Logistics
26 columns
200 rows
8 formats

Freight Route Performance Analytics

This dataset provides detailed records of freight shipments, including route information, timing, costs, shipment characteristics, and delivery performance metrics. It enables comprehensive analysis of route efficiency, cost drivers, and on-time delivery, supporting logistics planners in optimizing routes and reducing operational expenses. The dataset is ideal for benchmarking, predictive analytics, and supply chain optimization.

last updated 22 Jul 2025
by GoMask

The brief that made it

Route optimization and cost reduction analysis

Sample rows

preview · 8 of 200 rows · all 26 columns

shipment_idstring	vehicle_typestring	shipment_weight_kgfloat	on_time_deliveryboolean	origin_statestring	route_idstring	origin_citystring	origin_countrystring	destination_citystring	destination_statestring	destination_countrystring	departure_datetimedatetime	arrival_datetimedatetime	scheduled_arrival_datetimedatetime	carrier_namestring	distance_kmfloat	duration_hoursfloat	total_cost_usdfloat	fuel_cost_usdfloat	toll_cost_usdfloat	other_cost_usdfloat	delay_hoursfloat	shipment_volume_m3float	number_of_stopsinteger	shipment_typestring	temperature_controlledboolean
SHP000001	truck	19850	true	TX	RTE1001	Houston	USA	Atlanta	GA	USA	2023-01-05T08:30:00Z	2023-01-05T22:30:00Z	2023-01-05T23:00:00Z	TransAmerican Freight	1270.5	14	1940.5	1230.5	210	500	0	62.3	2	general	false
SHP000002	truck	10230	false	ON	RTE1002	Toronto	Canada	Montreal	QC	Canada	2023-02-14T06:15:00Z	2023-02-14T12:45:00Z	2023-02-14T12:15:00Z	Canadian Express Logistics	541.2	6.5	870.2	520.2	80	270	0.5	22.4	1	general	false
SHP000003	ship	42000	true	blank	RTE1003	Hamburg	Germany	Rotterdam	blank	Netherlands	2023-03-10T11:00:00Z	2023-03-12T07:00:00Z	2023-03-12T12:00:00Z	EuroMarine Shipping Co.	463.4	44	6800	5200	360	1240	0	160.7	1	general	false
SHP000004	truck	21300	false	MH	RTE1004	Mumbai	India	Delhi	DL	India	2023-04-02T04:45:00Z	2023-04-02T19:15:00Z	2023-04-02T18:00:00Z	BlueLine Roadways Pvt	1425	14.5	1235.5	850.5	135	250	1.25	55.2	2	general	false
SHP000005	ship	90000	true	blank	RTE1005	Shanghai	China	Los Angeles	CA	USA	2023-05-25T18:00:00Z	2023-06-05T13:00:00Z	2023-06-05T14:00:00Z	Asia Pacific Maritime	10400	260	38900	28750	3100	7050	0	300	0	general	false
SHP000006	truck	15600	true	TX	RTE1006	Dallas	USA	Chicago	IL	USA	2023-06-10T07:00:00Z	2023-06-10T16:00:00Z	2023-06-10T16:30:00Z	Midwest Freight Lines	1290	9	1780	1230	150	400	0	34.5	2	general	false
SHP000007	truck	8900	false	SP	RTE1007	Sao Paulo	Brazil	Rio de Janeiro	RJ	Brazil	2023-07-03T15:30:00Z	2023-07-03T20:00:00Z	2023-07-03T19:30:00Z	BrasilLog Transportes	430	4.5	620	400	90	130	0.5	23	1	general	false
SHP000008	rail	11000	false	VIC	RTE1008	Melbourne	Australia	Sydney	NSW	Australia	2023-08-17T01:00:00Z	2023-08-17T10:30:00Z	2023-08-17T10:00:00Z	AussieFast Logistics	877	9.5	1120	810	80	230	0.5	36	2	perishable	true
SHP000009	rail	6700	true	blank	RTE1009	London	UK	Paris	blank	France	2023-09-02T06:00:00Z	2023-09-02T13:00:00Z	2023-09-02T13:00:00Z	EuroStar Freight Ltd	495	7	880	620	80	180	0	18.2	1	perishable	true
SHP000010	truck	18600	true	NY	RTE1010	New York	USA	Miami	FL	USA	2023-10-11T09:00:00Z	2023-10-12T05:00:00Z	2023-10-12T06:00:00Z	Eastern Corridor Haulage	2068	20	3240	2100	340	800	0	50.2	3	general	false
SHP000011	ship	38100	true	NSW	RTE1011	Sydney	Australia	Auckland	blank	New Zealand	2023-12-03T22:00:00Z	2023-12-05T08:00:00Z	2023-12-05T10:00:00Z	Tasman Sea Carriers	2160	34	5400	4200	410	790	0	120.5	1	general	false
SHP000012	truck	14200	true	CA	RTE1012	San Francisco	USA	Seattle	WA	USA	2024-01-20T05:00:00Z	2024-01-20T17:30:00Z	2024-01-20T18:00:00Z	Pacific Northwest Freight	1285	12.5	1850	1240	120	490	0	39.5	2	general	false
SHP000013	truck	11900	false	DL	RTE1013	Delhi	India	Ahmedabad	GJ	India	2024-02-15T03:00:00Z	2024-02-15T13:30:00Z	2024-02-15T12:30:00Z	Indian Road Carriers	956	10.5	1050	720	110	220	1	31.2	2	general	false
SHP000014	rail	5100	true	blank	RTE1014	Berlin	Germany	Prague	blank	Czech Republic	2024-03-18T07:00:00Z	2024-03-18T19:00:00Z	2024-03-18T19:30:00Z	Central Euro Freight	349	12	600	390	70	140	0	11.5	1	perishable	true
SHP000015	truck	17000	false	TX	RTE1015	Houston	USA	Mexico City	blank	Mexico	2024-04-11T06:30:00Z	2024-04-12T02:30:00Z	2024-04-12T02:00:00Z	TransAmerican Freight	1440	20	2300	1510	170	620	0.5	47.5	3	general	false
SHP000016	truck	6200	false	blank	RTE1016	Nairobi	Kenya	Mombasa	blank	Kenya	2024-05-07T04:00:00Z	2024-05-07T12:30:00Z	2024-05-07T12:00:00Z	East Africa Logistics	485	8.5	550	400	30	120	0.5	13	1	hazardous	false
SHP000017	truck	9700	true	IL	RTE1017	Chicago	USA	St Louis	MO	USA	2024-06-13T07:45:00Z	2024-06-13T12:15:00Z	2024-06-13T12:30:00Z	Great Lakes Trucking	482	4.5	450	310	40	100	0	20.2	1	general	false
SHP000018	rail	9000	false	QLD	RTE1018	Brisbane	Australia	Melbourne	VIC	Australia	2024-07-08T02:00:00Z	2024-07-08T21:00:00Z	2024-07-08T20:00:00Z	AussieFast Logistics	1683	19	1750	1340	110	300	1	44.2	2	perishable	true
SHP000019	truck	11500	true	CA	RTE1019	Los Angeles	USA	Phoenix	AZ	USA	2024-08-12T06:00:00Z	2024-08-12T15:30:00Z	2024-08-12T16:00:00Z	Southwest Logistics	595	9.5	800	520	70	210	0	26.3	2	general	false
SHP000020	truck	16000	false	blank	RTE1020	Cape Town	South Africa	Johannesburg	blank	South Africa	2024-09-09T05:00:00Z	2024-09-09T19:00:00Z	2024-09-09T18:00:00Z	Rainbow Nation Freight	1400	14	1350	900	120	330	1	41	2	hazardous	false

What the 200 rows show

from the 200-row sample

Ship (vehicle type) stands out: mean shipment_weight_kg is 47,976, against 7,429 for the rest.

89%on_time_delivery = true
7,000median shipment_weight_kg
4shipment types
35origin countries
50destination countries
695.4median distance_km

Mean shipment_weight_kg by vehicle_type200 rows

shipment_weight_kg200 rows, in bands of 10k

Median 7,000, from 0 to 92,000.

origin_state92 rows with a value · 108 left blank

CA9
DL9
TX8
IL7
WC6
ON5
MH5
QLD5
SP4
VIC4

26 columns by typefrom the column list below

string 11
integer 1
float 9
datetime 3
boolean 2

Columns

26 columns in four groups

blueprint · 26 columns
column	type	description	example
Text 11 columns
`shipment_id`	string	Unique identifier for each freight shipmentunique	SHP000001
`route_id`	string	Unique identifier for the route taken by the shipment	RTE1001
`origin_city`	string	City where the shipment originated	Houston
`origin_state`	string	State or province where the shipment originatedoptional	TX
`origin_country`	string	Country where the shipment originated	USA
`destination_city`	string	City where the shipment is delivered	Atlanta
`destination_state`	string	State or province where the shipment is deliveredoptional	GA
`destination_country`	string	Country where the shipment is delivered	USA
`carrier_name`	string	Name of the carrier or logistics provider responsible for the shipment	TransAmerican Freight
`vehicle_type`	string	Type of vehicle used for the shipment (e.g., truck, rail, air, ship)truck · rail · air · ship · other	truck
`shipment_type`	string	Type of goods shipped (e.g., perishable, hazardous, general cargo)perishable · hazardous · general · other · optional	general
Numbers 10 columns
`distance_km`	float	Total distance covered by the shipment in kilometers0 or more	1270.5
`duration_hours`	float	Total transit time in hours from departure to arrival0 or more	14
`total_cost_usd`	float	Total cost incurred for the shipment in USD0 or more	1940.5
`fuel_cost_usd`	float	Fuel cost component of the total shipment cost in USD0 or more · optional	1230.5
`toll_cost_usd`	float	Toll cost component of the total shipment cost in USD0 or more · optional	210
`other_cost_usd`	float	Other miscellaneous costs associated with the shipment in USD0 or more · optional	500
`delay_hours`	float	Number of hours the shipment was delayed beyond the scheduled arrival time (0 if on time or early)0 or more	0
`shipment_weight_kg`	float	Total weight of the shipment in kilograms0 or more	19850
`shipment_volume_m3`	float	Total volume of the shipment in cubic meters0 or more · optional	62.3
`number_of_stops`	integer	Number of intermediate stops made during the shipment route0 or more · optional	2
Dates and times 3 columns
`departure_datetime`	datetime	Date and time when the shipment departed from the origin	2023-01-05T08:30:00Z
`arrival_datetime`	datetime	Date and time when the shipment arrived at the destination	2023-01-05T22:30:00Z
`scheduled_arrival_datetime`	datetime	Scheduled date and time for the shipment to arrive at the destination	2023-01-05T23:00:00Z
True or false 2 columns
`on_time_delivery`	boolean	Indicates whether the shipment arrived on or before the scheduled arrival time	true
`temperature_controlled`	boolean	Indicates if the shipment required temperature controloptional	false

Use it for

A logistics dashboard
The on_time_delivery rate, shipment_weight_kg by vehicle_type and a breakdown of origin_state. Excel, Power BI or Tableau.
Why do the 29 ship rows have a mean shipment_weight_kg of 47,976?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Shipments200
SHP00000119850truck
SHP00000210230truck
SHP00000342000ship
A software demo
Believable shipments with route_id, origin_city and origin_state to fill a screen in front of a buyer.

This dataset200 rows26 columns

Yours1,000 rows26 columnsorigin_city: UK only

blueprint · freight-route-performance-analytics

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

Each row represents a unique shipment route instance
Includes origin, destination, transport mode, and carrier
Captures planned vs actual delivery time
Records transport cost and delay reasons
Excludes cancelled shipments

Rows1,000 rows10,000 rows100,000 rows1,000,000 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: freight-route-performance-analytics

What should your data show?

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Sample rows

What the 200 rows show

Columns

Use it for

A logistics dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Logistics

What should your data show?