Insurance
21 columns
200 rows
8 formats

Driver Behavior Risk Indicators

This dataset provides granular vehicle telemetry and driver action records, including speed, acceleration, location, and risk event indicators, enabling comprehensive analysis of driving behavior and risk profiling. Designed for insurers, telematics providers, and transportation safety analysts, it supports advanced modeling of driver risk and safety interventions.

opened 5 times
last updated 22 Jul 2025
by GoMask

The brief that made it

Driver risk assessment and insurance premium calculation

Sample rows

preview · 8 of 200 rows · all 21 columns

record_idstring	road_typestring	risk_scorefloat	over_speedingboolean	weather_conditionstring	driver_idstring	vehicle_idstring	trip_idstring	timestampdatetime	latitudefloat	longitudefloat	speed_kmhfloat	acceleration_m_s2float	brake_forcefloat	steering_angle_degfloat	seatbelt_fastenedboolean	phone_usageboolean	harsh_accelerationboolean	harsh_brakingboolean	harsh_corneringboolean	traffic_densitystring
rec-1001	urban	0.16	false	clear	drv-0001	veh-0001	trip-00010	2024-05-01T08:05:12Z	40.7128	-74.006	42.7	0.9	0.22	12.1	true	false	false	false	false	medium
rec-1002	urban	0.18	false	clear	drv-0002	veh-0001	trip-00010	2024-05-01T08:07:13Z	40.7132	-74.0055	48.3	1.2	0.18	10.7	true	false	false	false	false	medium
rec-1003	urban	0.51	false	rain	drv-0003	veh-0002	trip-00011	2024-05-01T10:10:01Z	34.0522	-118.2437	0	-2.3	0.96	0	false	false	false	true	false	medium
rec-1004	residential	0.33	false	fog	drv-0004	veh-0003	trip-00012	2024-05-01T11:30:44Z	51.5074	-0.1278	26.4	0.4	0.29	6.9	true	false	false	false	false	high
rec-1005	highway	0.79	true	rain	drv-0005	veh-0004	trip-00013	2024-05-01T12:12:12Z	41.8781	-87.6298	104.2	4.1	0.38	-8.2	false	false	true	false	false	low
rec-1006	urban	0.19	false	clear	drv-0006	veh-0005	trip-00014	2024-05-01T13:41:25Z	29.7604	-95.3698	58.5	1.1	0.15	18.7	true	false	false	false	false	medium
rec-1007	highway	0.95	true	windy	drv-0007	veh-0006	trip-00015	2024-05-01T14:29:58Z	48.8566	2.3522	97.5	5.6	0.42	19.1	false	true	true	false	true	medium
rec-1008	urban	0.17	false	clear	drv-0001	veh-0001	trip-00010	2024-05-01T15:05:30Z	40.7136	-74.0044	44.2	1.4	0.2	13.5	true	false	false	false	false	medium
rec-1009	urban	0.57	false	rain	drv-0008	veh-0007	trip-00016	2024-05-01T16:20:10Z	35.6895	139.6917	0	0	0.93	0	false	false	false	true	false	high
rec-1010	highway	0.87	true	clear	drv-0009	veh-0008	trip-00017	2024-05-01T16:56:33Z	39.9042	116.4074	79.1	2.3	0.47	-7.6	false	true	true	false	true	medium
rec-1011	residential	0.41	false	clear	drv-0005	veh-0009	trip-00018	2024-05-01T17:43:26Z	55.7558	37.6176	10.5	0.2	0.11	2.7	false	true	false	false	false	high
rec-1012	urban	0.18	false	clear	drv-0010	veh-0010	trip-00019	2024-05-01T18:01:44Z	37.7749	-122.4194	74.7	1.9	0.33	11.2	true	false	false	false	false	medium
rec-1013	highway	0.99	true	snow	drv-0011	veh-0011	trip-00020	2024-05-01T19:11:37Z	52.52	13.405	112.6	6.7	0.56	16	false	true	true	true	true	medium
rec-1014	urban	0.15	false	clear	drv-0012	veh-0012	trip-00021	2024-05-01T21:13:26Z	53.3498	-6.2603	33.8	1	0.24	7.9	true	false	false	false	false	medium
rec-1015	residential	0.34	false	fog	drv-0013	veh-0013	trip-00022	2024-05-01T22:24:19Z	45.4215	-75.6996	28.1	0.6	0.16	6	true	false	false	false	false	high
rec-1016	urban	0.44	false	other	drv-0014	veh-0014	trip-00023	2024-05-01T23:45:08Z	28.6139	77.209	0	-1.9	0.98	0	true	false	false	true	false	medium
rec-1017	urban	0.18	false	clear	drv-0001	veh-0001	trip-00024	2024-05-02T00:10:21Z	40.7139	-74.0032	61.8	1.8	0.28	14.3	true	false	false	false	false	medium
rec-1018	highway	0.87	true	windy	drv-0015	veh-0005	trip-00025	2024-05-02T01:22:35Z	41.3851	2.1734	101.4	3.8	0.41	-14.6	false	false	true	false	true	low
rec-1019	urban	0.17	false	clear	drv-0003	veh-0002	trip-00026	2024-05-02T02:14:05Z	34.0524	-118.2447	47.3	1.2	0.25	11.8	true	false	false	false	false	medium
rec-1020	residential	0.32	false	clear	drv-0004	veh-0003	trip-00027	2024-05-02T03:59:11Z	51.5094	-0.1187	19.7	0.3	0.14	4.8	true	false	false	false	false	high

What the 200 rows show

from the 200-row sample

Highway (road type) stands out: 47 of its 47 rows have over_speeding = true, against 12 of 153 for the rest.

30%over_speeding = true
0.44median risk_score
3traffic densities
40.0median speed_kmh
1.0median acceleration_m_s2
0.28median brake_force

Over speeding rate by road_typeover_speeding = true

risk_score200 rows, in bands of 0.1

Every row with risk_score of 0.6 or more has over_speeding = true. None below it.

weather_condition200 rows · 6 values

clear89
rain36
fog23
windy19
other19
snow14

21 columns by typefrom the column list below

string 7
float 7
datetime 1
boolean 6

Columns

21 columns in four groups

blueprint · 21 columns
column	type	description	example
Text 7 columns
`record_id`	string	Unique identifier for each telemetry and driver action recordunique	rec-1001
`driver_id`	string	Unique identifier for the driver	drv-0001
`vehicle_id`	string	Unique identifier for the vehicle	veh-0001
`trip_id`	string	Unique identifier for the trip/session	trip-00010
`weather_condition`	string	Weather condition at the time of record6 values · optional	clear
`road_type`	string	Type of road being driven onhighway · urban · rural · residential · other · optional	urban
`traffic_density`	string	Traffic density at the time of recordlow · medium · high · optional	medium
Numbers 7 columns
`latitude`	float	Latitude coordinate of the vehicle at the time of record-90 to 90	40.7128
`longitude`	float	Longitude coordinate of the vehicle at the time of record-180 to 180	-74.006
`speed_kmh`	float	Vehicle speed in kilometers per hour0 or more	42.7
`acceleration_m_s2`	float	Vehicle acceleration in meters per second squared	0.9
`brake_force`	float	Brake force applied (normalized value, 0.0-1.0)0 to 1	0.22
`steering_angle_deg`	float	Steering wheel angle in degrees-540 to 540	12.1
`risk_score`	float	Calculated risk score for this record (0.0-1.0, higher is riskier)0 to 1 · optional	0.16
Dates and times 1 column
`timestamp`	datetime	Date and time when the telemetry record was captured	2024-05-01T08:05:12Z
True or false 6 columns
`seatbelt_fastened`	boolean	Indicates if the driver's seatbelt was fastened	true
`phone_usage`	boolean	Indicates if the driver was using a phone during the record	false
`harsh_acceleration`	boolean	True if harsh acceleration event detected at this timestamp	false
`harsh_braking`	boolean	True if harsh braking event detected at this timestamp	false
`harsh_cornering`	boolean	True if harsh cornering event detected at this timestamp	false
`over_speeding`	boolean	True if the vehicle was over the speed limit at this timestamp	false

Use it for

An insurance dashboard
The over_speeding rate, risk_score by road_type and a breakdown of weather_condition. Excel, Power BI or Tableau.
Why do 59 of 200 rows have over_speeding = true?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Records200
rec-10010.16urban
rec-10050.79highway
rec-10070.95highway
A software demo
Believable records with driver_id, vehicle_id and trip_id to fill a screen in front of a buyer.

This dataset200 rows21 columns

Yours1,000 rows21 columns

blueprint · driver-behavior-risk-indicators

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 21 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

Speed readings sampled at 1-minute intervals
Includes weather and traffic conditions as features
Flag risky maneuvers with calculated risk scores
Vehicle types distributed across fleet profiles
Driver age and experience impact risk calculations

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: driver-behavior-risk-indicators

What should your data show?

No signup. No card.

Sample rows

What the 200 rows show

Columns

Use it for

An insurance dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Insurance

What should your data show?