Accident severity levels and traffic signs interactions in state roads: a seemingly unrelated regression model in unbalanced panel data approach
DOI: 10.1016/j.aap.2018.07.037
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Summary
This document is a master’s thesis titled “Road safety evaluation on the national roads of south and central zone of Caldas, Colombia, and improvement proposals related with the infrastructure characteristics of the roads,” authored by Juan Felipe Gallego Zuluaga in 2020. The research addresses the need to diagnose traffic accident severity and identify high-risk zones on national roads in the central-southern region of the Caldas department. The study is motivated by the goal of improving road safety indicators through targeted infrastructure interventions, aligned with the five pillars of Colombia’s National Road Safety Plan (2011–2021). The methodology involves a historical analysis of traffic accident data collected between 2013 and 2017. Data were sourced from multiple entities, including the National Observatory of Road Safety (ONSV), the National Roads Institute (INVIAS), road concessions such as Autopistas del Café and Pacífico Tres, the Territorial Health Directorate, and local transit secretaries in Manizales and Chinchiná. The study defines specific road segments (tramos) within the study area and calculates the Equivalent Accident Number (NAE) to quantify accident severity. This metric allows for the comparison of accident frequency and severity across different road sections. The analysis identifies zones of high accidentality by evaluating NAE values across sectors of varying lengths (500 meters, 1 kilometer, and 2 kilometers). The findings provide a detailed diagnosis of accident patterns across thirteen specific road segments, such as Chinchiná–Estación Uribe, Estación Uribe–Puente la Libertad, and Tres Puertas–La Manuela. For each segment, the study pinpoints specific abscissas (road coordinates) with the highest NAE values, indicating critical hotspots. For instance, in the Chinchiná–Estación Uribe segment, high accidentality zones were identified between abscissas 31+600 and 32+099, among others. The analysis reveals the distribution of accidents by severity and type, highlighting infrastructure-related factors contributing to crashes. The study maps these high-risk zones and assesses the current state of road infrastructure, including geometric design, signage, and pavement conditions, at these locations. The significance of this work lies in its actionable proposals for road safety improvement. Based on the identified high-accidentality zones, the author suggests specific interventions within the framework of the National Road Safety Plan, with a primary emphasis on the infrastructure pillar. Recommendations include improvements to vertical and horizontal signage, road markings, visibility distances, and containment barriers. The study also considers institutional management, vehicle safety, human behavior, and victim care. By linking specific accident data to infrastructure deficiencies, the thesis provides a evidence-based roadmap for reducing traffic fatalities and injuries in the Caldas department, offering a model for systematic road safety evaluation and targeted infrastructure upgrades.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-20 |
| archive | success | semantic_scholar | — | — | 6 | 2026-06-26 |
| 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 | — | — | 1 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| 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