Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Hybrid route choice model incorporating latent cognitive effects of real-time travel information using physiological data
Transportation Research Part F: Traffic Psychology and Behaviour ( IF 4.349 ) Pub Date : 2021-06-25 , DOI: 10.1016/j.trf.2021.05.021
Shubham Agrawal , Srinivas Peeta

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.



中文翻译:

结合使用生理数据的实时旅行信息的潜在认知效应的混合路线选择模型

信息系统的激增使驾驶员能够在途中接收通常来自多个来源的实时旅行信息,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深刻理解来设计信息及其交付系统,以管理网络范围的交通。然而,大多数现有的路线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线选择决策的影响的能力。本文提出了一种混合路线选择建模框架,该框架结合了实时信息的潜在认知效应和几个可以直接测量的解释变量(即路线特征、信息特征、驾驶员属性、和情境因素)。通过分析使用脑电图 (EEG) 测量的驾驶员生理数据(即大脑电活动模式)来估计潜在的认知影响。在驾驶模拟器实验中收集了 95 名参与者的数据,这些实验旨在使用具有不同特征(在旅行时间和驾驶环境复杂性方面)和动态环境交通的路线的网络级设置来引出现实的路线选择。多个大脑区域的平均 EEG 频带功率被用于提取两个潜在的认知变量,这些变量捕捉驾驶员在信息提供期间和之后的认知努力,以及在实施路线选择决策之前的认知注意力不集中。多指标多原因模型用于测试几个解释因素对潜在认知变量的影响,以及它们对路线选择决策的综合影响。研究结果强调了驾驶员属性和信息特征对潜在认知努力和路线特征对潜在认知注意力不集中的显着影响。他们还表明,更专心并付出更多认知努力的驾驶员更有可能通过遵守所提供的信息而改变当前路线。研究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结合起来,同时设计用于设计和传播实时旅行信息的策略,以影响驾驶员的路线选择。以及它们对路线选择决策的综合影响。研究结果强调了驾驶员属性和信息特征对潜在认知努力和路线特征对潜在认知注意力不集中的显着影响。他们还表明,更专心并付出更多认知努力的驾驶员更有可能通过遵守所提供的信息而改变当前路线。研究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结合起来,同时设计用于设计和传播实时旅行信息的策略,以影响驾驶员的路线选择。以及它们对路线选择决策的综合影响。研究结果突出了驾驶员属性和信息特征对潜在认知努力和路线特征对潜在认知注意力不集中的显着影响。他们还表明,更专心并付出更多认知努力的驾驶员更有可能通过遵守所提供的信息而改变当前路线。研究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结合起来,同时设计用于设计和传播实时旅行信息的策略,以影响驾驶员的路线选择。研究结果强调了驾驶员属性和信息特征对潜在认知努力和路线特征对潜在认知注意力不集中的显着影响。他们还表明,更专心并付出更多认知努力的驾驶员更有可能通过遵守所提供的信息而改变当前路线。研究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结合起来,同时设计用于设计和传播实时旅行信息的策略,以影响驾驶员的路线选择。研究结果突出了驾驶员属性和信息特征对潜在认知努力和路线特征对潜在认知注意力不集中的显着影响。他们还表明,更专心并付出更多认知努力的驾驶员更有可能通过遵守所提供的信息而改变当前路线。研究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结合起来,同时设计用于设计和传播实时旅行信息的策略,以影响驾驶员的路线选择。他们还表明,更专心并付出更多认知努力的驾驶员更有可能通过遵守所提供的信息而改变当前路线。研究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结合起来,同时设计用于设计和传播实时旅行信息的策略,以影响驾驶员的路线选择。他们还表明,更专心并付出更多认知努力的驾驶员更有可能通过遵守所提供的信息而改变当前路线。研究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结合起来,同时设计用于设计和传播实时旅行信息的策略,以影响驾驶员的路线选择。

更新日期:2021-06-28
down
wechat
bug