Evaluation of Work Zone Mobility by Utilizing Naturalistic Driving Study Data [supporting dataset]
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Summary
This document serves as a metadata record and supporting dataset description for the research report titled "Evaluation of Work Zone Mobility by Utilizing Naturalistic Driving Study Data," conducted under the STRIDE Project B2. The primary objective of the associated research is to evaluate work zone mobility using data from the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS). The study aims to verify and calibrate the work zone capacity methods defined in the Highway Capacity Manual (HCM) by analyzing real-world driving conditions. The methodology involves the collection of forward and rear-view video footage, along with time series traces, for traversed work zones at 0.1-second intervals. Researchers utilized these video recordings to identify specific work zone configurations, including traffic control devices, area types, the presence of dynamic message signs (DMS), intelligent transportation systems (ITS) technologies, and the presence of workers and equipment. Additionally, the videos were used to estimate traffic density by observing the number of vehicles surrounding the participant vehicle and to determine average traffic flow based on observed speed and traffic density. The analytical approach focuses on developing speed-flow relationships to estimate the capacities of different work zone sections and configurations. These estimated capacities are then used to verify and calibrate the work zone capacity method outlined in the HCM. Furthermore, the study estimates the probability of breakdown for each level of flow rates, allowing volume-to-capacity ratios to be used to verify HCM results. The original SHRP2 data utilized in this study is restricted and available only through a Data Use License, meaning it cannot be uploaded to the public repository. Instead, the supporting dataset available via Zenodo and Dataverse contains a single .docx file (STRIDE Project B2.docx) with a file size of 13.1 kB. The significance of this work lies in its contribution to freeway management and optimal reliability by providing empirical validation for highway capacity models. By leveraging naturalistic driving data, the research offers a detailed assessment of how various work zone configurations impact mobility and traffic flow. However, the provided text notes that there is no data dictionary or other documentation available for the dataset as of January 2021, and the National Transportation Library assumes no liability for the data or software provided by the researchers. The project was funded by the U.S. Department of Transportation and conducted between August 2018 and August 2019.
Key finding
The document describes a dataset supporting a study that used naturalistic driving data to calibrate and verify work zone capacity methods defined in the Highway Capacity Manual.
Methodology
naturalistic
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 (9 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 | — | — | 5 | 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 | 8 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | partial | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified_with_issues.
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- Empirical Findings: observational prevalence, crash risk outcomes
- Methodological Resource: dataset resource