Multi-Modal Shipment Tracking Logs

This dataset offers detailed, event-level tracking logs for shipments across rail, air, and sea, capturing transit milestones, delays, and location data for each shipment movement. It enables supply chain teams and logistics analysts to benchmark reliability, analyze disruptions, and optimize routing strategies for large-scale freight operations. Ideal for AI/ML modeling, resilience studies, and operational analytics.

  • last updated 3 Jan 2026
  • by GoMask
The brief that made it

Supply chain reliability benchmarking and disruption analysis

Sample rows

preview · 8 of 140 rows · all 21 columns
shipment_idstringmode_of_transportstringshipment_weight_kgfloatis_final_eventbooleanevent_typestringtracking_log_idstringcarrier_namestringevent_timestampdatetimelocation_namestringlocation_street_addressstringlocation_citystringlocation_statestringlocation_postal_codestringlocation_countrystringdelay_reasonstringdelay_duration_minutesintegerprevious_event_timestampdatetimenext_expected_event_timestampdatetimeshipment_volume_m3floatshipment_content_descriptionstringcustoms_statusstring
SHIP1001rail7450falsedepartedLOG1001-APacific Rail Cargo2023-07-01T09:05:00ZChicago Rail Terminal233 W Harrison StChicagoIL60606USAblankblankblank2023-07-01T21:00:00Z18.7Automotive componentsnot_applicable
SHIP1001rail7450falsein_transitLOG1001-BPacific Rail Cargo2023-07-01T20:50:00ZCleveland Rail Junction800 Superior AveClevelandOH44114USAblankblank2023-07-01T09:05:00Z2023-07-02T09:15:00Z18.7Automotive componentsnot_applicable
SHIP1001rail7450truecompletedLOG1001-CPacific Rail Cargo2023-07-02T09:10:00ZNewark Rail Depot1200 Raymond BlvdNewarkNJ07102USAblankblank2023-07-01T20:50:00Zblank18.7Automotive componentsnot_applicable
SHIP1002sea29500falsedepartedLOG1002-AAtlantic Sea Logistics2023-06-12T15:25:00ZPort of RotterdamblankRotterdamblank3072APNetherlandsblankblankblank2023-06-18T09:00:00Z85.5Bulk steel coilspending
SHIP1002sea29500falsearrivedLOG1002-BAtlantic Sea Logistics2023-06-18T08:40:00ZPort of New YorkblankNew YorkNY10004USAblankblank2023-06-12T15:25:00Z2023-06-18T13:00:00Z85.5Bulk steel coilspending
SHIP1002sea29500falsecustomsLOG1002-CAtlantic Sea Logistics2023-06-18T13:10:00ZPort of New York CustomsblankNew YorkNY10004USAblankblank2023-06-18T08:40:00Z2023-06-18T17:30:00Z85.5Bulk steel coilscleared
SHIP1002sea29500truecompletedLOG1002-DAtlantic Sea Logistics2023-06-18T17:15:00ZPort of New YorkblankNew YorkNY10004USAblankblank2023-06-18T13:10:00Zblank85.5Bulk steel coilscleared
SHIP1003air6.2falsedepartedLOG1003-ASky Freight Express2023-04-03T22:10:00ZLondon Heathrow AirportblankLondonblankTW6UKblankblankblank2023-04-04T04:00:00Z0.17Legal documentsnot_applicable

What the 140 rows show

from the 140-row sample

Sea (mode of transport) stands out: mean shipment_weight_kg is 25,263, against 3,302 for the rest.

  • 25%is_final_event = true
  • 6,800median shipment_weight_kg
  • 4customs statuses
  • 9shipment content descriptions
  • 29location countries
  • 22.7median shipment_volume_m3
Mean shipment_weight_kg by mode_of_transport138 rows
020k40k6,741rail42 rows20.0air44 rows25,263sea52 rows
shipment_weight_kg138 rows, in bands of 5,000
03060542847201510020k35kshipment_weight_kg →

Median 6,800, from 0.30 to 32,000.

event_type138 rows with a value · 2 left blank
  1. departed54
  2. completed34
  3. arrived26
  4. customs11
  5. in_transit8
  6. delayed5
21 columns by typefrom the column list below
  • string 14
  • integer 1
  • float 2
  • datetime 3
  • boolean 1

Columns

21 columns in four groups
blueprint · 21 columns
columntypedescriptionexample
Text 14 columns
shipment_idstringUnique identifier for the shipmentuniqueSHIP1001
tracking_log_idstringUnique identifier for each tracking event loguniqueLOG1001-A
carrier_namestringName of the carrier responsible for the shipment at this eventPacific Rail Cargo
mode_of_transportstringMode of transport for this shipment segment (rail, air, sea)rail · air · searail
event_typestringType of tracking event (departed, arrived, delayed, customs, transferred, etc.)7 valuesdeparted
location_namestringName of the location where the event occurred (port, station, airport, etc.)Chicago Rail Terminal
location_street_addressstringStreet address of the event locationoptional233 W Harrison St
location_citystringCity of the event locationChicago
location_statestringState or province of the event locationoptionalIL
location_postal_codestringPostal code of the event locationoptional60606
location_countrystringCountry of the event locationUSA
delay_reasonstringReason for delay if the event_type is 'delayed'3 reasons · optionalWeather conditions
shipment_content_descriptionstringDescription of the shipment contents9 descriptions · optionalAutomotive components
customs_statusstringCustoms clearance status at the event location, if applicablepending · cleared · hold · not_applicable · optionalnot_applicable
Numbers 3 columns
delay_duration_minutesintegerDuration of delay in minutes, if applicable0 or more · optional180
shipment_weight_kgfloatTotal weight of the shipment in kilograms0 or more · optional7450
shipment_volume_m3floatTotal volume of the shipment in cubic meters0 or more · optional18.7
Dates and times 3 columns
event_timestampdatetimeDate and time when the tracking event occurred2023-07-01T09:05:00Z
previous_event_timestampdatetimeTimestamp of the previous event for this shipment (for calculating transit times)optional2023-07-01T09:05:00Z
next_expected_event_timestampdatetimeScheduled or estimated timestamp for the next event in the shipment's journeyoptional2023-07-01T21:00:00Z
True or false 1 column
is_final_eventbooleanIndicates if this event is the final tracking event for the shipmentfalse

Use it for

  • is final event25%34 of 138 rowsmean shipment weight …6.7krail20.0air25.3ksea

    A logistics dashboard

    The is_final_event rate, shipment_weight_kg by mode_of_transport and a breakdown of event_type. Excel, Power BI or Tableau.

  • Why do the 52 sea rows have a mean shipment_weight_kg of 25,263?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable shipments with tracking_log_id, carrier_name and mode_of_transport to fill a screen in front of a buyer.

Not quite right?

Make it yours.

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This dataset140 rows21 columns
Yours1,000 rows21 columnslocation_name: UK only

blueprint · multi-modal-shipment-tracking-logs

Behind this dataset

Same schema. As many rows as you need.

These 140 rows came out of a blueprint — 21 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 shipment entry must include mode (rail, air, sea) and unique shipment ID.
  • Transit statuses (departed, in-transit, delayed, arrived) are timestamped and sequenced per leg.
  • Delay reasons are provided and standardized (e.g., weather, customs, equipment failure).
  • Origin and destination ports/terminals referenced from a validated lookup list.
  • Cargo type must be specified for each shipment.
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
multi-modal-shipment-tracking-logs

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