Logistics
25 columns
200 rows
8 formats

Logistics Route Optimization Dataset

This dataset provides granular logistics route data, including provider information, origin and destination addresses, delivery times, costs, and optimization status. It enables detailed analysis of route efficiency, cost reduction strategies, and supply chain performance benchmarking for logistics providers and shippers.

opened 5 times
last updated 20 Aug 2025
by GoMask

The brief that made it

Route optimization and cost reduction analysis for logistics providers

Sample rows

preview · 8 of 200 rows · all 25 columns

route_idstring	vehicle_typestring	cargo_weight_kgfloat	optimizedboolean	origin_countrystring	provider_idstring	provider_namestring	origin_street_addressstring	origin_citystring	origin_statestring	origin_postal_codestring	destination_street_addressstring	destination_citystring	destination_statestring	destination_postal_codestring	destination_countrystring	distance_kmfloat	estimated_delivery_time_hoursfloat	actual_delivery_time_hoursfloat	delivery_datedate	route_cost_usdfloat	route_statusstring	number_of_stopsinteger	average_stop_time_minutesfloat	cargo_typestring
RT0001	bike	0.5	true	USA	LGP001	UrbanBike Delivery	123 Pine St	San Francisco	CA	94107	456 Market Ave	San Francisco	CA	94103	USA	4.2	0.45	0.4	2024-04-15	12.5	completed	0	blank	documents
RT0002	van	15	true	UK	LGP002	ExpressVan Logistics	78 Lake Road	London	Greater London	SW1A 1AA	22 Queen St	London	Greater London	EC2V 7JN	UK	9.8	0.95	blank	blank	23.7	planned	1	blank	meal kits
RT0003	truck	2000	false	USA	LGP003	Continental Trucking	200 Main St	Dallas	TX	75201	800 Elm St	Houston	TX	77002	USA	386.3	6.2	7	2024-04-13	790	delayed	2	44	furniture
RT0004	ship	32150	true	China	LGP004	Global Sea Cargo	10 Harbor Blvd	Shanghai	Shanghai	200120	88 Port Way	Rotterdam	South Holland	3011AA	Netherlands	10150.7	270	268	2024-03-28	12350	completed	1	120	steel machinery
RT0005	airplane	350	false	Germany	LGP005	FastAir Express	1 Aviation Dr	Frankfurt	Hesse	60547	99 Sky Rd	Chicago	IL	60666	USA	7312.4	13.5	blank	blank	8250	planned	0	blank	pharmaceuticals
RT0006	truck	800	true	USA	LGP003	Continental Trucking	120 Rose Ave	Austin	TX	78701	500 Oak Dr	San Antonio	TX	78205	USA	130.5	2.1	blank	blank	470	planned	3	blank	books
RT0007	train	9800	true	Germany	LGP006	EuroRail Cargo	125 Bahnstrasse	Berlin	Berlin	10178	72 Rue du Nord	Paris	Île-de-France	75010	France	878	8.1	7.7	2024-04-12	1100	completed	2	52	grain
RT0008	van	9	false	USA	LGP007	RoadRunner Vans	333 7th Ave	New York	NY	10001	950 Lexington Ave	New York	NY	10022	USA	6.3	0.67	0.71	2024-04-16	24.5	delayed	1	14	flowers
RT0009	ship	10500	false	Netherlands	LGP004	Global Sea Cargo	55 Dockside Rd	Rotterdam	South Holland	3013AL	120 Pier St	Liverpool	Merseyside	L3 4BX	UK	650.2	28.5	blank	blank	3700	in-progress	2	blank	grain
RT0010	bike	0.12	true	Netherlands	LGP008	QuickParcel Bike	44 Green Ln	Amsterdam	North Holland	1011AC	79 Waterweg	Amsterdam	North Holland	1013RT	Netherlands	2.5	0.32	0.31	2024-04-15	9.5	completed	0	blank	gift card
RT0011	truck	540	false	USA	LGP009	Pacific Truck Lines	789 Maple St	Seattle	WA	98104	321 Pine St	Portland	OR	97204	USA	277.6	4.3	blank	blank	610	planned	2	blank	books
RT0012	airplane	1100	true	Hong Kong	LGP010	SwiftAir Freight	88 Airport Rd	Hong Kong	HK	999077	150 Jet Ave	Singapore	SG	238859	Singapore	2554.2	5.7	5.5	2024-04-10	3200	completed	1	25	semiconductors
RT0013	train	2300	false	Czechia	LGP011	TransEuro Rail	17 Central Pl	Prague	Prague	11000	147 Bahnhofstr	Vienna	Vienna	1010	Austria	333.1	3.5	blank	blank	670	planned	3	blank	apparel
RT0014	van	8	true	UK	LGP012	Urban Courier Van	56 King St	Manchester	Greater Manchester	M2 4WU	91 Princess Ave	Manchester	Greater Manchester	M1 2HY	UK	7.8	0.87	0.82	2024-04-16	19.6	completed	1	15	food
RT0015	truck	9800	true	China	LGP013	Asia Road Freight	12 Silk Rd	Beijing	Beijing	100000	89 Lotus St	Guangzhou	Guangdong	510000	China	2218.4	25.3	blank	blank	2200	in-progress	4	blank	grain
RT0016	ship	41000	false	USA	LGP014	PortConnect Shipping	140 Ocean Dr	Los Angeles	CA	90045	202 Dock Ave	Tokyo	Tokyo	104-0061	Japan	8772.1	198.3	200.5	2024-03-31	11900	delayed	2	160	auto parts
RT0017	bike	0.8	true	France	LGP015	City Parcel Bike	31 Elm St	Paris	Île-de-France	75008	67 Rue St Denis	Paris	Île-de-France	75001	France	1.9	0.22	0.19	2024-04-17	7.8	completed	0	blank	documents
RT0018	truck	1600	true	South Korea	LGP016	EastAsia Trucking	20 Industrial Rd	Seoul	Seoul	04524	88 River St	Busan	Busan	48941	South Korea	325	5.2	blank	blank	690	planned	2	blank	automobile parts
RT0019	van	4.5	true	Canada	LGP017	MetroVan Couriers	555 9th Ave	Toronto	Ontario	M5V 2B7	100 King St	Toronto	Ontario	M5H 1J9	Canada	3.4	0.38	0.36	2024-04-18	11.2	completed	1	10	small parcels
RT0020	ship	8200	true	Norway	LGP018	Northern Ship Lines	17 Port Rd	Oslo	Oslo	0150	33 Dock St	Hamburg	Hamburg	20095	Germany	1152	40.5	39.9	2024-04-12	3750	completed	2	98	grain

What the 200 rows show

from the 200-row sample

Ship (vehicle type) stands out: mean cargo_weight_kg is 20,489, against 1,330 for the rest.

61%optimized = true
735.0median cargo_weight_kg
5route statuses
17cargo types
27destination countries
325.5median distance_km

Mean cargo_weight_kg by vehicle_type200 rows

cargo_weight_kg200 rows, in bands of 5,000

Median 735.0, from 0.04 to 42,000.

origin_country200 rows · top 10 of 30 values

USA55
France20
Germany18
UK15
Netherlands14
China11
Australia7
Singapore7
Japan7
Canada6

25 columns by typefrom the column list below

string 16
integer 1
float 6
date 1
boolean 1

Columns

25 columns in four groups

blueprint · 25 columns
column	type	description	example
Text 16 columns
`route_id`	string	Unique identifier for each route evaluated or takenunique	RT0001
`provider_id`	string	Unique identifier for the logistics provider	LGP001
`provider_name`	string	Name of the logistics provider	UrbanBike Delivery
`origin_street_address`	string	Street address of the route's origin location	123 Pine St
`origin_city`	string	City of the route's origin location	San Francisco
`origin_state`	string	State or province of the route's origin location	CA
`origin_postal_code`	string	Postal code of the route's origin location	94107
`origin_country`	string	Country of the route's origin location	USA
`destination_street_address`	string	Street address of the route's destination location	456 Market Ave
`destination_city`	string	City of the route's destination location	San Francisco
`destination_state`	string	State or province of the route's destination location	CA
`destination_postal_code`	string	Postal code of the route's destination location	94103
`destination_country`	string	Country of the route's destination location	USA
`vehicle_type`	string	Type of vehicle used for the route (e.g., truck, van, bike)8 values	bike
`route_status`	string	Current status of the route (e.g., planned, in-progress, completed, delayed, canceled)planned · in-progress · completed · delayed · canceled	completed
`cargo_type`	string	Type of cargo transported on the routeoptional	documents
Numbers 7 columns
`distance_km`	float	Total distance of the route in kilometers0 or more	4.2
`estimated_delivery_time_hours`	float	Estimated delivery time for the route in hours0 or more	0.45
`actual_delivery_time_hours`	float	Actual delivery time recorded for the route in hours0 or more · optional	0.4
`route_cost_usd`	float	Total cost incurred for the route in USD0 or more	12.5
`number_of_stops`	integer	Total number of stops along the route0 or more	0
`average_stop_time_minutes`	float	Average time spent at each stop in minutes0 or more · optional	44
`cargo_weight_kg`	float	Total weight of cargo transported in kilograms0 or more · optional	0.5
Dates and times 1 column
`delivery_date`	date	Date when the delivery was completedoptional	2024-04-15
True or false 1 column
`optimized`	boolean	Indicates if the route was selected as part of an optimization process	true

Use it for

A logistics dashboard
The optimized rate, cargo_weight_kg by vehicle_type and a breakdown of origin_country. Excel, Power BI or Tableau.
Why do the 32 ship rows have a mean cargo_weight_kg of 20,489?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Routes200
RT00010.5bike
RT000215van
RT00032000truck
A software demo
Believable routes with provider_id, provider_name and origin_street_address to fill a screen in front of a buyer.

This dataset200 rows25 columns

Yours1,000 rows25 columnsorigin_street_address: UK only

blueprint · logistics-route-optimization-dataset

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 25 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

List available routes by region
Track delivery time per route
Incorporate fuel consumption data
Flag routes with frequent delays

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: logistics-route-optimization-dataset

What should your data show?

No signup. No card.

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?