Black Spot Determination of Traffic Accident Locations and Its Spatial Association Characteristic Analysis Based on GIS
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Summary
This paper addresses the challenge of identifying traffic accident "black spots" (accident-prone locations) by integrating Geographic Information Systems (GIS) with spatial analysis. The authors note that traditional traffic safety analysis relies on general statistics, which lack the capability to display spatial distributions or analyze relationships between accidents and road network elements. To overcome this, the study proposes a method to convert text-based accident addresses into geospatial coordinates, enabling the extraction of black spots based on the potential for accident reduction and the analysis of contributing road attributes. The methodology involves two primary technical processes: mobile positioning and geocoding. For real-time data capture, the authors utilize a Virtual Reference Station (VRS)-based mobile positioning system comprising a VRS network, a GPRS communication network, and a user terminal with a Bluetooth GPS receiver and PDA. This setup allows for high-precision location recording. For historical data described only by text addresses, the study employs geocoding technology. This process includes address parsing and standardizing—dividing addresses into fields such as main road, intersecting road, keyword, and distance—and address matching using ArcGIS. The road network is processed into distinct layers for intersections and microscopic road sections to facilitate spatial relation computation. Black spots are identified using the "potential of reducing accidents" index, defined as the difference between the actual number of accidents and the average number of accidents for similar road facilities. Intersections are classified by branch number and control systems, while road sections are classified by urban/suburban status, road level, and directionality. The study applied this method to one year of traffic accident data in a specific region, successfully identifying thirty black spots. The results of the association analysis reveal specific correlations between black spots and road attributes. For microscopic road sections, accidents were more frequent on roads with lower levels, smaller traffic volumes, and smaller intersection spacing. The authors explain that lower road levels often have worse conditions, while lower traffic volumes allow drivers to speed without interference, and smaller intersection spacing disrupts lane changes. For intersections, the analysis focused on the number of branches and average traffic volume. The study concludes that GIS-based spatial analysis effectively identifies accident-prone areas and links them to specific road infrastructure characteristics, providing a scientific basis for targeted traffic safety improvements.
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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