Incorporating Traffic Control and Safety Hardware Performance Functions into Risk-based Highway Safety Analysis
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Summary
This study addresses a critical limitation in existing highway safety analysis methods: the failure to account for the time-dependent deterioration of traffic control and safety hardware. While traditional risk-based models estimate crash frequencies and severities, they often treat the condition of hardware—such as signs, signals, pavement markings, and barriers—as static or average values. This oversight is problematic because these components have significantly shorter service lives than the highway infrastructure itself, leading to frequent replacements and varying safety impacts over time. The authors aim to refine risk-based safety analysis by incorporating specific performance functions for these hardware categories to more accurately predict crash risks throughout the hardware's lifecycle. The proposed method computes a Safety Index (SI) that integrates traffic exposure, crash frequency, and crash severity factors. The methodology involves categorizing highway segments by land area and functional class, then computing crash risk factors attributable to geometric design, traffic operations, roadside features, and traffic control hardware. Crucially, the study utilizes time-varying performance functions to model the degradation of hardware conditions. For traffic signs, retroreflectivity is modeled based on age, heating degree-days, precipitation, and elevation. For LED traffic signals, light intensity degradation is modeled using manufacturer-specific linear functions and service life thresholds. Pavement marking performance is modeled using Cumulative Traffic Passages (CTP) to predict retroreflectivity decay for flat thermoplastic and profiled markings. These performance metrics are used to calculate the relative increase in crash frequency and severity over time, replacing static assumptions with dynamic variables. The method was validated using five-year data from nearly 200 rural and urban highway segments. The authors employed root-mean square error (RMSE), Chi-square tests, Spearman’s rank correlation, and Mann-Whitney U tests to validate the computed SI values against historical crash records and Empirical Bayesian before-after analysis results. The study demonstrates that treating hardware condition as a time-dependent variable allows for a more precise estimation of how deteriorating safety hardware contributes to increased crash frequency and severity. By linking specific deterioration rates of signs, signals, and markings to crash risk factors, the model captures the variability in safety performance that static models miss. The significance of this research lies in its contribution to holistic highway safety management. By integrating performance-based considerations into risk analysis, transportation agencies can better prioritize economically feasible safety improvement projects under budget constraints. The refined method provides a more realistic tool for estimating the benefits of safety hardware maintenance and replacement, ensuring that safety investments are timed effectively to maximize user safety benefits. This approach bridges the gap between infrastructure lifecycle management and crash prediction, offering a robust framework for evaluating the long-term safety impacts of traffic control and safety hardware.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | DOAJ | — | — | 1 | 2026-06-19 |
| archive | success | openalex | — | — | 4 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-19 |
| chunk | success | chunk | — | — | 1 | 2026-06-19 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-19 |
| promote | success | — | — | — | 1 | 2026-06-19 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-19 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Empirical Findings: crash risk outcomes