Hybrid route choice model incorporating latent cognitive effects of real-time travel information using physiological data
Hybrid route choice model incorporating latent cognitive effects of real-time travel information using physiological data
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DOI：
10.1016/j.trf.2021.05.021
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发表时间：
2021-06-25
影响因子：
4.1
通讯作者：
Peeta, Srinivas
中科院分区：
文献类型：
--
作者：
Agrawal, Shubham;Peeta, Srinivas
信息系统的激增使驾驶员能够接收通常来自多个来源的途中实时旅行信息，以便做出明智的路线决策。交通运营商可以利用对信息提供下的路线选择行为的鲁棒理解来设计用于管理全网络交通的信息及其递送系统。然而，大多数现有的路径选择模型缺乏考虑信息对驾驶员的潜在认知影响及其对路径选择决策的影响的能力。本文提出了一种混合路径选择建模框架，该框架结合了实时信息的潜在认知效应和可以直接测量的几个解释变量的效应（即，路线特征、信息特征、驾驶员属性和情境因素）。潜在的认知影响通过分析驾驶员的生理数据（即，脑电活动模式）。收集数据的95名参与者在驾驶模拟器实验，旨在引出现实的路线选择使用网络级设置具有不同的特点（在行驶时间和驾驶环境的复杂性）和动态环境交通的路线。在多个大脑区域的平均EEG频带功率被用来提取两个潜在的认知变量，捕获驾驶员的认知努力期间和之后立即提供的信息，和认知注意力不集中之前，实施路线选择的决定。多指标多原因模型被用来测试潜在的认知变量的几个解释因素的影响，以及它们的组合对路径选择决策的影响。研究结果表明，驾驶员属性和信息特征对潜在认知努力有显著影响，路径特征对潜在认知疏忽有显著影响。他们还表明，那些更专注、付出更多认知努力的司机更有可能通过遵守所提供的信息从当前路线切换。该研究的见解可以帮助交通运营商和信息服务提供商将人为因素和认知方面纳入其中，同时制定设计和传播实时出行信息的策略，以影响驾驶员的路线选择。
The proliferation of information systems is enabling drivers to receive en route real-time travel information, often from multiple sources, for making informed routing decisions. A robust understanding of route choice behavior under information provision can be leveraged by traffic operators to design information and its delivery systems for managing network-wide traffic. However, most existing route choice models lack the ability to consider the latent cognitive effects of information on drivers and their implications on route choice decisions. This paper presents a hybrid route choice modeling framework that incorporates the latent cognitive effects of real-time information and the effects of several explanatory variables that can be measured directly (i.e., route characteristics, information characteristics, driver attributes, and situational factors). The latent cognitive effects are estimated by analyzing drivers' physiological data (i.e., brain electrical activity patterns) measured using an electroencephalogram (EEG). Data was collected for 95 participants in driving simulator experiments designed to elicit realistic route choices using a network-level setup featuring routes with different characteristics (in terms of travel time and driving environment complexity) and dynamic ambient traffic. Averaged EEG band powers in multiple brain regions were used to extract two latent cognitive variables that capture driver's cognitive effort during and immediately after the information provision, and cognitive inattention before implementing the route choice decision. A Multiple Indicators Multiple Causes model was used to test the effects of several explanatory factors on the latent cognitive variables, and their combined impacts on route choice decisions. The study results highlight the significant effects of driver attributes and information characteristics on latent cognitive effort and of route characteristics on latent cognitive inattention. They also indicate that drivers who are more attentive and exert more cognitive effort are more likely to switch from their current route by complying with the information provided. The study insights can aid traffic operators and information service providers to incorporate human factors and cognitive aspects while devising strategies for designing and disseminating real-time travel information to influence drivers' route choices.