Transportation
23 columns
120 rows
8 formats

Fleet Route Optimization Telematics

This dataset offers comprehensive telematics records for transportation fleets, including trip-level details on routes, fuel consumption, idle times, emissions, and sensor-detected anomalies. It is ideal for optimizing fleet operations, supporting sustainability initiatives, and enabling advanced AI/ML analytics for smart city logistics and cost reduction.

last updated 27 Nov 2025
by GoMask

The brief that made it

AI/ML-powered route optimization for fleet efficiency

Sample rows

preview · 8 of 120 rows · all 23 columns

vehicle_idstring	cargo_typestring	average_speed_kphfloat	anomaly_detectedboolean	statestring	route_idstring	trip_idstring	driver_idstring	start_timedatetime	end_timedatetime	start_latitudefloat	start_longitudefloat	end_latitudefloat	end_longitudefloat	distance_kmfloat	fuel_consumed_litersfloat	idle_time_minutesfloat	max_speed_kphfloat	anomaly_typestring	emissions_kg_co2float	cargo_weight_kgfloat	citystring	countrystring
TRK-001	furniture	28.2	false	NY	R101	T0001	D001	2024-06-01T07:05:00Z	2024-06-01T08:02:00Z	40.7128	-74.006	40.7306	-73.9352	23.1	4.7	8.2	54	none	10.1	300	New York	USA
TRK-002	food	18.3	true	CA	R102	T0002	D002	2024-06-01T10:15:00Z	2024-06-01T11:06:00Z	34.0522	-118.2437	34.0407	-118.2468	14.2	2.9	4.4	39	engine	6.8	550	Los Angeles	USA
TRK-003	textiles	22.2	false	England	R103	T0003	D003	2024-06-02T15:52:00Z	2024-06-02T16:51:00Z	51.5074	-0.1278	51.5155	-0.1427	19.9	4.2	6.1	48	none	9	475	London	UK
TRK-004	medical supplies	18	true	Ile-de-France	R104	T0004	D004	2024-06-03T09:30:00Z	2024-06-03T10:28:00Z	48.8566	2.3522	48.8647	2.3488	12.5	2.5	3.2	36	fuel_leak	5.2	620	Paris	France
TRK-005	furniture	15.8	false	Berlin	R105	T0005	D005	2024-06-03T14:00:00Z	2024-06-03T14:44:00Z	52.52	13.405	52.54	13.41	10.7	2	2.6	33	none	4.1	285	Berlin	Germany
TRK-006	textiles	11.1	false	Moscow	R106	T0006	D006	2024-06-04T05:41:00Z	2024-06-04T06:27:00Z	55.7558	37.6173	55.77	37.62	8.9	1.7	2.7	29	none	3.4	120	Moscow	Russia
TRK-007	food	16.9	true	Tokyo	R107	T0007	D007	2024-06-04T12:45:00Z	2024-06-04T13:49:00Z	35.6895	139.6917	35.6804	139.769	15.6	2.7	6.7	34	brake	5.7	330	Tokyo	Japan
TRK-008	medical supplies	12.4	false	Shanghai	R108	T0008	D008	2024-06-05T08:23:00Z	2024-06-05T09:12:00Z	31.2304	121.4737	31.24	121.48	9.8	2.1	1.9	28	none	4	190	Shanghai	China
TRK-009	furniture	15.2	false	Delhi	R109	T0009	D009	2024-06-05T14:33:00Z	2024-06-05T15:20:00Z	28.6139	77.209	28.635	77.22	11.2	2.3	3.5	32	none	4.5	260	Delhi	India
TRK-010	textiles	10.6	false	Lazio	R110	T0010	D010	2024-06-06T12:03:00Z	2024-06-06T12:50:00Z	41.9028	12.4964	41.91	12.5	8.1	1.6	2.2	25	none	3.1	140	Rome	Italy
BUS-A21	passengers	15.5	false	NY	R201	T0011	D021	2024-06-01T06:30:00Z	2024-06-01T07:16:00Z	40.7128	-74.006	40.7306	-73.9352	11.7	3.9	6.1	33	none	7.9	blank	New York	USA
BUS-B12	passengers	18.2	false	CA	R202	T0012	D022	2024-06-01T13:20:00Z	2024-06-01T14:08:00Z	34.0522	-118.2437	34.0407	-118.2468	13.3	4.1	8.4	38	none	8.4	blank	Los Angeles	USA
BUS-C33	passengers	13.9	false	England	R203	T0013	D023	2024-06-02T17:11:00Z	2024-06-02T17:54:00Z	51.5074	-0.1278	51.5155	-0.1427	10.2	3.1	7.3	33	none	7.2	blank	London	UK
BUS-D44	passengers	16.7	true	Ile-de-France	R204	T0014	D024	2024-06-03T08:35:00Z	2024-06-03T09:26:00Z	48.8566	2.3522	48.8647	2.3488	14.1	4.2	10	41	brake	8.8	blank	Paris	France
BUS-E55	passengers	14.7	false	Berlin	R205	T0015	D025	2024-06-03T16:10:00Z	2024-06-03T17:03:00Z	52.52	13.405	52.54	13.41	12.8	3.8	5.8	36	none	7.5	blank	Berlin	Germany
BUS-F66	passengers	18.6	false	Moscow	R206	T0016	D026	2024-06-04T07:10:00Z	2024-06-04T08:01:00Z	55.7558	37.6173	55.77	37.62	15.4	4.6	8.9	39	none	9.2	blank	Moscow	Russia
BUS-G77	passengers	13.2	true	Tokyo	R207	T0017	D027	2024-06-04T19:19:00Z	2024-06-04T20:06:00Z	35.6895	139.6917	35.6804	139.769	11.9	3.5	6.5	35	temperature	7.6	blank	Tokyo	Japan
BUS-H88	passengers	12.2	false	Shanghai	R208	T0018	D028	2024-06-05T09:32:00Z	2024-06-05T10:17:00Z	31.2304	121.4737	31.24	121.48	10.8	3.2	5.1	30	none	7.3	blank	Shanghai	China
CAR-ABC	passengers	22.6	false	NY	R011	T0019	D051	2024-06-01T08:02:00Z	2024-06-01T08:41:00Z	40.7128	-74.006	40.7306	-73.9352	12.1	0	0.4	45	none	0	280	New York	USA
CAR-DEF	blank	21.2	false	CA	R012	T0020	D052	2024-06-01T16:20:00Z	2024-06-01T17:06:00Z	34.0522	-118.2437	34.0407	-118.2468	10.5	0	0.2	39	none	0	blank	Los Angeles	USA

What the 120 rows show

from the 120-row sample

Electronics (cargo type) stands out: mean average_speed_kph is 12.9, against 18.2 for the rest.

19%anomaly_detected = true
18.1median average_speed_kph
5anomaly types
20countries
32cities
12.8median distance_km

Mean average_speed_kph by cargo_type107 rows

average_speed_kph120 rows, in bands of 2.5

Median 18.1, from 8.7 to 31.9.

state120 rows · top 10 of 35 values

Tokyo12
CA11
NY8
Berlin8
Moscow7
England6
Île-de-France6
Lazio5
Lombardy5
Madrid5

23 columns by typefrom the column list below

string 9
float 11
datetime 2
boolean 1

Columns

23 columns in four groups

blueprint · 23 columns
column	type	description	example
Text 9 columns
`vehicle_id`	string	Unique identifier for each vehicle in the fleetunique	TRK-001
`route_id`	string	Unique identifier for the assigned route	R101
`trip_id`	string	Unique identifier for each trip instanceunique	T0001
`driver_id`	string	Unique identifier for the driver operating the vehicle	D001
`anomaly_type`	string	Type of anomaly detected (if any), e.g., 'engine', 'brake', 'temperature', 'none'engine · brake · temperature · fuel_leak · none · optional	none
`cargo_type`	string	Type of cargo transported during the trip8 types · optional	furniture
`city`	string	City where the trip primarily occurredoptional	New York
`state`	string	State or region where the trip primarily occurredoptional	NY
`country`	string	Country where the trip primarily occurredoptional	USA
Numbers 11 columns
`start_latitude`	float	Latitude coordinate where the trip started-90 to 90	40.7128
`start_longitude`	float	Longitude coordinate where the trip started-180 to 180	-74.006
`end_latitude`	float	Latitude coordinate where the trip ended-90 to 90	40.7306
`end_longitude`	float	Longitude coordinate where the trip ended-180 to 180	-73.9352
`distance_km`	float	Total distance covered during the trip in kilometers0 or more	23.1
`fuel_consumed_liters`	float	Total fuel consumed during the trip in liters0 or more	4.7
`idle_time_minutes`	float	Total time the vehicle spent idling during the trip, in minutes0 or more	8.2
`average_speed_kph`	float	Average speed of the vehicle during the trip in kilometers per hour0 or more	28.2
`max_speed_kph`	float	Maximum speed reached during the trip in kilometers per hour0 or more	54
`emissions_kg_co2`	float	Estimated CO2 emissions for the trip in kilograms0 or more · optional	10.1
`cargo_weight_kg`	float	Weight of cargo transported during the trip in kilograms0 or more · optional	300
Dates and times 2 columns
`start_time`	datetime	Timestamp when the trip started	2024-06-01T07:05:00Z
`end_time`	datetime	Timestamp when the trip ended	2024-06-01T08:02:00Z
True or false 1 column
`anomaly_detected`	boolean	Indicates if any sensor-detected anomaly occurred during the trip	false

Use it for

A transportation dashboard
The anomaly_detected rate, average_speed_kph by cargo_type and a breakdown of state. Excel, Power BI or Tableau.
Why do the 12 electronics rows have a mean average_speed_kph of 12.9?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Vehicles120
TRK-00128.2furniture
TRK-00218.3food
TRK-00418medical …
A software demo
Believable vehicles with route_id, trip_id and driver_id to fill a screen in front of a buyer.

This dataset120 rows23 columns

Yours1,000 rows23 columnscity: UK only

blueprint · fleet-route-optimization-telematics

Behind this dataset

Same schema. As many rows as you need.

These 120 rows came out of a blueprint — 23 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 completed fleet route trip
Include GPS trace summary, fuel usage, and idle duration
Sensor anomalies logged with timestamp and type
Electric and combustion vehicles differentiated
Weather impact noted per trip
Route efficiency score calculated for each journey

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: fleet-route-optimization-telematics

What should your data show?

No signup. No card.

Sample rows

What the 120 rows show

Columns

Use it for

A transportation dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Transportation

What should your data show?