Transportation Route Efficiency Dataset

This dataset provides comprehensive trip-level details for transportation and logistics route optimization, including trip timing, route selection, vehicle and driver assignment, and efficiency metrics such as delays, fuel usage, and route scores. It enables in-depth analysis of operational performance, resource utilization, and external factors like traffic and weather, supporting data-driven decision-making for route planning and cost reduction.

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

Optimizing delivery routes for reduced fuel consumption and delays

Sample rows

preview · 8 of 200 rows · all 26 columns
trip_idstringweather_conditionsstringroute_efficiency_scorefloatorigin_statestringvehicle_idstringdriver_idstringroute_idstringdeparture_datetimedatetimearrival_datetimedatetimeorigin_citystringorigin_postal_codestringorigin_countrystringdestination_citystringdestination_statestringdestination_postal_codestringdestination_countrystringdistance_kmfloatplanned_duration_minutesintegeractual_duration_minutesintegerdelay_minutesintegerfuel_consumed_litersfloataverage_speed_kphfloattraffic_conditionsstringcargo_typestringcargo_weight_kgfloatnotesstring
TRIP000001clear0.98TXVEH102DRV203RTE5012023-01-10T08:05:002023-01-10T11:35:00Dallas75201USAHoustonTX77002USA385.4210210082.3110.1lightconsumer electronics1850Smooth trip, on-time delivery to Houston.
TRIP000002rain0.94ILVEH103DRV204RTE5022023-02-15T09:15:002023-02-15T15:45:00Chicago60601USADetroitMI48226USA450.3390390094.769.3moderateauto parts1250Traffic moderate due to rain, but kept schedule.
TRIP000003fog0.99ONVEH104DRV205RTE5032023-03-22T07:30:002023-03-22T07:55:00TorontoM5H2N2CanadaTorontoONM5H2N2Canada12252502.929lightpharmaceuticals7Short local delivery, light fog present.
TRIP000004rain0.83CAVEH105DRV206RTE5042023-04-18T06:45:002023-04-18T14:45:00San Diego92101USASan FranciscoCA94103USA808.648051030165.595.2heavyfurniture3200Heavy traffic and rain caused moderate delay.
TRIP000005clear0.89NLVEH106DRV207RTE5052023-05-20T15:00:002023-05-20T19:30:00Monterrey64000MexicoSan AntonioTX78205USA545.725527015112.8121.4moderatepackaged food800Border crossing added minor delay.
TRIP000006clear0.96FLVEH107DRV208RTE5062023-06-12T10:05:002023-06-12T13:05:00Miami33101USAOrlandoFL32801USA376.3180180075.8125.4lightsporting goods980Efficient trip, no delays, sporting goods delivered.
TRIP000007clear0.92BCVEH108DRV209RTE5072023-07-08T07:10:002023-07-08T12:10:00VancouverV5K0A1CanadaCalgaryABT2P1J9Canada9703003000211.3194moderatebooks4300Direct shipment, books delivered on time.
TRIP000008clear1CAVEH109DRV210RTE5082023-08-13T13:20:002023-08-13T13:35:00Los Angeles90001USALos AngelesCA90001USA2151500.78moderateofficial documents1Express local document delivery.

What the 200 rows show

from the 200-row sample

Storm (weather conditions) stands out: mean route_efficiency_score is 0.61, against 0.94 for the rest.

  • 0.97median route_efficiency_score
  • 4traffic conditionses
  • 7origin countries
  • 7destination countries
  • 56origin cities
  • 59destination states
Mean route_efficiency_score by weather_conditions200 rows
00.510.97clear110 ro…0.83rain40 rows0.95snow5 rows0.97fog11 rows0.61storm23 rows1.0unkno…11 rows
route_efficiency_score200 rows, in bands of 0.1
0751502621013181490.30.71route_efficiency_score →

Median 0.97, from 0.37 to 1.0.

origin_state200 rows · top 10 of 39 values
  1. CA33
  2. TX30
  3. FL10
  4. ON8
  5. AB7
  6. CDMX7
  7. IL6
  8. QC6
  9. BE6
  10. BC5
26 columns by typefrom the column list below
  • string 16
  • integer 3
  • float 5
  • datetime 2

Columns

26 columns in three groups
blueprint · 26 columns
columntypedescriptionexample
Text 16 columns
trip_idstringUnique identifier for each transportation tripuniqueTRIP000001
vehicle_idstringUnique identifier for the vehicle used on the tripVEH102
driver_idstringUnique identifier for the driver assigned to the tripDRV203
route_idstringUnique identifier for the route taken during the tripRTE501
origin_citystringCity where the trip originatedDallas
origin_statestringState where the trip originatedTX
origin_postal_codestringPostal code of the trip's originoptional75201
origin_countrystringCountry of the trip's origin7 countriesUSA
destination_citystringCity where the trip endedHouston
destination_statestringState where the trip endedTX
destination_postal_codestringPostal code of the trip's destinationoptional77002
destination_countrystringCountry of the trip's destination7 countriesUSA
traffic_conditionsstringGeneral traffic conditions during the triplight · moderate · heavy · unknown · optionallight
weather_conditionsstringGeneral weather conditions during the trip6 values · optionalclear
cargo_typestringType of cargo transported on the tripoptionalconsumer electronics
notesstringAdditional notes or comments about the tripoptionalLocal pharmacy delivery.
Numbers 8 columns
distance_kmfloatTotal distance of the trip in kilometers0 or more385.4
planned_duration_minutesintegerPlanned duration of the trip in minutes1 or more210
actual_duration_minutesintegerActual duration of the trip in minutes1 or more210
delay_minutesintegerDifference between actual and planned duration in minutes0
fuel_consumed_litersfloatTotal fuel consumed during the trip in liters0 or more82.3
average_speed_kphfloatAverage speed of the vehicle during the trip in kilometers per hour0 or more110.1
route_efficiency_scorefloatCalculated efficiency score for the route (0-1 scale, higher is better)0 to 10.98
cargo_weight_kgfloatWeight of cargo transported in kilograms0 or more · optional1850
Dates and times 2 columns
departure_datetimedatetimeDate and time when the trip started2023-01-10T08:05:00
arrival_datetimedatetimeDate and time when the trip ended2023-01-10T11:35:00

Use it for

  • median route e…0.97200 rowsmean route efficiency…0.97clear0.83rain0.95snow0.97fog

    A transportation dashboard

    Route_efficiency_score by weather_conditions and a breakdown of origin_state. Excel, Power BI or Tableau.

  • Why do the 23 storm rows have a mean route_efficiency_score of 0.61?

    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 trips with vehicle_id, driver_id and route_id to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 26 columns, your size and your rules. See 20 rows before you pay.

Preview 20 rows free

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This dataset200 rows26 columns
Yours1,000 rows26 columnsorigin_city: UK only

blueprint · transportation-route-efficiency-dataset

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 26 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
  • Trip start/end locations logged
  • Route choices and alternatives recorded
  • Efficiency scored by time and cost
  • Traffic and delay events annotated
Rows
Open the blueprint in Data Factory

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Exports
CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence
yours to use, including commercially
API slug
transportation-route-efficiency-dataset

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