A MODEL SUGGESTION FOR THE DETERMINATION OF THE TRAFFIC ACCIDENT HOTSPOTS ON THE TURKISH HIGHWAY ROAD NETWORK: A PILOT STUDY
DOI: 10.1590/s1982-21702015000100011
archive: archived pipeline: cataloged verified
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Summary
This pilot study addresses the critical need for accurate traffic accident hotspot identification on the Turkish highway network to improve road safety and allocate limited countermeasure budgets effectively. While traditional methods like crash frequency, rate, and severity are commonly used, they suffer from limitations such as random variation and incorrect classification. The authors aim to develop a GIS-based crash analysis system by comparing these traditional approaches with spatial statistical methods, including Moran’s I, Getis-Ord G, and planar and network kernel density estimation (KDE). The study evaluates the sensitivity of these seven methods to the spatial characteristics of crash clusters to propose a more robust model for hotspot detection. The research utilized fatal and injury accident data from the Afyonkarahisar and Konya provinces in Turkey for the period of 2005–2012, comprising 9,217 records. The methodology involved applying each of the seven detection methods to identify hotspots and analyzing their performance based on the repetitiveness of identified locations over the seven-year period. Traditional methods were assessed using critical values for frequency, rate, and severity. Spatial statistical methods included local spatial autocorrelation (Moran’s I and Getis-Ord G) and density analysis (planar and network KDE), with the latter implemented using ArcGIS and the SANET software tool. The study examined how each method performed in different road geometries, such as junctions versus straight road segments, and evaluated their ability to distinguish true hotspots from random fluctuations. The results revealed significant differences in the performance of the methods. The crash severity method identified the highest number of hotspots (656 segments) but was highly sensitive to random, multi-vehicle accidents, with most identified hotspots appearing in only a single year. Conversely, crash frequency and rate methods identified the fewest hotspots but were prone to deceptive results due to traffic volume variations. Spatial statistical methods showed distinct strengths based on location type: Getis-Ord G was sensitive to point clusters but often produced large, indistinct areas at junctions, while Moran’s I was more effective for junctions but deceptive in areal clusters. Planar KDE provided strong visual detection but lacked formal statistical inference. Notably, planar and network KDE yielded similar results, though network KDE struggled with visualizing areal clusters. The study concludes that no single method is optimal for all scenarios. Instead, a combined approach improves accuracy: using planar KDE with Getis-Ord G for junction locations and planar KDE with Moran’s I for straight road segments. This finding suggests that integrating traditional and spatial statistical methods can mitigate the weaknesses of individual approaches. As the first stage of a larger project, this pilot study lays the groundwork for future research incorporating Poisson regression, negative binomial regression, and Empirical Bayesian methods to further refine the hotspot detection model for Turkey’s highway safety programs.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | OpenAlex-citations | — | — | 1 | 2026-06-19 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-19 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| 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