GCRI · Critical Infrastructure
Transport and logistics nodes
Ports, airports, rail freight yards, pipelines and distribution centres are the nodes where flows of goods concentrate. A closure at one can stall supply chains far away, because alternative routes and capacity are limited and slow to bring into use. This page sets out how logistics nodes are disrupted, how their criticality is measured, and what evidence serves the operators and authorities who manage them.
Transport and logistics nodes in seven parts.
- 01The questionTransport and logistics nodes: how the risk develops
- 02Across the domainsPorts, corridors and hubs move what every domain needs
- 03IndicatorsWhat is measured, and how well
- 04SourcesWhere the evidence comes from
- 05DecisionsWho decides, and what the evidence serves
- 06Core questionsWhat experts ask
- 07MechanismsDrivers and failure pathways
Transport and logistics nodes: how the risk develops.
Ports, roads, rail and supply routes. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Flows concentrate at few nodes
Economies of scale concentrate container traffic, air freight and fuel imports in a small number of ports, hubs and terminals. Each becomes a point where disruption affects many supply chains.
Hazards close nodes
Storms, floods, heat and sea-level rise close ports and airports, damage equipment and cut access roads and rail. Coastal ports are exposed to surge and wind.
Labour and systems are dependencies
Strikes, staff shortages, customs system outages and cyber incidents can stop a node as surely as physical damage.
Backlogs persist after reopening
Ships, containers and trucks pile up during a closure. Clearing the backlog takes longer than the closure itself, and the effects travel along shipping and supply networks.
Activity and hazards in Western Europe.
Night lights, population density, marine heat and true-colour imagery over Western Europe, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 4
Activity and hazards, for this page
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Activity and hazards, framed on Western Europe.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA HLS S30Sources and licences
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- GHRSST Level 4 MUR analyses, NASA Jet Propulsion Laboratory; distributed by the NASA Physical Oceanography DAAC (PO.DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Observed (EO): NASA HLS S30 (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (7 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Sea surface temperature anomaly: GHRSST MUR25, NASA JPL PO.DAAC · 12 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
Ports, corridors and hubs move what every domain needs.
Logistics studies are designed for the Strategic Systems series. UNCTAD's Review of Maritime Transport 2024 documents recent disruptions; port security follows the ISPS Code.
- Port disruption records.
- Corridor capacity data.
Asset criticality register.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Asset criticality register
Transport and logistics nodes · Lifeline system A · Register v4
Critical assets
| SS-12 | Electricity | Tier 1 | Flood 1-in-100 | 18 |
| WTW-2 | Water | Tier 1 | Flood 1-in-100 | 26 |
| DX-1 | Telecom | Tier 2 | Heat | 5 |
| H-Campus | Health | Tier 1 | Earthquake | 73 |
| PN-3 | Transport | Tier 2 | Storm surge | n/a |
SS-12 dependencies
- Upstream
- Transmission L4, L7
- Downstream
- WTW-2, H-Campus, DX-1
- Restoration target
- 14 h
- Owner
- Asset owner
Dependency graph
red: SS-12 and its dependants
Restoration time by sector
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Port throughput | twenty-foot equivalent units | monthly | Port authority statistics | Published statistics |
| Vessel waiting time | hours at anchor before berth | weekly | Automatic identification system data | OBSERVED |
| Closure days by cause | days | annual | Operator and authority records | REPORTED |
| Share of national trade through one node | percent of trade value | annual | Customs and trade statistics | Published statistics |
| Alternative capacity available | percent of node throughput other nodes could absorb | per assessment | Capacity studies | ESTIMATED |
| Time to clear backlog | days | per event | Operator records | OBSERVED |
A shock through an input-output economy.
A loss of output in one sector propagates through the technical coefficients to its suppliers and customers, and recovers at the rate you set; the coefficients are teaching values, not national accounts. Teaching model with stated illustrative values, not a prediction. The map on this page shows Western Europe; the model itself is not tied to a place.
Controls
- Sector hit first (position in the list)
- 0
- Size of the shock
- 30% of its output
- Duration of the shock
- 30 days
- Recovery rate
- 0.08 per day
- Days simulated
- 180 days
Readouts
- Output lost over the run
- 0.70% of a year's output
- Total over direct (multiplier)
- 1.70 times
- Most affected other sector
- Trade
- Its peak inoperability
- 3.2%
Numbers, methods and sources
| Sector | Peak inoperability (%) | Output weight (%) |
|---|---|---|
| Agriculture | 30.50 | 15.8 |
| Energy | 2.69 | 19.1 |
| Manufacturing | 1.61 | 10.7 |
| Transport and ports | 1.58 | 22.1 |
| Trade | 3.22 | 21.7 |
| Services | 0.26 | 10.6 |
Methods
- Input-output analysis Leontief (1936); Miller and Blair (2009), Input-Output Analysis, Cambridge University Press
- Inoperability input-output model Haimes and Jiang (2001), Journal of Infrastructure Systems 7
- Dynamic inoperability and recovery Lian and Haimes (2006), Systems Engineering 9
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Seaports Natural Earth
- Maritime chokepoints Nexus (named straits and canals at their published coordinates)
- Railways Natural Earth
- Major roads Natural Earth
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Critical Infrastructure in Foundry
- Tested at Critical Infrastructure in Universe
- Codified in Energy and Critical Infrastructure in Standards
- Published in Critical Infrastructure in Reports
- Taught in Faculty of Infrastructure, Energy and Industry in Academy
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| UNCTADstat maritime transport statistics | Port calls, fleet and maritime trade data | Public, UNCTAD terms (to check) |
| IMF PortWatch | Port activity and trade disruption estimates from satellite vessel tracking | Public, IMF terms (to check) |
| National customs and trade statistics | Trade values and volumes by port of entry | Public records, licence per government |
| OpenStreetMap | Locations of ports, terminals, rail and road access | Open, ODbL |
| ICAO and national civil aviation statistics | Airport traffic and capacity | Terms per publisher |
Model transport and logistics nodes as a system. Run the cascade.
Assembled from the Nexus Standards area profile Critical infrastructure and critical entities: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Dam failure
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
Systems and couplings
- Critical infrastructure and critical entities
- Energy risk standards Dependency
- Space weather, spectrum and timing risk standards Common-mode dependency
- Compound and cascading hazard standards Cascade
- Cyber resilience and digital operational resilience Common driver
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-H94Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)NRS-H107Hazard family: Digital infrastructure concentration and monoculture (NHS-1 tech.digital-concentration)NRS-K31Domain pack profile: Cyber national resilience (DOMAIN-031)NRS-K36Domain pack profile: Critical infrastructure (DOMAIN-036)NRS-K48Domain pack profile: Financial system resilience (DOMAIN-048)
Observation
- OpenStreetMap planet and replication (listed for reference)
- Global Power Plant Database (listed for reference)
- NASA Black Marble night-time lights (VNP46) (listed for reference)
- Global Human Settlement Layer (listed for reference)
- Damage to critical infrastructure attributed to disasters
- Number of disruptions to basic services attributed to disasters
- Proportion of the rural population who live within 2 km of an all-season road
Example geography
- Europe: 113 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
Who decides, and what the evidence serves.
Critical Infrastructure prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Port and airport authorities | Which resilience investments protect throughput best | Hazard exposure; closure history |
| Ministries of transport and trade | How dependent national trade is on single nodes, and where to build alternatives | Criticality analyses; diversion capacity |
| Logistics operators | How to plan routes and stocks around node disruptions | Recovery profiles; waiting time data |
Outputs this topic produces
- Node criticality analyses by share of trade and available alternatives
- Hazard exposure reviews of ports and airports
- Backlog and recovery profiles from past closures
- Diversion capacity studies for national logistics
How transport and logistics nodes is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in transport and logistics nodes: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
What experts ask.
- Which nodes have no practical alternative, and what flows depend on them?
- How do closures of ports, bridges or rail lines propagate through supply chains?
- How do fuel, food and medical supplies depend on specific logistics routes?
- How do just-in-time inventories change the speed at which shortages appear?
- How quickly can damaged nodes be repaired or flows rerouted?
Drivers and failure pathways.
Transport networks are shaped by geography and economies of scale, so flows concentrate on a limited number of nodes. Large ports, mountain passes, river crossings and maritime straits carry volumes that alternatives cannot absorb quickly. Logistics practice has favoured lean inventories, which reduce costs but also reduce the buffer between a closure and a shortage.
Other lifelines depend on these nodes. Power stations need fuel deliveries, water treatment needs chemicals, hospitals need medicines and repair crews need roads to reach damaged assets. Transport nodes depend in turn on electricity, communications and digital systems for signalling, cargo handling and customs. Earthquakes, floods, storms, heat and industrial accidents can close nodes physically, while labour disputes, cyber incidents and congestion can close them operationally.
Mobility networks for people are covered in transport and mobility, and supply chain concentration is studied with Trade and Strategic Resources.
How it is studied.
- 01
Network criticality analysis
Methods from network science and contagion and urban and transport engineering rank nodes by the flows that depend on them.
- 02
Supply chain mapping
Commodity flow data traces which goods pass through which nodes, and where alternatives exist.
- 03
Disruption simulation
Optimisation and simulation from optimisation and operations research test rerouting capacity and recovery times.
- 04
Limits
Freight data is fragmented and commercially sensitive. Models capture physical capacity better than contracts and behaviour, and rerouting often depends on decisions by many private actors.
How it is studied, and what the methods cannot say.
- 01Urban and transport engineeringNetwork criticality · Climate-resilient design · Disruption operationsApplied here
- 02Optimisation and operations researchResilience investment portfolios · Restoration scheduling · Network design under uncertaintyApplied here
- 03Network science and contagionCritical node and link analysis · Interdependent network models · Redundancy assessmentApplied here
- 04Reliability and resilience engineeringResilience metrics for lifelines · Reliability analysis of networks · Restoration planningApplied here
- 05Sensors, IoT and observation networksStructural health monitoring · Leak and pressure monitoring · Grid sensingApplied here
Where this page's references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
What makes a port critical?
Why do disruptions last longer than closures?
How is port activity tracked?
Why do shortages appear so quickly after a port closes?
Does the platform rank specific ports or bridges?
How is this topic different from transport and mobility?
Who reviews coverage of this topic?
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