Quantifying the Impacts of Real Time Travel Information on Route Choice Behavior Using Psychophysiological Analysis
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 paper addresses the gap in understanding how real-time travel information influences driver route choice behavior by focusing on the mediating role of driver cognition. Traditional route choice models rely on rational choice theories and observable factors such as road characteristics, traffic conditions, and individual demographics. However, these models often overlook the cognitive state of the driver, which is difficult to measure objectively. Self-reported metrics are prone to bias, and existing research lacks tangible methods to quantify how information consumption affects mental workload and engagement. The authors propose using psychophysiological analysis to bridge this gap, aiming to determine how the amount, content, and source of real-time information impact driver psychology and subsequent decision-making. To investigate this, the researchers designed a driving simulator experiment set in a real-world road network in Northern Indianapolis. The study utilizes a multi-source information environment, delivering travel data via variable message signs (VMS), on-screen displays, speakers, and personal devices like smartphones and GPS units. The core methodology involves collecting physiological data using biosensors to estimate cognitive states objectively. Specifically, electroencephalogram (EEG) sensors record brain electrical activity to compute mental workload and sensory engagement. Electrocardiogram (ECG) sensors monitor heart rate and variability to assess stress levels. Additionally, eye-tracking technology records gaze patterns, blink rates, and pupil size to identify areas of interest, such as traffic signals or information displays. The analytical framework integrates EEG and eye-tracking data to provide a comprehensive view of driver cognition. While EEG data estimates the level of cognitive state, it cannot determine the cause of that state. By combining this with eye-tracking data, the researchers can infer whether cognitive load is driven by driving tasks or non-driving activities, such as processing travel information. This integrated approach allows for the segregation of cognitive states caused by different stimuli, offering a more precise understanding of how real-time information consumes cognitive resources. The experimental design includes specific participant criteria, requiring individuals to be over 18, free of motion sickness or impairments, and not wearing corrective glasses, ensuring data quality. The significance of this research lies in its potential to refine route choice models by incorporating latent variables related to driver psychology. By quantifying the cognitive impacts of Advanced Traveler Information Systems (ATIS), the study aims to explain how information overload or specific information sources affect driving performance and route selection. This approach moves beyond static parameters to capture the dynamic interplay between information provision and driver cognition. The findings are expected to improve the accuracy of hybrid route choice models and provide insights into the design of more effective, cognitively aware travel information systems.
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 (8 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 6 | 2026-06-15 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 8 | 2026-06-15 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-06-15 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-15; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
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
- Theoretical Contribution: computational model