Optimizing Future Work Zones in New England for Improved Safety and Mobility
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Summary
This study addresses the critical challenges of safety and mobility in highway work zones, motivated by the deteriorating infrastructure condition in New England and the associated increase in construction activities. The research focuses on optimizing speed control and merge control strategies to mitigate congestion and crash risks, particularly rear-end and angle crashes caused by stop-and-go traffic and unsafe merging. The authors identify limitations in existing research, noting that traditional crash data analysis fails to capture pre-crash driver behaviors, while traditional driving simulators lack the fidelity required for credible behavioral data. To address these gaps, the study employs a multi-method approach. First, it analyzes Naturalistic Driving Study (NDS) data to characterize crash and near-crash events, identifying endogenous factors like distraction, fatigue, and speeding, as well as exogenous factors such as sudden slowdowns and unsafe merging. Second, the researchers developed and utilized a Virtual Reality (VR) driving simulator to evaluate three speed control strategies under daytime and nighttime conditions, supplemented by driver surveys. Third, the study proposes a novel "New England Merge" (NEM) control strategy, which divides the approach to a work zone into a "meter zone" (where vehicles increase gaps and lane changes are prohibited) and a "merge zone." The performance of NEM was evaluated against conventional methods (no control, early merge, late merge, and signalized merge) using VISSIM microsimulation and the Surrogate Safety Assessment Model (SSAM) for two work zone types: two-lane highways with one lane closed and three-lane highways with one lane closed. The findings reveal that distraction is the primary endogenous factor contributing to work zone crashes, followed by fatigue and speeding, with crashes most likely occurring during Levels of Service B, C, and D. The VR simulation and survey results indicate that radar speed signs and dynamic speed displays effectively reduce speeds, while tubular markers implicitly reduce speed variation. Regarding merge control, the NEM strategy demonstrated superior performance compared to existing methods. VISSIM simulations showed that NEM significantly outperformed no control, early merge, and late merge under medium to extremely heavy traffic conditions, and consistently outperformed signalized merge across all flow conditions in terms of delay, throughput, and queue length. SSAM analysis confirmed that NEM generated fewer rear-end conflicts under medium to high input flows. Although NEM resulted in higher lane-change conflicts under heavy flow, this was attributed to its higher throughput and the distribution of lane changes prior to the merge point rather than at the conflict zone. The significance of this research lies in the development of the NEM strategy, which offers a practical solution for improving work zone safety and mobility. The study concludes that NEM can be readily implemented with connected and automated vehicles but remains practical for current traffic conditions given driver compliance and law enforcement. The integration of NDS data analysis, VR simulation, and microsimulation provides a comprehensive framework for evaluating work zone controls, offering actionable insights for transportation agencies aiming to enhance safety and efficiency in future infrastructure projects.
Key finding
The New England Merge strategy significantly outperformed early, late, and signalized merge methods in reducing conflicts and improving throughput under medium to heavy traffic conditions.
Methodology
mixed_methods
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 | 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: crash risk outcomes