Advanced decision modeling for real time variable tolling : development and testing of a data collection platform.
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 report addresses the limitations of current economic decision models in forecasting driver demand for High Occupancy Tolled (HOT) lanes. Traditional methods, such as Expected Utility Theory and Random Utility Theory, rely on simplifications that fail to capture actual driver behavior under risk and ambiguity, leading to inaccurate revenue and congestion forecasts. The study aims to bridge this gap by developing a mobile data collection platform capable of capturing naturalistic driving behavior and real-time decision-making processes. By leveraging advances in smartphone technology, the research seeks to gather empirical data on how drivers perceive and react to variable tolls, which serve as ambiguous signals regarding travel time savings. The methodology involves the development and testing of an in-vehicle perception acquisition device based on iOS technology. This software application allows drivers to upload the tool to their existing smartphones or tablets, enabling real-time data collection without vehicle modification. The system captures dynamic data, including the time and location of lane-choice decisions, the specific toll price at that moment, and verbal responses to audio prompts regarding expected delays or savings. To test the prototype, the researchers created a virtual HOT facility on U.S. 218 in Johnson County, Iowa, designed to replicate the operational attributes of the I-394 HOT facility in Minneapolis. The testing process involved field trials where the system recorded driver interactions, geofence-triggered prompts, and distance traveled during data recording, while also integrating external traffic data from the Minnesota Department of Transportation. The findings demonstrate the successful development and functional testing of the mobile data collection platform. The prototype effectively captured naturalistic choice outcomes, environmental states, and drivers’ self-articulated perceptions of risk and ambiguity. The field tests on the virtual HOT facility confirmed that the system could trigger prompts based on geofencing and record relevant behavioral data, such as the distance traveled while responding to questions. The report highlights that this technology facilitates the collection of experience-based data, which is crucial for dynamic decision-making models like Expectancy-Valence Theory, as opposed to static, description-based models. The significance of this work lies in its potential to improve the accuracy of transportation revenue and congestion forecasts by replacing prescriptive rational models with descriptive behavioral models. By capturing how drivers actually use imprecise information signaled by variable tolls, the platform supports the development of superior behavioral models that account for cognitive biases, such as loss aversion and probability weighting. This approach allows for a more realistic understanding of driver hesitancy and risk-seeking behaviors in dynamic traffic environments. The study serves as the first phase of a larger naturalistic driving study, providing the necessary infrastructure to test and implement new models that explicitly describe decision-making under ambiguity, ultimately enhancing the management and funding strategies for tolled facilities.
Key finding
The study developed and field-tested a smartphone-based data collection platform capable of capturing naturalistic driver choices and perceptions in real-time without altering vehicles.
Methodology
field_study
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 (6 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 | — | — | 2 | 2026-06-10 |
| 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 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
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.
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: behavioral performance data, observational prevalence
- Theoretical Contribution: computational model