Beyond utility: incorporating eye-tracking, skin conductance and heart rate data into cognitive and econometric travel behaviour models
超越效用：将眼动、皮肤电导与心率纳入出行行为模型AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要围绕住宿选择和驾驶间隙接受决策，比较计量选择模型与认知选择模型，直接对应个体选择行为的建模。
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超越效用：将眼动、皮肤电导与心率纳入出行行为模型
- 研究问题
- 眼动和生理状态信息能否改善静态住宿选择与动态驾驶间隙接受模型，并帮助解释认知决策过程中的参数？
- 方法与思路
- 作者把生理数据纳入计量选择模型和决策场理论模型，分别研究陈述偏好中的住宿选择及驾驶模拟器中的动态决策。
- 材料陈述的贡献
- 摘要称眼动信息改善两类静态模型，动态情境中将压力和眼动关联到认知过程参数，带来了更大的模型改善。
- 阅读关注点
- 应核对生理变量如何进入效用或过程参数、模型识别与验证方式，以及改善指标是否来自独立样本的预测比较。
- 阅读边界
- 这是预印本，同行评审或期刊接收未核实；摘要未提供样本量、改善幅度和验证细节，不能据此认定生理指标具有因果作用。
原文摘要
Choice models for large-scale applications have historically relied on economic theories (e.g. utility maximisation) that establish relationships between the choices of individuals, their characteristics, and the attributes of the alternatives. In a parallel stream, choice models in cognitive psychology have focused on modelling the decision-making process, but typically in controlled scenarios. Recent research developments have attempted to bridge the modelling paradigms, with choice models that are based on psychological foundations, such as decision field theory (DFT), outperforming traditional econometric choice models for travel mode and route choice behaviour. The use of physiological data, which can provide indications about the choice-making process and mental states, opens up the opportunity to further advance the models. In particular, the use of such data to enrich 'process' parameters within a cognitive theory-driven choice model has not yet been explored. This research gap is addressed by incorporating physiological data into both econometric and DFT models for understanding decision-making in two different contexts: stated-preference responses (static) of accomodation choice and gap-acceptance decisions within a driving simulator experiment (dynamic). Results from models for the static scenarios demonstrate that both models can improve substantially through the incorporation of eye-tracking information. Results from models for the dynamic scenarios suggest that stress measurement and eye-tracking data can be linked with process parameters in DFT, resulting in larger improvements in comparison to simpler methods for incorporating this data in either DFT or econometric models. The findings provide insights into the value added by physiological data as well as the performance of different candidate modelling frameworks for integrating such data.