Education
19 columns
62 rows
8 formats

School Bus Incident IoT Log

This dataset provides comprehensive, real-time logs of school bus incidents detected by edge IoT sensors, including event type, location, severity, and contextual information such as bus, route, and driver. It enables K-12 transportation teams to monitor fleet safety, ensure compliance, and respond rapidly to critical events, supporting data-driven improvements in student transportation operations.

last updated 1 Jan 2026
by GoMask

The brief that made it

Real-time fleet safety monitoring and incident response

Sample rows

preview · 8 of 62 rows · all 19 columns

incident_idstring	event_severitystring	speed_kphfloat	video_availableboolean	resolved_by_user_idstring	bus_idstring	route_idstring	school_district_idstring	event_timestampdatetime	event_typestring	sensor_idstring	latitudefloat	longitudefloat	student_countinteger	driver_idstring	audio_availableboolean	incident_descriptionstring	resolvedboolean	resolution_timestampdatetime
A7C3F0D2	info	0	true	USR-JSMITH	BUS-0192	RT-102A	SD-ALPHA	2024-05-01T07:15:22Z	door_open	SNS-0015	42.3721	-71.1132	23	DRV-104	false	Normal pickup. Door opened at school.	true	2024-05-01T07:30:01Z
B2E1-9F6D	info	0	false	USR-ABROWN	BUS-0025	RT-210B	SD-BETA	2024-05-01T14:55:11Z	idle	SNS-0099	42.4058	-71.0284	15	DRV-097	false	Bus idling at final stop.	true	2024-05-01T15:05:14Z
7A4B-11C8	warning	32.4	true	blank	BUS-0037	RT-318E	SD-GAMMA	2024-05-02T08:22:47Z	hard_brake	SNS-0202	42.329	-71.0497	18	DRV-083	true	Hard brake to avoid cyclist.	false	blank
F6C1B8A2	critical	54.9	true	blank	BUS-0113	RT-102A	SD-ALPHA	2024-05-01T07:18:39Z	speeding	SNS-0134	42.3715	-71.1148	20	DRV-104	true	Bus exceeded speed limit in residential zone.	false	blank
1C-8F7B-DA	info	0	false	USR-NSCOTT	BUS-0056	RT-154C	SD-DELTA	2024-05-01T16:04:50Z	door_close	SNS-0045	42.3603	-71.0588	6	DRV-119	false	Door closed after student dropoff.	true	2024-05-01T16:10:22Z
E9983F4B	info	17.2	false	USR-JSMITH	BUS-0192	RT-102A	SD-ALPHA	2024-05-02T07:11:12Z	geofence_entry	SNS-0015	42.3744	-71.1198	25	DRV-104	false	Bus entered school geofence.	true	2024-05-02T07:19:00Z
D1F6E5C3	critical	12.1	true	blank	BUS-0245	RT-322B	SD-BETA	2024-05-03T13:44:33Z	collision_detected	SNS-0213	42.4045	-71.0623	30	DRV-121	true	Minor collision with parked car.	false	blank
4B3A-7E9F	warning	0	true	USR-JSMITH	BUS-0113	RT-102A	SD-ALPHA	2024-05-02T07:25:11Z	stop_arm_deployed	SNS-0134	42.3727	-71.1169	21	DRV-104	false	Stop arm deployed for student crossing.	true	2024-05-02T07:29:01Z
A2EE9D6F	info	29.8	false	USR-KLEE	BUS-0037	RT-318E	SD-GAMMA	2024-05-02T08:29:01Z	geofence_exit	SNS-0202	42.33	-71.0504	18	DRV-083	false	Bus left school geofence.	true	2024-05-02T08:35:12Z
C8D7E0B4	info	0	false	USR-NSCOTT	BUS-0056	RT-154C	SD-DELTA	2024-05-01T15:59:12Z	door_open	SNS-0045	42.3606	-71.0592	7	DRV-119	false	Door opened for wheelchair access.	true	2024-05-01T16:12:51Z
7B1B6A2F	info	0	false	USR-ABROWN	BUS-0025	RT-210B	SD-BETA	2024-05-01T15:04:00Z	idle	SNS-0099	42.4061	-71.0289	15	DRV-097	false	Bus idling at end of route.	true	2024-05-01T15:10:17Z
53E0-19DA	warning	47.5	true	blank	BUS-0113	RT-102A	SD-ALPHA	2024-05-02T07:35:28Z	speeding	SNS-0134	42.3734	-71.1175	17	DRV-104	false	Speed above recommended limit.	false	blank
E4A9B791	warning	31.2	false	USR-JSMITH	BUS-0192	RT-102A	SD-ALPHA	2024-05-01T07:40:35Z	hard_acceleration	SNS-0015	42.3728	-71.1138	24	DRV-104	true	Sudden acceleration near intersection.	true	2024-05-01T07:43:00Z
F8B3-2D7E	info	8.7	false	blank	BUS-0245	RT-322B	SD-BETA	2024-05-03T13:51:20Z	other	SNS-0213	42.4052	-71.061	29	DRV-121	false	Unusual sensor reading at bus stop.	false	blank
C7F1-2E8A	info	0	false	USR-KLEE	BUS-0037	RT-318E	SD-GAMMA	2024-05-03T08:17:09Z	door_close	SNS-0202	42.3285	-71.0489	18	DRV-083	true	Door closed after all students boarded.	true	2024-05-03T08:25:24Z
A0D5-3C92	warning	22.5	true	blank	BUS-0056	RT-154C	SD-DELTA	2024-05-03T16:19:34Z	hard_brake	SNS-0045	42.361	-71.0601	6	DRV-119	true	Hard brake after vehicle cut in lane.	false	blank
B9E7-8A5F	info	18.1	false	USR-JSMITH	BUS-0192	RT-102A	SD-ALPHA	2024-05-03T07:11:59Z	geofence_entry	SNS-0015	42.374	-71.1193	25	DRV-104	false	Bus entered geofence.	true	2024-05-03T07:14:00Z
3A2F-4E7B	info	0	false	USR-ABROWN	BUS-0245	RT-322B	SD-BETA	2024-05-03T13:59:42Z	door_open	SNS-0213	42.4057	-71.0617	30	DRV-121	false	Door opened at final stop.	true	2024-05-03T14:08:23Z
F3E8B5A4	info	0	false	USR-JSMITH	BUS-0113	RT-102A	SD-ALPHA	2024-05-04T07:22:14Z	idle	SNS-0134	42.3711	-71.115	16	DRV-104	false	Bus idle during pickup.	true	2024-05-04T07:28:39Z
5B1C-0A7E	warning	34.7	true	blank	BUS-0037	RT-318E	SD-GAMMA	2024-05-04T08:10:11Z	hard_acceleration	SNS-0202	42.3297	-71.0495	20	DRV-083	false	Rapid acceleration leaving stop.	false	blank

What the 62 rows show

from the 62-row sample

Info (event severity) stands out: mean speed_kph is 5.7, against 31.2 for the rest.

45%video_available = true
6.4median speed_kph
11event types
14school districts
15routes
15drivers

Mean speed_kph by event_severity62 rows

speed_kph62 rows, in bands of 10

Median 6.4, from 0.0 to 75.5.

resolved_by_user_id36 rows with a value · 26 left blank

USR-JSMITH8
USR-ABROWN4
USR-KLEE4
USR-7814
USR-NSCOTT3
USR-8033
USR-9083
USR-8122
USR-9451
USR-7101

19 columns by typefrom the column list below

string 10
integer 1
float 3
datetime 2
boolean 3

Columns

19 columns in four groups

blueprint · 19 columns
column	type	description	example
Text 10 columns
`incident_id`	string	Unique identifier for each bus incident log entryunique	A7C3F0D2
`bus_id`	string	Unique identifier for the school bus involved in the incident	BUS-0192
`route_id`	string	Identifier for the bus route associated with the incidentoptional	RT-102A
`school_district_id`	string	Identifier for the school district operating the busoptional	SD-ALPHA
`event_type`	string	Type of incident or event detected (e.g., hard_brake, door_open, speeding, idle, geofence_exit, etc.)11 values	door_open
`event_severity`	string	Severity level of the incident (e.g., info, warning, critical)info · warning · critical · optional	info
`sensor_id`	string	Unique identifier for the IoT sensor that captured the event	SNS-0015
`driver_id`	string	Identifier for the bus driver at the time of the incidentoptional	DRV-104
`incident_description`	string	Free-text description or notes about the incidentoptional	Bus entered geofence.
`resolved_by_user_id`	string	Identifier for the user or staff member who resolved the incidentoptional	USR-JSMITH
Numbers 4 columns
`latitude`	float	Latitude coordinate where the incident occurred-90 to 90	42.3721
`longitude`	float	Longitude coordinate where the incident occurred-180 to 180	-71.1132
`speed_kph`	float	Bus speed in kilometers per hour at the time of the incident0 or more · optional	0
`student_count`	integer	Number of students on board at the time of the incident, if available0 or more · optional	23
Dates and times 2 columns
`event_timestamp`	datetime	Date and time when the incident was detected by the IoT sensor	2024-05-01T07:15:22Z
`resolution_timestamp`	datetime	Date and time when the incident was marked as resolvedoptional	2024-05-01T07:30:01Z
True or false 3 columns
`video_available`	boolean	Indicates if video footage is available for the incidentoptional	true
`audio_available`	boolean	Indicates if audio recording is available for the incidentoptional	false
`resolved`	boolean	Indicates if the incident has been reviewed and resolvedoptional	true

Use it for

An education dashboard
The video_available rate, speed_kph by event_severity and a breakdown of resolved_by_user_id. Excel, Power BI or Tableau.
Why do the 33 info rows have a mean speed_kph of 5.7?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Incidents62
A7C3F0D20info
B2E1-9F6D0info
7A4B-11C832.4warning
A software demo
Believable incidents with bus_id, route_id and school_district_id to fill a screen in front of a buyer.

This dataset62 rows19 columns

Yours1,000 rows19 columns

blueprint · school-bus-incident-iot-log

Behind this dataset

Same schema. As many rows as you need.

These 62 rows came out of a blueprint — 19 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 log entry must contain timestamp, sensor ID, and bus route identifier.
Incident types must be categorized (e.g., door left open, speed anomaly, unauthorized stop).
Severity level assigned based on sensor readings and predefined thresholds.
Compliance status (resolved/unresolved) must be logged within 24 hours.
GPS coordinates are required for all incident events.

Rows1,000 rows10,000 rows100,000 rows1,000,000 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: school-bus-incident-iot-log

What should your data show?

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Sample rows

What the 62 rows show

Columns

Use it for

An education dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Education

What should your data show?