Datasetpublic

dsfox/worlds-largest-inland-freight-hubs

Largest Land Hubs — Inland Freight Terminals The world's largest inland freight hubs — dry ports and rail intermodal terminals — ranked by container throughput (TEU). Part of globalhubs.top, datasets of the world's largest freight transport hubs across maritime, aviation and land. (Passengers are out of scope.) Files File Description hubs.csv One row per hub (UTF-8, comma-separated) hubs.parquet Same data in Apache Parquet CITATION.cff Citation… See the full description on the dataset page: https://huggingface.co/datasets/dsfox/worlds-largest-inland-freight-hubs.

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Largest Land Hubs — Inland Freight Terminals

The world's largest inland freight hubs — dry ports and rail intermodal terminals — ranked by container throughput (TEU). Part of globalhubs.top, datasets of the world's largest freight transport hubs across maritime, aviation and land. (Passengers are out of scope.)

Files

File	Description
`hubs.csv`	One row per hub (UTF-8, comma-separated)
`hubs.parquet`	Same data in Apache Parquet
`CITATION.cff`	Citation metadata (GitHub "Cite this repository")

Schema

Column	Type	Description
`rank`	int	Rank by container throughput
`hub_name`	string	Hub / terminal name
`unlocode`	string	UN/LOCODE (UNECE) — 5-char location code of the hub's city/place, e.g. `DEDUI`; blank if none
`city`	string	City
`country`	string	Country
`country_iso2`	string	ISO 3166-1 alpha-2 country code
`region`	string	Geographic region
`latitude`	float	Latitude (WGS84, city-level)
`longitude`	float	Longitude (WGS84, city-level)
`teu`	int	Container throughput (TEU)
`yoy`	float	Year-over-year change, percent (where available)
`operator`	string	Operating company (where known)
`website`	string	Official website (verified; blank if none)
`address`	string	Postal address of the terminal / its operator (`n/d` if not confidently found)

Coverage & caveats

Top 50 inland freight hubs by container throughput. Unlike seaports and airports, inland terminals report on inconsistent bases (handled TEU, intermodal lifts, or design capacity) and reporting years differ, so ranks are indicative rather than strictly comparable. Pure border crossings are excluded.

Usage

python

import pandas as pd
df = pd.read_csv("hubs.csv")                   # or pd.read_parquet("hubs.parquet")
print(df.sort_values("rank").head(10)[["rank", "hub_name", "country", "teu", "yoy"]])

python

# Read the Parquet straight from the Hugging Face hub (no manual download)
import pandas as pd
df = pd.read_parquet("hf://datasets/dsfox/worlds-largest-inland-freight-hubs/hubs.parquet")

sql

-- DuckDB: query the Hugging Face Parquet in place
INSTALL httpfs; LOAD httpfs;
SELECT rank, hub_name, country, teu
FROM 'hf://datasets/dsfox/worlds-largest-inland-freight-hubs/hubs.parquet'
ORDER BY rank LIMIT 10;

Notebooks (Kaggle): starter · EDA.

Related datasets

Related open CC0 datasets in the globalhubs.top series: container ports · cargo airports · container shipping lines · air-cargo carriers · freight railways · logistics companies. See also humans.top — the world's most influential people.

License

Released under CC0 1.0 — public domain. Free for any use, including AI / LLM training. No attribution required (a link back to globalhubs.top is always welcome).

How to cite

Author: Dmitry Golubnichiy (ORCID 0009-0007-3307-2202). Cite via the DOI 10.5281/zenodo.21058132 (resolves to the latest version), or:

bibtex

@misc{globalhubs_land,
  title  = {Largest Land Hubs — Inland Freight Terminals (open dataset)},
  author = {Golubnichiy, Dmitry},
  year   = {2026},
  doi    = {10.5281/zenodo.21058132},
  url    = {https://globalhubs.top},
  note   = {CC0 1.0 (public domain)}
}

Versioning

v1 — initial release.