Public-sample guide
Documentation
Use the sample safely, inspect its verified structure, and move from file discovery to reproducible analysis.
Getting started
Five-step quick start
Verified package
File Structure
The current public sample archive contains the following files.
complete_trip_saltlake_2020_1day_Sample_v1.0/ ├── data/ │ └── part-001-of-001.parquet ├── data_dictionary.csv └── Getting_Started.ipynb
network.zip is provided separately and contains auto, transit, and walk/bike node and link tables used to resolve route_taken node IDs.Verified fields
Data Dictionary
The release includes a field-level dictionary. This compact preview uses descriptions verified from that file.
| Field | Type | Description |
|---|---|---|
linked_trip_id | string | Identifier for a reconstructed linked journey containing one or more trip segments. |
trip_id | string | Identifier for a reconstructed single-mode trip segment. |
segment_order | integer | Sequential position of a segment within a linked journey. |
travel_mode | string | Inferred segment-level mode: car, walk/bike, bus, or rail. |
trip_duration | float | Reconstructed segment duration in minutes. |
network_distance | float | Reconstructed network distance in miles. |
trip_weight | float | Population expansion weight for eligible records. |
Hands-on analysis
Example Analysis Notebook
The example notebook demonstrates how to locate and load the public sample, inspect complete trips and travel segments, run quality checks, summarize travel modes, link related records, and use the companion network tables to visualize a selected route without exposing unnecessary identifiers.
network.zip beside the extracted release directory to enable route visualization. Browser execution will use JupyterLite when configured and is intended for a smaller tutorial subset, not large-scale processing.Discover and load release files
from pathlib import Path
import pandas as pd
data_dir = Path("data")
csv_files = list(data_dir.glob("*.csv"))
parquet_files = list(data_dir.glob("*.parquet"))
def load_table(path: Path) -> pd.DataFrame:
if path.suffix.lower() == ".csv":
return pd.read_csv(path)
if path.suffix.lower() == ".parquet":
return pd.read_parquet(path)
raise ValueError(f"Unsupported: {path.suffix}")Inspect safely
files = csv_files or parquet_files
if not files:
raise FileNotFoundError(
"Download and extract the public sample first."
)
table = load_table(files[0])
print(table.shape)
print(table.columns.tolist())
table.head()Review missing values
missing_summary = (
table.isna()
.sum()
.sort_values(ascending=False)
.rename("missing_values")
)
missing_summary.head(20)Discover likely identifiers
candidate_trip_id_columns = [
c for c in table.columns
if "trip" in c.lower() and "id" in c.lower()
]
candidate_coordinate_columns = [
c for c in table.columns
if any(term in c.lower() for term in
["lat", "lon", "lng", "latitude", "longitude"])
]Data processing overview
Six broad stages
Interpretation
Known Limitations
- The public sample covers one undisclosed day and may not represent all seasonal or long-term travel patterns.
- Spatial and temporal information may be aggregated or generalized for privacy.
- Travel modes are inferred and may contain classification uncertainty.
- Device-based mobility observations may not represent the full population.
- Some complete trips may contain multiple travel modes.
- The public sample is intended for evaluation and reproducibility support, not as a replacement for the controlled full dataset.
Common questions
FAQ
Why is the exact sample date not disclosed?
The date and month are withheld and temporal fields are generalized to reduce disclosure risk while preserving research utility.
Is the full dataset publicly downloadable?
No. Full-dataset access is controlled, reviewed, and may require a Data Use Agreement.
Can the public sample be used in a publication?
It is intended to support research evaluation and reproducibility. Check the final release license when configured and cite the dataset and paper.
Why can mode percentages exceed 100% in the explorer?
The explorer reports percentages of trips containing each mode. A complete trip may contain multiple modes, so those percentages do not necessarily sum to 100%.
Does the notebook run directly in the browser?
The notebook can be viewed or downloaded immediately. Browser-based execution will be available through JupyterLite when configured. JupyterLite runs Python in the browser and is intended for the smaller tutorial subset, not large-scale processing.
How should the dataset be cited?
Use the current BibTeX on the Paper & Citation page and check for updates if a journal DOI becomes available.
Who should I contact with access questions?
Contact the research team. The project email will appear here when configured.
