| --- |
| language: |
| - en |
| pipeline_tag: token-classification |
| tags: |
| - named-entity-recognition |
| - ner |
| - nlp |
| - spacy |
| - road-traffic-crash |
| - road-safety |
| - information-extraction |
| - geospatial |
| - nigeria |
| --- |
| |
| # RTC-NER |
|
|
| **rtc-ner** is a domain-specific Named Entity Recognition (NER) model developed for extracting road traffic crash (RTC)-related geographic, medical, and incident entities from unstructured textual road traffic crash reports. |
|
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| The model is designed to transform narrative RTC reports into structured information that can subsequently support road traffic crash analysis, geospatial processing, and emergency-response research. |
|
|
| ## Model Description |
|
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| rtc-ner is a transformer-based spaCy NER model trained for the identification of domain-specific entities occurring in road traffic crash narratives. |
|
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| The model focuses particularly on extracting geographic locations and selected incident-related entities that are relevant to the analysis and geospatial structuring of RTC reports. |
|
|
| ### Entity Labels |
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| The model recognizes the following entity types: |
|
|
| | Entity | Description | |
| | ---------- | ----------------------------------------------------------------- | |
| | `ROAD` | Road, highway, street, expressway, or other road-related location | |
| | `LANDMARK` | Named or identifiable landmark mentioned in an RTC report | |
| | `SUBURB` | Suburb or locality within a larger urban area | |
| | `TOWN` | Town or city/locality | |
| | `LGA` | Local Government Area | |
| | `STATE` | Nigerian state or other state-level administrative location | |
| | `HOSPITAL` | Hospital or healthcare facility mentioned in the report | |
| | `CASUALTY` | Person reported as a casualty in the crash | |
| | `INJURED` | Person reported as injured in the crash | |
|
|
| ## Intended Use |
|
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| rtc-ner is intended for research and information-extraction applications involving road traffic crash narratives, including: |
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| * Extraction of geographic entities from RTC reports |
| * Structuring unstructured crash narratives |
| * Identification of locations for subsequent geocoding |
| * Road traffic crash database construction |
| * Road safety research |
| * Natural language processing of RTC reports |
| * Preparation of textual RTC data for geospatial analysis |
| * Research on emergency response and road traffic crash management |
|
|
| ## Model Architecture |
|
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| The model was developed using the spaCy NLP framework with a transformer-based architecture. |
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| The repository contains the trained spaCy pipeline and its associated configuration, vocabulary, tokenizer, and model components. |
|
|
| ## Model Files |
|
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| The repository contains the complete trained spaCy model, including: |
|
|
| ```text |
| config.cfg |
| meta.json |
| tokenizer |
| vocab/ |
| ner/ |
| tok2vec/ |
| ``` |
|
|
| These files should be retained together when loading the model. |
|
|
| ## Model Usage Test |
| The `RTC_NER_model_test.ipynb` contains the code for testing usage of the model. |
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| The final output contains the entities identified by the model together with their corresponding entity labels. |
|
|
| ## Limitations |
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| rtc-ner is a domain-specific research model and should not be assumed to generalize perfectly to all forms of text. |
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| Performance may be affected by: |
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| * Differences in writing style between training and application data |
| * Spelling variations and typographical errors |
| * Ambiguous place names |
| * Unreported or implicitly mentioned locations |
| * Differences between news reports and other forms of RTC narratives |
| * Geographic names that have multiple interpretations |
| * Domain shifts outside Nigerian road traffic crash reporting |
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| Extracted geographic entities should therefore be reviewed or validated before being used for high-precision geospatial applications. |
|
|
| ## Research Context |
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| rtc-ner was developed as part of research investigating the transformation of unstructured road traffic crash narratives into structured, machine-readable information for road safety and emergency-response applications. |
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| The model is intended to serve as an information-extraction component within a broader research workflow involving RTC data curation, geospatial processing, and artificial intelligence. |
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| The code for model training and evaluation as well as data extraction can be found at: |
| https://github.com/PatUnoka/Geospatial-and-Contextual-Information-Extraction-from-Road-Traffic-Crash-Narratives.git |
|
|
| ## Citation |
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| If you use rtc-ner in academic research, please cite the associated research publication and dataset from which the model was developed. |
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|
| [1] P. O. Idakwo, O. Adekanmbi, A. Soronnadi, and A. David, “Geo-parsing and analysis of road traffic crash incidents for data-driven emergency response planning,” Heliyon, vol. 11, no. 4, p. e41067, 2025, doi: 10.1016/j.heliyon.2024.e41067. |
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| [2] P. O. Idakwo, O. Adekanmbi, and A. David, “Nigerian Multi-modal Road Traffic Crash Data,” 2026, doi: 10.5281/ZENODO.15862127. |
|
|
| ## Disclaimer |
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| ## This model is provided for research purposes. Predictions generated by the model should be treated as automated information-extraction results and should be independently validated before being used in operational, safety-critical, medical, or emergency-response decision-making. |
