README.md

---
language:
- en
license: mit
task_categories:
- object-detection
task_ids:
- vehicle-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 |
| `conf_threshold` | float | Detection confidence threshold used |
| `iou_threshold` | float | NMS IoU threshold used |
| `imgsz` | int | Inference image size (px) |
| `model_version` | string | Model checkpoint identifier |
| `dir_a_label` | string | Human-readable direction A label (e.g. "towards Woodlands") |
| `dir_b_label` | string | Human-readable direction B label (e.g. "towards City") |
| `is_ramp` | bool | True if camera is likely on an entry/exit ramp (proximity heuristic) |

> **Versioning note**: Filter by `conf_threshold`, `iou_threshold`, and `imgsz` to isolate consistent collection windows. Exclude `is_ramp=True` rows for mainline-only throughput analysis.

## 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}
}
```