Vehicle Load Capacity Utilization
This dataset provides granular records of vehicle load capacity utilization, capturing details on transported loads, vehicle specifications, trip routes, and efficiency metrics. It enables logistics and transportation teams to analyze fleet performance, optimize load distribution, and reduce operational costs through data-driven insights.
Sample rows
preview · 8 of 70 rows · all 20 columns| record_idstring | vehicle_typestring | vehicle_capacity_kgfloat | origin_citystring | vehicle_idstring | license_platestring | driver_idstring | trip_idstring | departure_datetimedatetime | arrival_datetimedatetime | origin_statestring | origin_countrystring | destination_citystring | destination_statestring | destination_countrystring | actual_load_kgfloat | utilization_percentfloat | distance_kmfloat | fuel_consumed_litersfloat | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| VLCU-0001 | truck | 18000 | Dallas | VH-1023 | AB-1234-TY | DRV-201 | TRP-4601 | 2023-03-11T07:15:00 | 2023-03-11T15:25:00 | TX | USA | Houston | TX | USA | 16100 | 89.4 | 385 | 102.5 | Smooth delivery, no delays. |
| VLCU-0002 | lorry | 22000 | Chicago | VH-1055 | KL-8876-OP | DRV-214 | TRP-4612 | 2023-03-18T09:05:00 | 2023-03-18T17:35:00 | IL | USA | Indianapolis | IN | USA | 22000 | 100 | 298 | 88.3 | Full load; required extra care. |
| VLCU-0003 | van | 3500 | Buffalo | VH-1077 | NY-4561-CA | DRV-232 | TRP-4655 | 2023-03-19T06:55:00 | 2023-03-19T10:30:00 | NY | USA | Rochester | NY | USA | 3290 | 94 | 108 | 16.2 | blank |
| VLCU-0004 | truck | 14500 | Orlando | VH-1092 | FL-9921-JK | DRV-241 | TRP-4698 | 2023-02-27T11:40:00 | 2023-02-27T17:05:00 | FL | USA | Tampa | FL | USA | 11000 | 75.9 | 135 | 36.4 | Early arrival. |
| VLCU-0005 | van | 4500 | San Jose | VH-1203 | CA-9007-QW | DRV-203 | TRP-4702 | 2023-03-02T13:20:00 | 2023-03-02T18:10:00 | CA | USA | Sacramento | CA | USA | 4010 | 89.1 | 188 | 24.5 | blank |
| VLCU-0006 | truck | 18000 | Dallas | VH-1023 | AB-1234-TY | DRV-211 | TRP-4707 | 2023-03-13T08:00:00 | 2023-03-13T15:17:00 | TX | USA | Austin | TX | USA | 16300 | 90.6 | 316 | 85.9 | Traffic on I-35. |
| VLCU-0007 | lorry | 20500 | Detroit | VH-1110 | MI-6692-ZX | DRV-255 | TRP-4733 | 2023-03-05T05:30:00 | 2023-03-05T13:12:00 | MI | USA | Cleveland | OH | USA | 19470 | 95 | 280 | 73.9 | blank |
| VLCU-0008 | truck | 17200 | Atlanta | VH-1165 | GA-8712-RT | DRV-247 | TRP-4740 | 2023-03-15T10:00:00 | 2023-03-15T20:25:00 | GA | USA | Savannah | GA | USA | 15900 | 92.4 | 399 | 108.2 | Weather delays. |
| VLCU-0009 | van | 3500 | Buffalo | VH-1077 | NY-4561-CA | DRV-232 | TRP-4752 | 2023-03-21T08:10:00 | 2023-03-21T12:35:00 | NY | USA | Syracuse | NY | USA | 3200 | 91.4 | 238 | 19.7 | blank |
| VLCU-0010 | truck | 16800 | Seattle | VH-1088 | WA-5531-LM | DRV-212 | TRP-4761 | 2023-03-07T06:50:00 | 2023-03-07T16:15:00 | WA | USA | Portland | OR | USA | 14900 | 88.7 | 282 | 74.3 | Stopped for inspection. |
| VLCU-0011 | truck | 17200 | Atlanta | VH-1165 | GA-8712-RT | DRV-221 | TRP-4773 | 2023-03-16T07:30:00 | 2023-03-16T15:30:00 | GA | USA | Augusta | GA | USA | 12000 | 69.8 | 233 | 47.2 | blank |
| VLCU-0012 | van | 4500 | San Jose | VH-1203 | CA-9007-QW | blank | TRP-4781 | 2023-03-10T09:10:00 | 2023-03-10T13:30:00 | CA | USA | Oakland | CA | USA | 2780 | 61.8 | 67 | 10.1 | Partial load. |
| VLCU-0013 | lorry | 22000 | Chicago | VH-1055 | KL-8876-OP | DRV-214 | TRP-4787 | 2023-03-23T08:40:00 | 2023-03-23T17:00:00 | IL | USA | Milwaukee | WI | USA | 21260 | 96.7 | 150 | 48 | blank |
| VLCU-0014 | lorry | 20500 | Detroit | VH-1110 | MI-6692-ZX | DRV-255 | TRP-4793 | 2023-03-09T06:45:00 | 2023-03-09T14:10:00 | MI | USA | Toledo | OH | USA | 18700 | 91.2 | 92 | 29.7 | blank |
| VLCU-0015 | truck | 14500 | Orlando | VH-1092 | FL-9921-JK | DRV-241 | TRP-4802 | 2023-02-28T10:20:00 | 2023-02-28T17:35:00 | FL | USA | Jacksonville | FL | USA | 9500 | 65.5 | 226 | 52.2 | Route detour. |
| VLCU-0016 | truck | 16800 | Seattle | VH-1088 | WA-5531-LM | DRV-229 | TRP-4813 | 2023-03-12T09:00:00 | 2023-03-12T18:45:00 | WA | USA | Spokane | WA | USA | 15890 | 94.6 | 450 | 120.7 | blank |
| VLCU-0017 | van | 3500 | Buffalo | VH-1077 | NY-4561-CA | blank | TRP-4824 | 2023-03-22T07:10:00 | 2023-03-22T10:50:00 | NY | USA | Albany | NY | USA | 2550 | 72.9 | 384 | 27.1 | Weather was clear. |
| VLCU-0018 | truck | 17200 | Atlanta | VH-1165 | GA-8712-RT | DRV-247 | TRP-4831 | 2023-03-17T12:40:00 | 2023-03-17T18:20:00 | GA | USA | Columbus | GA | USA | 12500 | 72.7 | 170 | 37.2 | blank |
| VLCU-0019 | lorry | 20500 | Detroit | VH-1110 | MI-6692-ZX | DRV-269 | TRP-4839 | 2023-03-11T04:30:00 | 2023-03-11T09:20:00 | MI | USA | Grand Rapids | MI | USA | 11700 | 57.1 | 222 | 53.6 | Half load, quick trip. |
| VLCU-0020 | van | 4500 | San Jose | VH-1203 | CA-9007-QW | DRV-203 | TRP-4842 | 2023-03-05T12:30:00 | 2023-03-05T15:20:00 | CA | USA | Fresno | CA | USA | 4400 | 97.8 | 242 | 23.5 | blank |
What the 70 rows show
from the 70-row sampleTruck (vehicle type) stands out: mean vehicle_
- 6,400median vehicle_
capacity_ kg - 15origin countries
- 15destination countries
- 5,348median actual_
load_ kg - 84.6median utilization_
percent - 215.0median distance_
km
Median 6,400, from 1,200 to 22,000.
- string 13
- float 5
- datetime 2
Columns
20 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 13 columns | |||
record_id | string | Unique identifier for each vehicle load capacity utilization recordunique | VLCU-0001 |
vehicle_id | string | Unique identifier for the vehicle | VH-1023 |
vehicle_type | string | Type or classification of the vehicle (e.g., truck, van, lorry)6 types | truck |
license_plate | string | License plate number of the vehicle | AB-1234-TY |
driver_id | string | Unique identifier for the driver assigned to the vehicle for this tripoptional | DRV-201 |
trip_id | string | Unique identifier for the trip or delivery instance | TRP-4601 |
origin_city | string | City where the trip originated | Dallas |
origin_state | string | State or region where the trip originated | TX |
origin_country | string | Country where the trip originated | USA |
destination_city | string | City where the trip ended | Houston |
destination_state | string | State or region where the trip ended | TX |
destination_country | string | Country where the trip ended | USA |
notes | string | Additional notes or comments about the trip or loadoptional | Early arrival. |
| Numbers 5 columns | |||
vehicle_capacity_kg | float | Maximum load capacity of the vehicle in kilograms0 or more | 18000 |
actual_load_kg | float | Actual weight of the load transported in kilograms0 or more | 16100 |
utilization_percent | float | Percentage of the vehicle's load capacity utilized for this trip0 to 100 | 89.4 |
distance_km | float | Distance traveled during the trip in kilometers0 or more · optional | 385 |
fuel_consumed_liters | float | Amount of fuel consumed during the trip in liters0 or more · optional | 102.5 |
| Dates and times 2 columns | |||
departure_datetime | datetime | Date and time when the vehicle departed for the trip | 2023-03-11T07:15:00 |
arrival_datetime | datetime | Date and time when the vehicle arrived at the destinationoptional | 2023-03-11T15:25:00 |
Use it for
A logistics dashboard
Vehicle_
capacity_ kg by vehicle_ type and a breakdown of origin_ city. Excel, Power BI or Tableau. Why do the 19 truck rows have a mean vehicle_
capacity_ kg of 15,753? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Records70VLCU-000118000truckVLCU-000222000lorryVLCU-00033500van
A software demo
Believable records with vehicle_
id, vehicle_ type and license_ plate to fill a screen in front of a buyer.
blueprint · vehicle-load-capacity-utilization
Behind this dataset
Same schema. As many rows as you need.
These 70 rows came out of a blueprint — 20 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.
- Record actual vs maximum load per trip
- Log trip distance and duration
- Flag loads below 70% capacity
- Sum daily vehicle utilization rates
- Identify vehicles with repeated underutilization
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
- vehicle-load-capacity-utilization