license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- fleet-management
- telematics
- trucking
- dispatch
- predictive-maintenance
- synthetic-data
- delivery
- vehicles
- mindweave
- maintenance
- fuel
- test-data
- logistics
- transportation
- fleet
pretty_name: Vehicle Fleet Management Dataset (Free Sample)
size_categories:
- 1K<n<10K
configs:
- config_name: depots
data_files: data/depots.csv
default: true
- config_name: drivers
data_files: data/drivers.csv
- config_name: maintenance
data_files: data/maintenance.csv
- config_name: trips
data_files: data/trips.csv
- config_name: vehicles
data_files: data/vehicles.csv
Vehicle Fleet Management Dataset (Free Sample)
This is a free sample with 2,126 rows. The full dataset has 12,624 rows across 5 tables.
Fleet operations data for a simulated delivery company with 60 vehicles across 3 depots. 15,000 trip records, maintenance logs, fuel purchases, and driver assignments over 18 months.
Features mileage-based maintenance schedules, fuel efficiency tracking by vehicle type, seasonal route patterns, and two anomalies — a fuel price spike and a vehicle recall affecting 8 trucks.
Ideal for: fleet management software, logistics optimization, predictive maintenance ML, fuel cost analysis, and transportation dashboards.
Sample tables
| Table | Sample Rows |
|---|---|
| depots | 3 |
| drivers | 15 |
| maintenance | 50 |
| trips | 2,000 |
| vehicles | 58 |
| Total | 2,126 |
Full dataset
The complete dataset includes all tables with full row counts:
| Table | Full Rows |
|---|---|
| depots | 3 |
| drivers | 15 |
| maintenance | 523 |
| trips | 12,025 |
| vehicles | 58 |
| Total | 12,624 |
Formats included: CSV, Parquet, SQLite
Get the full dataset on Gumroad
About
Generated by Mindweave Technologies -- realistic synthetic datasets for developers, QA teams, and data engineers.
Every dataset features:
- Enforced foreign key relationships across all tables
- Realistic statistical distributions (not uniform random)
- Temporal patterns (seasonal, time-of-day, day-of-week)
- Injected anomalies for ML training and anomaly detection
- Deterministic generation (same seed = same output)
Browse all datasets: https://mindweavetech.gumroad.com