The interurban DRAG-Spain model: The main factors of influence on road accidents in Spain
DOI: 10.1016/j.retrec.2011.08.011
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Summary
This paper presents the development and results of the interurban DRAG-Spain model, a macroscopic statistical framework designed to identify the primary factors influencing injury and fatal road accidents in Spain. Motivated by the need for scientific methods to evaluate road safety policies and predict accident trends, the study addresses the complexity of multiple interacting variables—such as exposure, infrastructure, weather, and driver behavior—that affect accident rates. The research aims to provide a robust tool for analyzing the causal factors behind accident frequencies and severities on Spain’s interurban network, where over 80% of fatal accidents occur. The methodology employs the DRAG (Demande Routière, Accidents et Gravité) approach, which utilizes a multi-layer recursive structure to model exposure, accident frequency, and severity separately. The model was fitted using monthly time-series data from January 1990 to December 2004, comprising 180 observations. Nineteen independent variables were selected from ten categories, including exposure, infrastructure, weather, drivers, economic conditions, vehicle stock, surveillance, speed, and legislative measures. Due to data limitations and multicollinearity issues, several variables were excluded or proxied; for instance, speed was represented by radar check positives, and vehicle age was used instead of ABS/ESP penetration in certain contexts. The statistical model uses Box-Cox transformations to determine functional forms and corrects for heteroscedasticity and autocorrelation. The results reveal that exposure, measured by vehicle-kilometers traveled, has the greatest influence on accident rates, with a 10% increase in exposure leading to a 7.16% rise in accidents with injuries and a 7.66% rise in fatal accidents. Inexperienced drivers, higher speeds (indicated by radar positives), and an aging vehicle stock negatively impact safety, significantly increasing accident risks. Conversely, increased surveillance (traffic officers and alcohol checks), the proportion of high-capacity networks, and vehicle safety features like ABS have positive effects, reducing accident frequencies. For example, a 10% increase in the length of high-capacity networks reduces fatal accidents by 2.87%. Weather variables showed mixed results, with fog and snow-covered ground slightly increasing injury accidents, while precipitation reduced fatal accidents. Legislative measures, such as the 1992 Traffic Act and reduced alcohol limits, also demonstrated significant negative elasticities, indicating a reduction in accidents. The significance of this study lies in its ability to quantify the specific impacts of various road safety factors, allowing for more informed policy-making. By distinguishing between factors that increase risk (e.g., exposure, inexperienced drivers) and those that mitigate it (e.g., surveillance, infrastructure improvements), the model provides a basis for evaluating the effectiveness of current and future safety strategies. The findings underscore the importance of managing exposure and enhancing surveillance and infrastructure to reduce both injury and fatal accidents, offering a validated framework for macroscopic accident prediction in Spain.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-18 |
| archive | success | unpaywall | — | — | 2 | 2026-06-25 |
| extract | success | pdftotext | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | semantic_scholar | — | — | 4 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-18 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| 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, observational prevalence