| --- |
| license: mit |
| language: |
| - pt |
| tags: |
| - tabular-classification |
| - graph-theory |
| - urban-mobility |
| - public-transport |
| - scikit-learn |
| - sao-paulo |
| - brazil |
| library_name: sklearn |
| datasets: |
| - cintia-shinoda/sp-transit-network-centrality |
| metrics: |
| - f1 |
| - accuracy |
| --- |
| |
| # SP Transit Node Classifier |
|
|
| Classifies bus stops in São Paulo's transit network as **Hub**, **Intermediate**, or **Peripheral** based on graph features and geographic coordinates. |
|
|
| The goal: **predict betweenness centrality class without computing betweenness itself** (which is computationally expensive for large networks). |
|
|
| ## How to Use |
| ```python |
| import joblib |
| import numpy as np |
| from huggingface_hub import hf_hub_download |
| |
| path = hf_hub_download( |
| repo_id="cintia-shinoda/sp-transit-node-classifier", |
| filename="model.joblib", |
| ) |
| model = joblib.load(path) |
| |
| # Input: [degree, degree_centrality, closeness_centrality, lat, lon] |
| node = np.array([[8, 0.00036, 0.018, -23.55, -46.63]]) |
| pred = model.predict(node) |
| # 0 = Peripheral, 1 = Intermediate, 2 = Hub |
| ``` |
|
|
| ## Features |
|
|
| | Feature | Description | |
| |---------|-------------| |
| | degree | Number of direct connections | |
| | degree_centrality | Normalized degree centrality | |
| | closeness_centrality | Closeness centrality | |
| | lat | Latitude | |
| | lon | Longitude | |
|
|
| ## Metrics |
|
|
| | Metric | Value | |
| |--------|-------| |
| | F1 Macro (test) | 0.59 | |
| | Accuracy (test) | 0.68 | |
| | F1 Macro (5-fold CV) | 0.43 | |
|
|
| ## Feature Importance |
|
|
| | Feature | Importance | |
| |---------|-----------| |
| | lat | 0.2793 | |
| | lon | 0.2604 | |
| | closeness_centrality | 0.2566 | |
| | degree | 0.1061 | |
| | degree_centrality | 0.0976 | |
|
|
| ## Key Finding |
|
|
| Geographic position (lat/lon) is the strongest predictor of hub status, confirming that high-centrality stops concentrate in specific corridors of São Paulo. |
|
|
| ## Limitations |
|
|
| - Labels derived from betweenness centrality quantiles — simplified classification |
| - Trained on a single GTFS snapshot — may not generalize to network changes |
| - Does not consider temporal patterns (peak vs. off-peak) |
| - Class imbalance: 66% Peripheral, 24% Intermediate, 10% Hub |
|
|
| ## Dataset |
|
|
| [SP Transit Network Centrality](https://huggingface.co/datasets/cintia-shinoda/sp-transit-network-centrality) — 21,892 bus stops with graph centrality metrics. |
|
|
| ## Citation |
| ```bibtex |
| @misc{shinoda2026sp-classifier, |
| author = {Cintia Shinoda}, |
| title = {SP Transit Node Classifier}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| url = {https://huggingface.co/cintia-shinoda/sp-transit-node-classifier} |
| } |
| ``` |
