Work Zone Crash Performance Data Measurement
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Summary
This research addresses the critical safety challenges associated with highway work zones in Tennessee, where increasing vehicle miles traveled and aging infrastructure have led to frequent construction activities and associated crashes. The study was motivated by the Tennessee Department of Transportation’s (TDOT) administrative goal to reduce work zone crashes by incorporating safety metrics into the performance plans of operations staff. The primary objective was to analyze historical crash patterns to develop specific performance metrics and allowable targets for work zone safety, thereby aiding in the planning, design, and regulation of safety measures. The methodology involved the collection and processing of comprehensive data across all 95 counties in Tennessee. Researchers utilized historical and archived crash data maintained by TDOT, alongside work zone project data and roadway segment data, including geometry and traffic characteristics. To predict work zone crashes and identify causal factors, the study employed an Artificial Neural Network Model (ANNM). This machine learning approach was selected for its ability to handle nonlinear multivariate prediction problems where relationships between variables are complex. The model was trained to identify predominant system factors influencing crash density and to estimate the corresponding economic costs of these incidents. The findings resulted in the development of a robust framework for predicting work zone crash density and costs. The ANNM successfully identified key explanatory variables and their relative importance in contributing to crashes. The study produced specific performance measures and allowable targets for each county and region in Tennessee, allowing for granular monitoring of safety performance. The results included detailed analyses of crash frequencies by work zone type (construction, maintenance, utility) and by county, providing a clear picture of where safety interventions are most needed. The model also facilitated the calculation of crash costs based on injury severity levels, offering a financial perspective on the impact of work zone incidents. The significance of this work lies in its provision of actionable tools for TDOT operations staff. By establishing clear performance metrics and targets, the research enables the agency to monitor the effectiveness of implemented safety strategies and evaluate staff efficiency in managing work zone safety. The findings support evidence-based decision-making for education, training, and traffic control measures. Ultimately, the study contributes to the broader field of transportation safety by demonstrating how advanced data analytics and neural networks can be applied to improve work zone safety management and reduce the economic and human costs associated with crashes.
Key finding
An Artificial Neural Network model successfully predicted work zone crash density and economic costs, enabling the development of county-specific safety performance measures and allowable targets for Tennessee.
Methodology
dataset
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.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- incidence prevalence
- work zones
- comparative international
- fatality injury trends
- demographic disparities
- naturalistic crash near crash
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).
- Applied Guidance: countermeasure evaluation
- Empirical Findings: crash risk outcomes
- Methodological Resource: dataset resource