Vehicle Route Efficiency Scores
This dataset provides detailed efficiency scores for transportation routes, combining schedule adherence and resource utilization metrics to help optimize fleet operations and reduce costs. It includes route, vehicle, driver, timing, and performance data, making it ideal for analyzing and improving transportation efficiency across logistics networks.
Sample rows
preview · 8 of 45 rows · all 17 columns| route_idstring | vehicle_typestring | distance_kmfloat | route_namestring | vehicle_idstring | driver_idstring | scheduled_departure_timedatetime | actual_departure_timedatetime | scheduled_arrival_timedatetime | actual_arrival_timedatetime | scheduled_duration_minutesinteger | actual_duration_minutesinteger | resource_utilization_percentfloat | schedule_adherence_percentfloat | efficiency_scorefloat | datedate | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| R10001 | Truck | 88.5 | Central Loop | V301 | D2101 | 2023-10-01T07:00:00 | 2023-10-01T07:05:00 | 2023-10-01T09:30:00 | 2023-10-01T09:33:00 | 150 | 148 | 81.2 | 97.8 | 89.3 | 2023-10-01 | Slight traffic delays, overall smooth run. |
| R10002 | Van | 62.3 | North Distribution | V307 | D2112 | 2023-10-02T06:45:00 | 2023-10-02T06:50:00 | 2023-10-02T08:00:00 | 2023-10-02T08:12:00 | 75 | 82 | 67.5 | 89.1 | 78.2 | 2023-10-02 | blank |
| R10003 | Truck | 101.9 | East Express | V311 | D2120 | 2023-10-03T08:10:00 | 2023-10-03T08:12:00 | 2023-10-03T10:25:00 | 2023-10-03T10:25:00 | 135 | 133 | 92.8 | 99.5 | 96.7 | 2023-10-03 | Excellent adherence. |
| R10004 | Bus | 53.6 | West Suburb Shuttle | V315 | D2109 | 2023-10-04T07:45:00 | 2023-10-04T07:43:00 | 2023-10-04T09:40:00 | 2023-10-04T09:39:00 | 115 | 116 | 76.3 | 99.1 | 87.7 | 2023-10-04 | blank |
| R10005 | Van | 47.1 | Airport Link | V320 | D2132 | 2023-10-05T05:30:00 | 2023-10-05T05:29:00 | 2023-10-05T06:40:00 | 2023-10-05T06:43:00 | 70 | 74 | 54.4 | 94.2 | 77 | 2023-10-05 | blank |
| R10006 | Bus | 36.7 | Downtown Circuit | V325 | D2141 | 2023-10-06T09:00:00 | 2023-10-06T09:00:00 | 2023-10-06T10:20:00 | 2023-10-06T10:24:00 | 80 | 84 | 60.1 | 95 | 80.6 | 2023-10-06 | Minor delay at stop 3. |
| R10007 | Truck | 73.2 | Harbor Run | V330 | D2127 | 2023-10-07T06:15:00 | 2023-10-07T06:19:00 | 2023-10-07T08:35:00 | 2023-10-07T08:41:00 | 140 | 142 | 70.7 | 96.3 | 84.8 | 2023-10-07 | blank |
| R10008 | Van | 58 | South End Connector | V302 | D2103 | 2023-10-08T08:30:00 | 2023-10-08T08:43:00 | 2023-10-08T10:00:00 | 2023-10-08T10:17:00 | 90 | 94 | 79.5 | 81.2 | 80.1 | 2023-10-08 | Late start due to maintenance. |
| R10009 | Truck | 90.2 | River Valley | V309 | D2114 | 2023-10-09T07:15:00 | 2023-10-09T07:15:00 | 2023-10-09T09:30:00 | 2023-10-09T09:31:00 | 135 | 136 | 94.9 | 99.3 | 97.1 | 2023-10-09 | blank |
| R10010 | Bus | 48.4 | Uptown Loop | V313 | D2150 | 2023-10-10T10:30:00 | 2023-10-10T10:34:00 | 2023-10-10T12:20:00 | 2023-10-10T12:32:00 | 110 | 118 | 62.4 | 85.1 | 75 | 2023-10-10 | Heavy traffic downtown. |
| R10011 | Truck | 77.3 | Industrial Park | V336 | D2123 | 2023-10-11T06:10:00 | 2023-10-11T06:08:00 | 2023-10-11T08:35:00 | 2023-10-11T08:36:00 | 145 | 148 | 86.6 | 98.3 | 92.7 | 2023-10-11 | blank |
| R10012 | Van | 38.9 | City Center Transfer | V321 | D2104 | 2023-10-12T09:25:00 | 2023-10-12T09:25:00 | 2023-10-12T10:35:00 | 2023-10-12T10:31:00 | 70 | 66 | 59.3 | 99.7 | 82.5 | 2023-10-12 | blank |
| R10013 | Truck | 82.1 | Hilltop Road | V340 | D2154 | 2023-10-13T07:20:00 | 2023-10-13T07:19:00 | 2023-10-13T09:20:00 | 2023-10-13T09:18:00 | 120 | 119 | 89.7 | 99.9 | 94.8 | 2023-10-13 | blank |
| R10014 | Bus | 41.2 | Westside Shuttle | V344 | D2160 | 2023-10-14T08:00:00 | 2023-10-14T08:00:00 | 2023-10-14T09:45:00 | 2023-10-14T09:47:00 | 105 | 107 | 75 | 98.1 | 86.9 | 2023-10-14 | blank |
| R10015 | Van | 50.5 | Market Run | V303 | D2107 | 2023-10-15T06:55:00 | 2023-10-15T07:01:00 | 2023-10-15T08:00:00 | 2023-10-15T08:07:00 | 65 | 66 | 83.2 | 91.8 | 85.6 | 2023-10-15 | blank |
| R10016 | Bus | 44.8 | East Suburb Circuit | V318 | D2137 | 2023-10-16T09:10:00 | 2023-10-16T09:15:00 | 2023-10-16T10:40:00 | 2023-10-16T10:49:00 | 90 | 94 | 58 | 87.3 | 72.6 | 2023-10-16 | Late due to road construction. |
| R10017 | Truck | 97.6 | Outer Ring | V305 | D2157 | 2023-10-17T05:55:00 | 2023-10-17T05:57:00 | 2023-10-17T08:25:00 | 2023-10-17T08:25:00 | 150 | 148 | 88.3 | 99.8 | 94.1 | 2023-10-17 | blank |
| R10018 | Van | 62.7 | Greenbelt Express | V312 | D2118 | 2023-10-18T07:40:00 | 2023-10-18T07:39:00 | 2023-10-18T09:00:00 | 2023-10-18T09:00:00 | 80 | 81 | 71.4 | 98.9 | 85 | 2023-10-18 | blank |
| R10019 | Truck | 76.3 | Portside Ferry | V316 | D2149 | 2023-10-19T08:20:00 | 2023-10-19T08:22:00 | 2023-10-19T10:15:00 | 2023-10-19T10:25:00 | 115 | 123 | 66.9 | 91 | 77.2 | 2023-10-19 | Delay at loading dock. |
| R10020 | Bus | 29.1 | Civic Center | V319 | D2130 | 2023-10-20T09:30:00 | 2023-10-20T09:30:00 | 2023-10-20T10:30:00 | 2023-10-20T10:30:00 | 60 | 60 | 61.2 | 100 | 80.6 | 2023-10-20 | blank |
What the 45 rows show
from the 45-row sampleTruck (vehicle type) stands out: mean distance_
- 53.7median distance_
km - 90median scheduled_
duration_ minutes - 92median actual_
duration_ minutes - 73.1median resource_
utilization_ percent - 96.3median schedule_
adherence_ percent - 83.4median efficiency_
score
Median 53.7, from 28.5 to 101.9.
- string 6
- integer 2
- float 4
- date 1
- datetime 4
Columns
17 columns in three groups| column | type | description | example |
|---|---|---|---|
| Text 6 columns | |||
route_id | string | Unique identifier for the transportation routeunique | R10001 |
route_name | string | Descriptive name of the transportation route | Central Loop |
vehicle_id | string | Unique identifier for the vehicle assigned to this route | V301 |
vehicle_type | string | Type or category of the vehicle (e.g., truck, van, bus)3 types · optional | Truck |
driver_id | string | Unique identifier for the driver assigned to the routeoptional | D2101 |
notes | string | Additional notes or comments about the route's performance or anomaliesoptional | Excellent adherence. |
| Numbers 6 columns | |||
distance_km | float | Total distance of the route in kilometers0 or more | 88.5 |
scheduled_duration_minutes | integer | Planned duration of the route in minutes0 or more | 150 |
actual_duration_minutes | integer | Actual duration of the route in minutes0 or more · optional | 148 |
resource_utilization_percent | float | Percentage of resource utilization (e.g., vehicle capacity used) for the route0 to 100 | 81.2 |
schedule_adherence_percent | float | Percentage indicating how closely the route adhered to its schedule (100% = on time)0 to 100 | 97.8 |
efficiency_score | float | Composite efficiency score for the route, calculated based on schedule adherence and resource utilization (0-100 scale)0 to 100 | 89.3 |
| Dates and times 5 columns | |||
scheduled_departure_time | datetime | Planned departure time for the route | 2023-10-01T07:00:00 |
actual_departure_time | datetime | Actual departure time for the routeoptional | 2023-10-01T07:05:00 |
scheduled_arrival_time | datetime | Planned arrival time for the route | 2023-10-01T09:30:00 |
actual_arrival_time | datetime | Actual arrival time for the routeoptional | 2023-10-01T09:33:00 |
date | date | Date when the route was completed | 2023-10-01 |
Use it for
A transportation dashboard
Distance_
km by vehicle_ type. Excel, Power BI or Tableau. Why do the 17 Truck rows have a mean distance_
km of 76.5? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Routes45R1000188.5TruckR1000262.3VanR10003101.9Truck
A software demo
Believable routes with route_
name, vehicle_ id and vehicle_ type to fill a screen in front of a buyer.
This dataset45 rows17 columns
Yours1,000 rows17 columns
blueprint · vehicle-route-efficiency-scores
Behind this dataset
Same schema. As many rows as you need.
These 45 rows came out of a blueprint — 17 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 assigned a unique ID
- Efficiency score calculated via defined formula
- Late arrivals penalized in scoring
- Fuel usage factored into efficiency
- Flag routes below target efficiency
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
- vehicle-route-efficiency-scores