Arteries

Calculate lowest-cost distances between the world's cities (population > 100,000) over a real infrastructure network, with land kilometers, sea kilometers, and port calls priced to your desired specifications.

Modeling distance is important. Economists tend to rely on great-circle (as-the-crow-flies) distances because of their numerical simplicity. The caveat is that a majority of goods do not travel in clean arcs to their destinations. They move by road to a port, by sea along shipping lanes and through chokepoints, and by road again. Arteries computes that route for every pair of cities with at least 100,000 people.

You choose what a road kilometer, a sea kilometer, and a port call cost. The package efficiently returns the cheapest route's generalized cost, together with the land and sea kilometers it uses and its number of sea legs.

Results are exact: they equal Dijkstra's algorithm on the full global multimodal graph for any choice of costs. They are also very fast. Routing all 2,075 × 2,075 city pairs takes about 20 seconds on a laptop, a major improvement over previous options.

import arteries

net = arteries.load()                       # downloads the network data once (~21 MB)

res = net.route_costs(
    land_cost=5.0,                          # cost per road km (relative to sea)
    sea_cost=1.0,                           # cost per sea km
    port_cost=1000.0,                       # cost per port call (loading or unloading)
    closed_regions=["red_sea"],             # e.g. the 2023-24 Red Sea disruption
)
df = res.to_frame()      # origin, destination, cost, land_km, sea_km, total_km, sea_legs

• Exact. The answer equals Dijkstra on the full multimodal graph for any weights. Proof and tests: docs/METHODS.md.
• Fast. Routing all 2,075 × 2,075 city pairs takes about 20 seconds, because the expensive land shortest paths are precomputed once and every new weighting only reweights a small terminal graph.
• Decomposed. Every result reports land km, sea km, and the number of sea legs of the cheapest route.
• Scenarios. You can close whole sea regions (closed_regions=["red_sea"], "persian_gulf", "black_sea", "baltic_sea", or any lat/lon box), turn river and lake ports into land-only terminals (max_port_lane_km), close chokepoints (suez, panama, babalmandab, malacca, ormuz, gibraltar, bosporus, dardanelles, sunda, bering, chili, south_africa, northwest), or rescale individual sea lanes with sea_edge_multiplier.

Documentation: https://blaizegiangiulio.com/arteries/

Install

pip install arteries            # numpy, scipy, pandas
pip install "arteries[fast]"    # + numba (strongly recommended)

Data is cached in ~/.cache/arteries; set the ARTERIES_DATA environment variable to use another folder.

What is in the network

Rebuilding the data

The build pipeline (python -m arteries.build ...) and the Slurm scripts (slurm/) reproduce the release from the raw extracts. See docs/METHODS.md for every step, the exactness arguments, validation results and resource use.

Licenses and attribution

• Code: MIT.
• Road data: © OpenStreetMap contributors, ODbL 1.0. The distributed distance data is a derivative database and is shared under ODbL.
• Maritime network: searoute-py (Apache-2.0), from Eurostat SeaRoute / ORNL shipping lanes.
• Ports: IMF PortWatch.
• Cities: UN World Urbanization Prospects and citypopulation.de (Brinkhoff).

Citation

Giangiulio, B. (2026). Arteries: exact multimodal transport distances between world cities. Python package version 0.1.0.