Add dataset card

README.md

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+---
+language:
+- en
+license: mit
+task_categories:
+- object-detection
+task_ids:
+- object-detection
+pretty_name: CATI Singapore Expressway Traffic Dataset
+size_categories:
+- 10K<n<100K
+tags:
+- traffic
+- singapore
+- expressway
+- smart-city
+- vehicle-detection
+- computer-vision
+- lta
+---
+
+# CATI Singapore Expressway Traffic Dataset
+
+Real-time vehicle detection data collected from Singapore's 90 LTA traffic cameras using **CATI (Context-Aware Traffic Intelligence)** — a novel FiLM-conditioned YOLOv11 detector that adapts to environmental conditions in real time.
+
+## Dataset Description
+
+This dataset contains per-camera vehicle detection results collected continuously from Singapore's Land Transport Authority (LTA) expressway camera network. Each record captures a full detection sweep of a single camera including vehicle counts, class breakdown, directional split, and environmental context.
+
+### Coverage
+
+| Expressway | Cameras |
+|-----------|---------|
+| CTE (Central Expressway) | ~16 |
+| PIE (Pan-Island Expressway) | ~20 |
+| AYE (Ayer Rajah Expressway) | ~8 |
+| ECP (East Coast Parkway) | ~10 |
+| MCE (Marina Coastal Expressway) | ~4 |
+| TPE (Tampines Expressway) | ~8 |
+| BKE (Bukit Timah Expressway) | ~6 |
+| KJE (Kranji Expressway) | ~6 |
+| SLE (Seletar Expressway) | ~6 |
+
+## Data Schema
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `timestamp` | string | Detection timestamp (SGT, UTC+8) |
+| `camera_id` | int | LTA camera ID |
+| `road` | string | Expressway code (CTE, PIE, AYE, etc.) |
+| `lat` | float | Camera latitude (WGS84) |
+| `lon` | float | Camera longitude (WGS84) |
+| `weather` | string | Central Singapore weather at time of capture |
+| `total_vehicles` | int | Total vehicles detected |
+| `dir_a` | int | Vehicles moving in direction A (tracked via 2-frame IoU) |
+| `dir_b` | int | Vehicles moving in direction B |
+| `car` | int | Cars detected |
+| `motorcycle` | int | Motorcycles detected |
+| `bus` | int | Buses detected |
+| `truck` | int | Trucks detected |
+| `van` | int | Vans detected |
+| `lorry` | int | Lorries detected |
+
+## Model
+
+Detections are produced by **CATI** — a novel architecture that injects FiLM (Feature-wise Linear Modulation) layers into YOLOv11s, conditioning the backbone on real-time environmental metadata:
+
+- Weather condition and temperature (data.gov.sg)
+- Time of day (cyclical encoding)
+- Camera GPS position (sinusoidal positional encoding)
+- PM2.5 air quality
+- Camera resolution class
+
+Model weights: [SuhxsReddy/cati-singapore](https://huggingface.co/SuhxsReddy/cati-singapore)
+
+## How to Load
+
+```python
+from datasets import load_dataset
+import pandas as pd
+
+ds = load_dataset("SuhxsReddy/cati-singapore-dataset")
+df = ds["train"].to_pandas()
+
+# Vehicles by road
+print(df.groupby("road")["total_vehicles"].mean())
+
+# Directional flow on CTE
+cte = df[df["road"] == "CTE"]
+print(cte[["timestamp", "camera_id", "dir_a", "dir_b", "total_vehicles"]])
+```
+
+## Collection
+
+- **Sweep interval**: ~90 seconds (full network scan)
+- **Detection threshold**: confidence ≥ 0.08, IoU ≤ 0.25
+- **Image source**: LTA Datamall Traffic Images API
+- **Infrastructure**: HuggingFace Spaces (CPU), continuous 24/7 collection
+
+## License
+
+MIT — data sourced from Singapore's open data APIs under the [Singapore Open Data Licence](https://data.gov.sg/open-data-licence).
+
+## Citation
+
+```bibtex
+@dataset{cati_singapore_2026,
+  author    = {SuhxsReddy},
+  title     = {CATI Singapore Expressway Traffic Dataset},
+  year      = {2026},
+  publisher = {HuggingFace},
+  url       = {https://huggingface.co/datasets/SuhxsReddy/cati-singapore-dataset}
+}
+```