pidakwo/rtc-ner
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.
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
rtc-ner is a transformer-based spaCy NER model trained for the identification of domain-specific entities occurring in road traffic crash narratives.
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
The model recognizes the following entity types:
Intended Use
rtc-ner is intended for research and information-extraction applications involving road traffic crash narratives, including:
- 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
The model was developed using the spaCy NLP framework with a transformer-based architecture.
The repository contains the trained spaCy pipeline and its associated configuration, vocabulary, tokenizer, and model components.
Model Files
The repository contains the complete trained spaCy model, including:
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.
The final output contains the entities identified by the model together with their corresponding entity labels.
Limitations
rtc-ner is a domain-specific research model and should not be assumed to generalize perfectly to all forms of text.
Performance may be affected by:
- 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
Extracted geographic entities should therefore be reviewed or validated before being used for high-precision geospatial applications.
Research Context
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.
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.
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
If you use rtc-ner in academic research, please cite the associated research publication and dataset from which the model was developed.
[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.
[2] P. O. Idakwo, O. Adekanmbi, and A. David, “Nigerian Multi-modal Road Traffic Crash Data,” 2026, doi: 10.5281/ZENODO.15862127.