---
license: apache-2.0
task_categories:
- text-generation
- question-answering
language:
- en
tags:
- transit
- gtfs
- transportation
- public-transit
- instruction-tuning
- synthetic
size_categories:
- 1K<n<10K
dataset_info:
  features:
  - name: instruction
    dtype: string
  - name: response
    dtype: string
  - name: category
    dtype: string
  - name: template_id
    dtype: string
  - name: feed_id
    dtype: string
  - name: provider
    dtype: string
  splits:
  - name: train
    num_bytes: 904730
    num_examples: 3154
  - name: test
    num_bytes: 97857
    num_examples: 347
  download_size: 274812
  dataset_size: 1002587
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

# UmarTransit-Instruct-3k

A synthetic instruction-tuning dataset for **public transit systems** and **GTFS (General Transit Feed Specification)**, containing 3,306 question-answer pairs generated from 15 real-world open GTFS feeds across 10 countries.

Built to train domain-specific language models like [UmarTransit-1B](https://huggingface.co/umarfarookm/UmarTransit-1B).

> **Data Disclaimer:** This dataset was generated **exclusively from publicly available, open-source GTFS feeds** published by transit agencies for public use via the [Mobility Database](https://mobilitydatabase.org/). **No private, proprietary, or NDA-protected data** from any client, employer, or organization was used.

## Dataset Details

| Property | Value |
|----------|-------|
| **Total pairs** | 3,306 |
| **Training split** | 2,971 (90%) |
| **Test split** | 335 (10%) |
| **Categories** | 8 task categories |
| **Templates** | 45 question templates |
| **GTFS feeds** | 15 feeds from 10 countries |
| **Format** | JSONL (one JSON object per line) |
| **Language** | English |
| **License** | Apache 2.0 |

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("umarfarookm/UmarTransit-Instruct-3k")

# Access splits
train = dataset["train"]
test = dataset["test"]

# Example
print(train[0]["instruction"])
print(train[0]["response"])
```

## Data Format

Each record contains:

```json
{
  "instruction": "How many routes does Chicago Transit Authority (CTA) have?",
  "response": "Chicago Transit Authority (CTA) operates 133 routes. 4 are Tram/Streetcar/Light rail routes...",
  "category": "agency_overview",
  "template_id": "agency_route_count_v1",
  "feed_id": "389",
  "provider": "Chicago Transit Authority (CTA)"
}
```

| Field | Description |
|-------|-------------|
| `instruction` | The user question |
| `response` | The expected answer |
| `category` | Task category (1 of 8) |
| `template_id` | Which template generated this pair |
| `feed_id` | Source GTFS feed ID from Mobility Database |
| `provider` | Transit agency name |

## Task Categories

| Category | Count | Description |
|----------|-------|-------------|
| agency_overview | 1,075 | Agency transit modes, route counts, timezones |
| stop_info | 911 | Stop locations, coordinates, accessibility |
| schedule | 636 | Trip schedules, departure/arrival times |
| route_info | 457 | Route descriptions, types, trip counts |
| transfer | 161 | Transfer connections, types, wait times |
| network_stats | 30 | Aggregate network statistics |
| gtfs_knowledge | 22 | GTFS specification concepts and definitions |
| comparative | 14 | Cross-agency comparisons |

## Source GTFS Feeds

All feeds are publicly available through the [Mobility Database](https://mobilitydatabase.org/).

| Country | City/Region | Agency | Feed ID |
|---------|-------------|--------|---------|
| US | Los Angeles | LA Metro | 29 |
| US | Chicago | CTA | 389 |
| US | Boston | MBTA | 437 |
| US | Phoenix | Valley Metro | 1086 |
| US | Austin | Capital Metro | 1029 |
| US | Portland | TriMet | 1077 |
| Canada | Toronto | TTC | 247 |
| Germany | Berlin | VBB | 782 |
| France | Paris | Ile-de-France Mobilites | 865 |
| Netherlands | National | OVapi | 1292 |
| Belgium | National | NMBS/SNCB | 732 |
| Finland | Helsinki | HSL | 686 |
| Denmark | National | Rejseplanen | 150 |
| Australia | Perth | Transperth | 1026 |
| New Zealand | Auckland | Auckland Transport | 147 |

## Generation Process

1. **Download** 15 open GTFS feeds from the Mobility Database
2. **Clean** raw CSV data into normalized Parquet format
3. **Extract** feed statistics (routes, stops, trips, transfers, schedules)
4. **Generate** Q&A pairs using 45 templates across 8 categories
5. **Validate** all pairs for format, content quality, and factual accuracy
6. **Split** into train/test (90/10, stratified by category)

All scripts are open-source: [github.com/umarfarookm/transit-foundation-model](https://github.com/umarfarookm/transit-foundation-model)

## Quality Validation

- **Format errors:** 0 / 3,306
- **Duplicate instructions:** 0
- **Factual accuracy:** 100% (275 spot-checks against source data)
- **Average instruction length:** 66 characters
- **Average response length:** 136 characters

## Trained Model

This dataset was used to train [UmarTransit-1B](https://huggingface.co/umarfarookm/UmarTransit-1B), which shows a **+74% improvement** over the base model (Qwen2.5-1.5B-Instruct) on a 193-question benchmark evaluation.

## Limitations

- **English only** — no multilingual coverage
- **Static schedules** — no real-time or delay data
- **Template-based** — all Q&A pairs follow fixed templates, limiting response diversity
- **15 feeds** — does not cover all transit agencies worldwide
- **Small scale** — 3,306 pairs is modest compared to general instruction datasets

## Citation

```bibtex
@dataset{umartransit_instruct_3k,
  author = {Umar Farook M},
  title = {UmarTransit-Instruct-3k: Transit and GTFS Instruction Dataset},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/umarfarookm/UmarTransit-Instruct-3k}
}
```