Delivery Route Optimization Dataset

This dataset provides comprehensive synthetic records of last-mile delivery routes, including geospatial coordinates, stop sequences, package details, resource allocation, and contextual factors like weather and traffic. It is optimized for machine learning applications in route recommendation, delay prediction, and logistics resource planning, supporting both operational analytics and AI-driven optimization.

  • opened 17 times
  • last updated 20 Aug 2025
  • by GoMask
The brief that made it

Machine learning-based route recommendation

Sample rows

preview · 8 of 200 rows · all 28 columns
route_idstringresource_typestringpackage_weight_kgfloatstop_statestringvehicle_idstringdriver_idstringroute_datedateroute_start_timedatetimeroute_end_timedatetimetotal_stopsintegerroute_distance_kmfloatroute_duration_minutesfloatstop_idstringstop_sequenceintegerstop_scheduled_timedatetimestop_actual_timedatetimedelay_minutesfloatstop_latitudefloatstop_longitudefloatstop_street_addressstringstop_citystringstop_postal_codestringstop_countrystringpackage_idstringpackage_volume_m3floatdelivery_statusstringweather_conditionstringtraffic_conditionstring
R001van2.8NYV001D0012024-05-012024-05-01T08:00:002024-05-01T10:15:00318.4135S00112024-05-01T08:30:002024-05-01T08:33:00340.7128-74.006123 Broadway AveNew York10007USAP0010.07deliveredcloudymoderate
R001van7.2NYV001D0012024-05-012024-05-01T08:00:002024-05-01T10:15:00318.4135S00222024-05-01T09:00:002024-05-01T09:09:00940.7306-73.9866456 5th AveNew York10010USAP0020.15deliveredcloudyheavy
R001van1.5NYV001D0012024-05-012024-05-01T08:00:002024-05-01T10:15:00318.4135S00332024-05-01T10:00:00blankblank40.7498-73.9876789 Madison StNew York10016USAP0030.04pendingrainmoderate
R002van1.1IDFV002D0022024-05-022024-05-02T07:30:002024-05-02T09:05:00211.295S00412024-05-02T08:00:002024-05-02T08:04:00448.85662.352212 Rue de RivoliParis75001FranceP0040.03deliveredclearlight
R002van2.6IDFV002D0022024-05-022024-05-02T07:30:002024-05-02T09:05:00211.295S00522024-05-02T09:00:002024-05-02T09:02:00248.87082.30425 Avenue MontaigneParis75008FranceP0050.08deliveredcloudylight
R003truck78.6TōkyōV003D0032024-05-032024-05-03T06:00:002024-05-03T20:00:0021100.7840S00612024-05-03T12:30:002024-05-03T13:54:008435.6895139.69171 ChiyodaTokyo100-0001JapanP0060.9deliveredfogheavy
R003truck59.3TōkyōV003D0032024-05-032024-05-03T06:00:002024-05-03T20:00:0021100.7840S00722024-05-03T19:30:002024-05-03T20:15:004535.6762139.650322 ShibuyaTokyo150-0002JapanP0070.7returnedrainblocked
R004bike5.3ENGV004D0042024-05-042024-05-04T09:15:002024-05-04T12:00:00428.2165S00812024-05-04T09:40:002024-05-04T09:38:00-251.5074-0.127814 Oxford StLondonW1D 1BSUKP0080.11deliveredclearlight

What the 200 rows show

from the 200-row sample

Truck (resource type) stands out: mean package_weight_kg is 43.4, against 5.0 for the rest.

  • 5.3median package_weight_kg
  • 4delivery statuses
  • 4traffic conditions
  • 6weather conditions
  • 9stop countries
  • 13stop cities
Mean package_weight_kg by resource_type200 rows
040804.9van107 rows43.4truck38 rows3.7bike17 rows5.7car38 rows
package_weight_kg200 rows, in bands of 10
080160159688535321050100package_weight_kg →

Median 5.3, from 0.0 to 92.6.

stop_state200 rows · top 10 of 24 values
  1. IDF28
  2. NY24
  3. MOW18
  4. ENG17
  5. SG17
  6. CABA14
  7. NSW11
  8. Île-de-France7
  9. TDF6
  10. CDMX6
28 columns by typefrom the column list below
  • string 14
  • integer 2
  • float 7
  • date 1
  • datetime 4

Columns

28 columns in three groups
blueprint · 28 columns
columntypedescriptionexample
Text 14 columns
route_idstringUnique identifier for each delivery routeuniqueR001
vehicle_idstringUnique identifier for the delivery vehicle assigned to the routeV001
driver_idstringUnique identifier for the driver assigned to the routeD001
stop_idstringUnique identifier for each delivery stopuniqueS001
stop_street_addressstringStreet address of the delivery stop123 Broadway Ave
stop_citystringCity of the delivery stopNew York
stop_statestringState or region of the delivery stopNY
stop_postal_codestringPostal code of the delivery stop10007
stop_countrystringCountry of the delivery stop9 countriesUSA
package_idstringUnique identifier for the package delivered at the stopP001
delivery_statusstringStatus of the delivery at the stopdelivered · failed · pending · returneddelivered
resource_typestringType of resource allocated for the route (e.g., vehicle type)van · truck · bike · car · optionalvan
weather_conditionstringWeather condition during delivery at the stop6 values · optionalcloudy
traffic_conditionstringTraffic condition during delivery at the stoplight · moderate · heavy · blocked · optionalmoderate
Numbers 9 columns
total_stopsintegerTotal number of delivery stops in the route1 or more3
route_distance_kmfloatTotal distance of the route in kilometers0 or more18.4
route_duration_minutesfloatEstimated duration of the route in minutes0 or more135
stop_sequenceintegerOrder of the stop in the route (1 = first stop)1 or more1
delay_minutesfloatDelay in minutes at the stop (actual - scheduled)optional3
stop_latitudefloatLatitude coordinate of the delivery stop-90 to 9040.7128
stop_longitudefloatLongitude coordinate of the delivery stop-180 to 180-74.006
package_weight_kgfloatWeight of the package in kilograms0 or more2.8
package_volume_m3floatVolume of the package in cubic meters0 or more · optional0.07
Dates and times 5 columns
route_datedateDate when the delivery route is scheduled2024-05-01
route_start_timedatetimeScheduled start time for the delivery route2024-05-01T08:00:00
route_end_timedatetimeScheduled end time for the delivery routeoptional2024-05-01T10:15:00
stop_scheduled_timedatetimeScheduled arrival time at the stop2024-05-01T08:30:00
stop_actual_timedatetimeActual arrival time at the stopoptional2024-05-01T08:33:00

15 SQL practice questions for the Delivery Route Optimization Dataset

Written for this data and checked against it: 12 SQL and 3 Power BI exercises from beginner to advanced. Try the first three free; the practice kit has all 15, a starter .sql file and an answer key with every expected result.

  1. 01BeginnerFreeSELECT · WHERE · ORDER BY

    Failed deliveries list

    List every stop whose delivery_status is 'failed'. Return the columns stop_id, route_id, stop_city and package_weight_kg, ordered by stop_id and then route_id.

    Tables: delivery_route_optimization_dataset · your result: 4 columns, 20 rows
  2. 02BeginnerFreeDISTINCT · ORDER BY

    Distinct cities and countries

    Return each distinct combination of stop_country and stop_city. Return the columns stop_country and stop_city, ordered by stop_country and then stop_city.

    Tables: delivery_route_optimization_dataset · your result: 2 columns, 13 rows
  3. 03BeginnerFreeGROUP BY · COUNT

    Stops per delivery status

    Count the stops for each delivery_status. Return the columns delivery_status and stop_count, ordered by stop_count descending and then by delivery_status.

    Tables: delivery_route_optimization_dataset · your result: 2 columns, 4 rows
  1. 04Average weight by vehicle typeBeginner
  2. 05Average delay by traffic conditionPower BI
  3. 06Countries with many late stopsIntermediate
  4. 07Delay bucketsIntermediate
  5. 08Stops and distance by monthIntermediate
  6. 09Cities slower than overall averageIntermediate
  7. 10Failure rate by weather conditionPower BI
  8. 11Rank cities by delay within countryAdvanced
  9. 12Delay change versus previous stopAdvanced
  10. 13Running package weight by dateAdvanced
  11. 14Share of weight by countryAdvanced
  12. 15Most delayed stop per countryPower BI

Get the practice kit: the full dataset, all 15 exercises, a starter .sql and the answer key. One-time $3.99.

Get the practice kit · $3.99

Use it for

  • median package…5.3200 rowsmean package weight k…4.9van43.4truck3.7bike5.7car

    A logistics dashboard

    Package_weight_kg by resource_type and a breakdown of stop_state. Excel, Power BI or Tableau.

  • Why do the 38 truck rows have a mean package_weight_kg of 43.4?

    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 routes with vehicle_id, driver_id and route_date to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 28 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

1,000 rows: $1.99 · 10,000: $12.99One-time. No subscription. All prices

This dataset200 rows28 columns
Yours1,000 rows28 columnsstop_street_address: UK only

blueprint · delivery-route-optimization-dataset

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 28 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
  • Routes must contain geolocation points
  • Include traffic and weather features
  • Simulate peak and off-peak scenarios
  • Label delivery success and delays
  • Vary vehicle types and capacities
Rows
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
delivery-route-optimization-dataset

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