Evaluating the Impacts of Driver Cognitive and Physiological State on Decision Making Behavior under Real Time Travel Information

Agrawal, Shubham; Benedyk, Irina; Peet, Srinivas; Song, Dong Yoon · 2018 · ROSA P / Nextrans

archive: archived pipeline: cataloged verified

Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)

Summary

This study investigates how real-time travel information affects driver decision-making by examining the underlying cognitive and physiological states. The research is motivated by the dual nature of in-vehicle information systems: while they offer cognitive benefits for informed route choices, they also compete for cognitive resources required for multitasking driving. The authors aim to determine how information characteristics and driver demographics influence physiological responses, thereby informing the optimal design and timing of information delivery. To address these questions, the researchers conducted driving simulator experiments using a real-world network-level road map of Northern Indianapolis. Participants received real-time travel information consistent with ambient traffic conditions, with a reward system in place to simulate urgency and penalize rash driving. The experimental design varied information characteristics, including content (descriptive travel times vs. prescriptive route suggestions), amount, and source (voice-only, variable message signs-only, or both). Data collection involved sociodemographic surveys, attitude assessments, and continuous physiological monitoring using EEG, ECG, and eye-tracking devices. The EEG data focused on specific brain lobes associated with auditory, visual, tactile, and reasoning processes, utilizing the B-Alert system to compute mental workload and task engagement classifiers. The analysis, based on 76 participants who received voice information, employed ANCOVA to examine systematic differences in EEG power spectral densities. Results indicated that prescriptive information (suggesting alternative routes) elicited higher Gamma wave activity in the Occipital and Parietal lobes compared to descriptive information (providing travel times), suggesting higher-level cognitive processing. Furthermore, a significant interaction was found between driver age and information content in the Alpha band of the Temporal lobe. Millennials exhibited higher Alpha activity when receiving prescriptive information, attributed to greater trust in information systems, whereas non-millennials showed lower Alpha activity under the same conditions. The study concludes that real-time information characteristics significantly impact driver physiological states, with prescriptive cues demanding more cognitive resources than descriptive ones. These findings support the development of integrated in-vehicle monitoring systems that can adapt information delivery based on real-time physiological feedback. By understanding how different demographic groups process information, the research provides a foundation for designing systems that maximize tangible and intangible benefits while minimizing cognitive overload. Future work aims to integrate these physiological factors into route choice behavior models and analyze ECG and eye-tracking patterns to further refine understanding of the decision-making process.

Key finding

Drivers receiving prescriptive reroute information showed significantly higher occipital and parietal Gamma-band EEG activity than those receiving descriptive travel-time information (p ranging 0.03 to 0.048).

Methodology

simulator

Sample size: 76

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (7 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• cognitive capacity variation
• in vehicle coaching
• feedback modes

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: physiological data, behavioral performance data
• Methodological Resource: tool software