Last-Mile Delivery Route Metrics
This dataset provides granular metrics for completed last-mile delivery routes, including cost breakdowns, timing, delivery outcomes, and operational context. It enables detailed analysis for cost optimization, delay prediction, and service performance improvement in logistics operations. The data is ideal for benchmarking, predictive modeling, and operational reporting.
Sample rows
preview · 8 of 200 rows · all 20 columns| route_idstring | weather_conditionsstring | on_time_percentagefloat | route_regionstring | delivery_datedate | driver_idstring | vehicle_idstring | route_start_timedatetime | route_end_timedatetime | planned_distance_kmfloat | actual_distance_kmfloat | planned_duration_minutesinteger | actual_duration_minutesinteger | total_deliveriesinteger | completed_deliveriesinteger | failed_deliveriesinteger | total_cost_usdfloat | fuel_cost_usdfloat | labor_cost_usdfloat | delay_minutesinteger |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| RT_A12BCD12 | clear | 95 | Downtown | 2024-05-02 | DRV_1001 | VEH_X9A123 | 2024-05-02T08:10:00 | 2024-05-02T08:45:00 | 5.6 | 6 | 30 | 35 | 3 | 3 | 0 | 22.5 | 2.2 | 20 | 5 |
| RT_B21DFE23 | cloudy | 90.9 | West-Suburb | 2024-05-03 | DRV_1002 | VEH_X5B111 | 2024-05-03T13:25:00 | 2024-05-03T15:00:00 | 18.5 | 19.2 | 90 | 95 | 11 | 10 | 1 | 65.7 | 16.4 | 49 | 5 |
| RT_C32GHJ34 | rainy | 92.9 | East-Park | 2024-05-04 | DRV_1003 | VEH_X6C222 | 2024-05-04T09:05:00 | 2024-05-04T10:30:00 | 23 | 22.7 | 75 | 85 | 14 | 13 | 1 | 86.1 | 22.7 | 62 | 10 |
| RT_D45JKL45 | cloudy | 100 | Midtown | 2024-05-05 | DRV_1004 | VEH_X7D333 | 2024-05-05T06:10:00 | 2024-05-05T06:55:00 | 7.2 | 7.4 | 35 | 45 | 2 | 2 | 0 | 19.3 | 3.1 | 16 | 10 |
| RT_E56MNO56 | clear | 90 | North-City | 2024-05-06 | DRV_1005 | VEH_X8E444 | 2024-05-06T14:50:00 | 2024-05-06T16:10:00 | 12.6 | 13.3 | 70 | 80 | 7 | 7 | 0 | 39.5 | 8.2 | 31 | 10 |
| RT_F67PQR67 | windy | 88.9 | Central-Industrial | 2024-05-07 | DRV_1006 | VEH_X9F555 | 2024-05-07T10:00:00 | 2024-05-07T12:30:00 | 66.8 | 67.2 | 120 | 150 | 18 | 18 | 0 | 122.1 | 45 | 77 | 30 |
| RT_G78STU78 | cloudy | 80 | Downtown | 2024-05-08 | DRV_1012 | VEH_X1G666 | 2024-05-08T07:45:00 | 2024-05-08T08:50:00 | 4.9 | 5 | 35 | 65 | 5 | 5 | 0 | 25.2 | 2.8 | 22 | 30 |
| RT_H89VWX89 | rainy | 94.3 | Outer-Rural | 2024-05-09 | DRV_1010 | VEH_X2H777 | 2024-05-09T17:30:00 | 2024-05-09T20:45:00 | 143.5 | 145.3 | 180 | 195 | 35 | 33 | 2 | 335.6 | 120 | 215 | 15 |
| RT_I90YZA90 | other | 94.7 | East-Harbor | 2024-05-10 | DRV_1015 | VEH_X3I888 | 2024-05-10T04:15:00 | 2024-05-10T06:45:00 | 58.2 | 59.8 | 145 | 150 | 19 | 18 | 1 | 105.9 | 36.7 | 68.6 | 5 |
| RT_J01BCD91 | foggy | 100 | West-Suburb | 2024-05-11 | DRV_1021 | VEH_X4J999 | 2024-05-11T16:10:00 | 2024-05-11T18:00:00 | 62.4 | 64.6 | 105 | 110 | 17 | 17 | 0 | 98.4 | 26.3 | 72 | 5 |
| RT_K12DFE92 | clear | 100 | Midtown | 2024-05-12 | DRV_1007 | VEH_X5K111 | 2024-05-12T11:00:00 | 2024-05-12T11:50:00 | 10.2 | 10.5 | 45 | 50 | 4 | 4 | 0 | 28.3 | 3.4 | 25 | 5 |
| RT_L23GHJ93 | cloudy | 90 | East-Park | 2024-05-13 | DRV_1018 | VEH_X6L222 | 2024-05-13T07:40:00 | 2024-05-13T09:10:00 | 31.2 | 31.8 | 90 | 105 | 10 | 9 | 1 | 57.6 | 18.2 | 39 | 15 |
| RT_M34JKL94 | windy | 83.3 | Central-Industrial | 2024-05-14 | DRV_1009 | VEH_X7M333 | 2024-05-14T15:20:00 | 2024-05-14T18:10:00 | 27.5 | 28.3 | 120 | 170 | 12 | 11 | 1 | 73.6 | 22.3 | 51 | 50 |
| RT_N45MNO95 | cloudy | 87.5 | South-East | 2024-05-15 | DRV_1023 | VEH_X8N444 | 2024-05-15T11:30:00 | 2024-05-15T12:50:00 | 16.9 | 17.2 | 75 | 80 | 8 | 7 | 1 | 39.2 | 5.7 | 33 | 5 |
| RT_O56PQR96 | rainy | 95.5 | South-Coast | 2024-05-16 | DRV_1025 | VEH_X9O555 | 2024-05-16T06:00:00 | 2024-05-16T08:45:00 | 95.1 | 96.4 | 160 | 165 | 22 | 21 | 1 | 180.9 | 71.9 | 109 | 5 |
| RT_P67STU97 | rainy | 92.3 | West-Suburb | 2024-05-17 | DRV_1027 | VEH_X1P666 | 2024-05-17T13:30:00 | 2024-05-17T15:20:00 | 27.8 | 30 | 110 | 120 | 13 | 12 | 1 | 65.9 | 19.9 | 46 | 10 |
| RT_Q78VWX98 | foggy | 93.3 | North-West | 2024-05-18 | DRV_1028 | VEH_X2Q777 | 2024-05-18T09:40:00 | 2024-05-18T11:30:00 | 53 | 53.2 | 100 | 110 | 15 | 14 | 1 | 68 | 21 | 47 | 10 |
| RT_R89YZA99 | windy | 96 | South-Coast | 2024-05-19 | DRV_1030 | VEH_X3R888 | 2024-05-19T04:50:00 | 2024-05-19T06:50:00 | 108 | 110.2 | 155 | 160 | 25 | 24 | 1 | 198.3 | 80.3 | 118 | 5 |
| RT_S01BCD02 | cloudy | 100 | North-East | 2024-05-20 | DRV_1032 | VEH_X4S999 | 2024-05-20T11:25:00 | 2024-05-20T12:30:00 | 8.5 | 9 | 55 | 65 | 3 | 3 | 0 | 24.4 | 3.1 | 21 | 10 |
| RT_T12DFE03 | cloudy | 100 | Downtown | 2024-05-21 | DRV_1033 | VEH_X5T111 | 2024-05-21T07:10:00 | 2024-05-21T08:05:00 | 7.9 | 8.1 | 40 | 55 | 4 | 4 | 0 | 27.9 | 3.5 | 24 | 15 |
What the 200 rows show
from the 200-row sampleOther (weather conditions) stands out: mean on_
- 95.5median on_
time_ percentage - 28.1median planned_
distance_ km - 29.9median actual_
distance_ km - 110median planned_
duration_ minutes - 119median actual_
duration_ minutes - 12median total_
deliveries
Median 95.5, from 0.0 to 100.0.
- string 5
- integer 6
- float 6
- date 1
- datetime 2
Columns
20 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 5 columns | |||
route_id | string | Unique identifier for the completed delivery routeunique | RT_A12BCD12 |
driver_id | string | Unique identifier for the driver assigned to the route | DRV_1001 |
vehicle_id | string | Unique identifier for the vehicle used on the route | VEH_X9A123 |
route_region | string | Region or zone where the route was executed | Downtown |
weather_conditions | string | Weather conditions during the route (e.g., clear, rainy, snowy)7 values · optional | clear |
| Numbers 12 columns | |||
planned_distance_km | float | Planned total distance for the route in kilometers0 or more | 5.6 |
actual_distance_km | float | Actual total distance traveled in kilometers0 or more | 6 |
planned_duration_minutes | integer | Planned total duration for the route in minutes0 or more | 30 |
actual_duration_minutes | integer | Actual total duration of the route in minutes0 or more | 35 |
total_deliveries | integer | Total number of deliveries scheduled for the route1 or more | 3 |
completed_deliveries | integer | Number of deliveries successfully completed0 or more | 3 |
failed_deliveries | integer | Number of deliveries that failed or were not completed0 or more | 0 |
total_cost_usd | float | Total cost incurred for the route in USD0 or more | 22.5 |
fuel_cost_usd | float | Fuel cost incurred for the route in USD0 or more · optional | 2.2 |
labor_cost_usd | float | Labor cost incurred for the route in USD0 or more · optional | 20 |
delay_minutes | integer | Total delay experienced on the route in minutes0 or more · optional | 5 |
on_time_percentage | float | Percentage of deliveries completed on time (0-100)0 to 100 · optional | 95 |
| Dates and times 3 columns | |||
delivery_date | date | Date when the route was completed | 2024-05-02 |
route_start_time | datetime | Timestamp when the route started | 2024-05-02T08:10:00 |
route_end_time | datetime | Timestamp when the route ended | 2024-05-02T08:45:00 |
Use it for
A logistics dashboard
On_
time_ percentage by weather_ conditions and a breakdown of route_ region. Excel, Power BI or Tableau. Why do the 28 other rows have a mean on_
time_ percentage of 78.3? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Routes200RT_A12BCD1295clearRT_B21DFE2390.9cloudyRT_C32GHJ3492.9rainy
A software demo
Believable routes with delivery_
date, driver_ id and vehicle_ id to fill a screen in front of a buyer.
This dataset200 rows20 columns
Yours1,000 rows20 columnsroute_region: UK only
blueprint · last-mile-delivery-route-metrics
Behind this dataset
Same schema. As many rows as you need.
These 200 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.
Rules it was built with
- Each route includes start/end time.
- Route geocoded and mapped.
- Delivery vehicle ID required.
- Delay events and reasons flagged.
- Fuel consumption logged per route.
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
- last-mile-delivery-route-metrics