研究的意义是多方面的。一方面，它显示了虚拟现实的能力，以引起复杂的骑自行车的行为和可行性的联合收集动态神经和选择数据。另一方面，我们证明了在混合模型框架中使用神经数据作为风险指标的潜力，这使我们能够更好地了解骑自行车的行为和相关的神经处理。这些有希望的发现为未来的研究铺平了道路，旨在探索神经科学输入在选择模型中的优势。
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 with our hypothesis, we found that decreased alpha amplitude is associated with higher perceived risk, which in turn increases the likelihood of braking. The implications of our study are manifold. On the one hand, it shows the ability of virtual reality to elicit complex cyclists’ behaviour and the feasibility of a joint collection of dynamic neural and choice data. On the other hand, we demonstrated the potential of the employment of neural data in a hybrid model framework as an indicator of risk that allowed us to gain a better understanding of cycling behaviour and associated neural processing. These promising findings pave the way for future studies that aim to explore the advantages of neuroscientific inputs in the choice models.