Container Port Throughput Metrics
This dataset provides detailed container port throughput metrics, including container volumes, congestion events, and operational KPIs such as berth occupancy and yard utilization rates. It enables comprehensive analysis of port efficiency, congestion patterns, and logistics performance, supporting data-driven optimization and benchmarking across global ports.
Sample rows
preview · 8 of 200 rows · all 15 columns| record_idstring | regionstring | congestion_duration_hoursfloat | congestion_eventboolean | port_idstring | port_namestring | countrystring | reporting_datedate | container_throughput_teuinteger | vessel_callsinteger | average_dwell_time_hoursfloat | berth_occupancy_ratefloat | yard_utilization_ratefloat | average_truck_turnaround_time_minutesfloat | operational_kpi_notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| REC-0001 | Asia-Pacific | 19.5 | true | SGSIN | Singapore | Singapore | 2023-01-15 | 3600000 | 495 | 34.2 | 89.7 | 84.5 | 62.4 | Monsoon rains caused brief delays |
| REC-0002 | Asia-Pacific | 25 | true | CNSHA | Shanghai | China | 2023-02-10 | 4210000 | 512 | 38.7 | 91.1 | 88.3 | 77.2 | Lunar New Year surge |
| REC-0003 | Europe | 0 | false | NLRTM | Rotterdam | Netherlands | 2023-03-08 | 2800000 | 364 | 31.8 | 81.6 | 77.4 | 54.1 | blank |
| REC-0004 | Asia-Pacific | 0 | false | JPTYO | Tokyo | Japan | 2023-01-27 | 1920000 | 288 | 28.4 | 73.3 | 68.9 | 47.8 | Routine operations |
| REC-0005 | North America | 0 | false | USLAX | Los Angeles | United States | 2023-02-14 | 1160000 | 125 | 22.5 | 49.2 | 44.7 | 37.6 | blank |
| REC-0006 | South America | 37.6 | true | BRSSZ | Santos | Brazil | 2023-03-29 | 165000 | 21 | 67.2 | 99.1 | 97.4 | 205.3 | Severe congestion due to labor shortage |
| REC-0007 | Asia-Pacific | 0 | false | AULSY | Sydney | Australia | 2023-03-12 | 1126000 | 132 | 19.8 | 60.8 | 57.9 | 33.2 | Routine operations |
| REC-0008 | Africa | 0 | false | ZAECZ | East London | South Africa | 2023-02-22 | 46000 | 7 | 10.5 | 9.1 | 7.2 | 17.8 | Minimal activity |
| REC-0009 | Europe | 0 | false | DEHAM | Hamburg | Germany | 2023-03-04 | 950000 | 97 | 25.7 | 56.4 | 48.9 | 38.5 | blank |
| REC-0010 | Asia-Pacific | 13.7 | true | HKHKG | Hong Kong | Hong Kong | 2023-01-19 | 2090000 | 319 | 33.4 | 81.2 | 78.1 | 61.5 | Equipment upgrade ongoing |
| REC-0011 | Africa | 84.7 | true | EGALY | Alexandria | Egypt | 2023-03-13 | 390000 | 41 | 65.1 | 100 | 95.8 | 221.6 | Unexpected surge in exports |
| REC-0012 | Europe | 0 | false | ITGOA | Genoa | Italy | 2023-02-23 | 640000 | 54 | 22.7 | 61.3 | 58.4 | 45.6 | Routine operations |
| REC-0013 | Asia-Pacific | 28.6 | true | INNSA | Nhava Sheva | India | 2023-01-31 | 1460000 | 153 | 41.6 | 94.9 | 89.7 | 79.1 | Truckers' strike |
| REC-0014 | Europe | 0 | false | GBSOU | Southampton | United Kingdom | 2023-03-26 | 478000 | 36 | 20.9 | 54.7 | 52.3 | 40.5 | blank |
| REC-0015 | North America | 0 | false | USNYC | New York | United States | 2023-01-30 | 1050000 | 107 | 18.4 | 52.4 | 47.9 | 36.1 | Quick clearance |
| REC-0016 | Asia-Pacific | 21.4 | true | KRPUS | Busan | South Korea | 2023-03-22 | 3100000 | 406 | 40.7 | 84.3 | 80.2 | 71.9 | Inbound festival demand |
| REC-0017 | Europe | 0 | false | FRLEH | Le Havre | France | 2023-01-20 | 890000 | 82 | 17.7 | 47.8 | 46.5 | 32.7 | Routine operations |
| REC-0018 | Africa | 111.3 | true | NGLOS | Lagos | Nigeria | 2023-02-19 | 277000 | 26 | 71.6 | 98.6 | 97.9 | 240.5 | Labor strike caused severe congestion |
| REC-0019 | Asia-Pacific | 0 | false | NZAKL | Auckland | New Zealand | 2023-03-11 | 288000 | 29 | 19.9 | 58.6 | 55.2 | 31.4 | blank |
| REC-0020 | Africa | 0 | false | SASEZ | Saldanha Bay | South Africa | 2023-01-09 | 0 | 0 | 0 | 0 | 0 | 0 | Port closed for maintenance |
What the 200 rows show
from the 200-row sampleAfrica region stands out: mean congestion_
- 28%congestion_
event = true - 0.0median congestion_
duration_ hours - 52countries
- 428,000median container_
throughput_ teu - 48median vessel_
calls - 29.3median average_
dwell_ time_ hours
Median 0.0, from 0.0 to 129.7.
- string 6
- integer 2
- float 5
- date 1
- boolean 1
Columns
15 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 6 columns | |||
record_id | string | Unique identifier for each throughput recordunique | REC-0001 |
port_id | string | Unique identifier for the port | SGSIN |
port_name | string | Official name of the port | Singapore |
country | string | Country where the port is located | Singapore |
region | string | Geographical region of the port (e.g., Asia-Pacific, Europe)6 regions · optional | Asia-Pacific |
operational_kpi_notes | string | Additional notes or remarks on operational KPIs or exceptional eventsoptional | Lunar New Year surge |
| Numbers 7 columns | |||
container_throughput_teu | integer | Total number of containers handled (in TEUs) during the reporting period0 or more | 3600000 |
vessel_calls | integer | Number of vessel calls at the port during the reporting period0 or more | 495 |
average_dwell_time_hours | float | Average time (in hours) containers spend at the port0 or more | 34.2 |
berth_occupancy_rate | float | Percentage of time berths are occupied (0-100)0 to 100 | 89.7 |
yard_utilization_rate | float | Percentage of yard space utilized (0-100)0 to 100 | 84.5 |
congestion_duration_hours | float | Total duration (in hours) of congestion events during the reporting period; 0 if no congestion event0 or more | 19.5 |
average_truck_turnaround_time_minutes | float | Average time (in minutes) for trucks to complete a round trip at the port0 or more · optional | 62.4 |
| Dates and times 1 column | |||
reporting_date | date | Date for which throughput and KPIs are reported | 2023-01-15 |
| True or false 1 column | |||
congestion_event | boolean | Indicates if a congestion event occurred during the reporting period | true |
Use it for
A logistics dashboard
The congestion_
event rate and congestion_ duration_ hours by region. Excel, Power BI or Tableau. Why do the 30 Africa rows have a mean congestion_
duration_ hours of 74.2? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Records200REC-000119.5Asia-Pac…REC-000225Asia-Pac…REC-00030Europe
A software demo
Believable records with port_
id, port_ name and country to fill a screen in front of a buyer.
This dataset200 rows15 columns
Yours1,000 rows15 columnscountry: UK only
blueprint · container-port-throughput-metrics
Behind this dataset
Same schema. As many rows as you need.
These 200 rows came out of a blueprint — 15 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.
Rules it was built with
- Each record: vessel arrival/departure with TEU count
- Includes wait time, crane usage, weather impact
- Congestion flagged when dwell exceeds threshold
- Cargo type and destination region specified
- KPIs: turnaround time, berth occupancy
Open the blueprint in Data Factory
1 credit per row. Preview 20 rows free. Buy rows for the full run.
- Exports
- CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
- Licence
- yours to use, including commercially
- API slug
- container-port-throughput-metrics