---
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}
}
```