Local Hazard Incidents (LHI) Data Product
What is Local Hazard Incidents data?
Description
Local Hazard Incidents (LHI) data provides real-time insights into hazardous road conditions and incidents as detected by connected vehicles. This includes events such as accidents, broken-down vehicles, slippery road surfaces, and low visibility conditions.
The data is captured at high frequency and delivered with near real-time latency, making it well-suited for live traffic management, road safety monitoring, and dynamic routing applications. Each incident record also includes the last 2 vehicle positions prior to the event detection, providing additional spatial context around the hazard location.
Data Source
The data is sourced from millions of vehicles globally via our vetted partners including:
- Automotive OEMs; vehicles with embedded connectivity from the car manufacturer
Use Cases
Key applications include:
- Real-Time Traffic Management: Traffic control centres and road operators can monitor live hazard events to coordinate incident response, adjust signal timing, and inform dynamic message signs.
- Road Safety Analysis: Government agencies and road safety analysts can assess the frequency and distribution of hazard events to identify high-risk corridors and support targeted safety interventions.
- Dynamic Routing & Navigation: Navigation platform providers can integrate live hazard data to re-route drivers away from active incidents and adverse conditions in real time.
- Smart City & Mobility Analytics: Urban planners and SaaS analytics platforms can analyse hazard patterns over time to inform infrastructure improvements and emergency preparedness strategies.
Local Hazard Incidents Data Schema
Main Attributes
| Attribute | Type | Description |
|---|---|---|
| session_id | string | Unique session id for a vehicle |
| pos1_lat / pos2_lat / pos3_lat | long | Latitude of the vehicle at positions 1, 2, and 3 — capturing the vehicle’s trajectory before the hazard event |
| pos1_lon / pos2_lon / pos3_lon | long | Longitude of the vehicle at positions 1, 2, and 3 — capturing the vehicle’s trajectory before the hazard event |
| pos1_time / pos2_time / pos3_time | long | Unix timestamps (ms) corresponding to each of the three positional snapshots |
| pos1_alt / pos2_alt / pos3_alt | long | Altitude of the vehicle (in mm) at each of the three positional snapshots |
| pos1_yaw / pos2_yaw / pos3_yaw | long | Vehicle heading (yaw angle in degrees) at each of the three positional snapshots |
| veh1_speed / veh2_speed / veh3_speed | long | Vehicle speed (mm/s) at each of the three positional snapshots |
| hazard_type | string | Category of the detected hazard: BROKEN_DOWN_VEHICLE, SLIPPERY_ROAD, ACCIDENT, EXCEPTIONAL_CONDITION_LOW_VISIBILITY |
| hazard_timestamp | long | Unix timestamp (ms) at which the hazard was detected by the vehicle |
| hazard_detection_status | string | Status of the hazard detection at the time of reporting: NOT_EXPECTED_AND_DETECTED, EXPECTED_AND_NOT_DETECTED, NULL |
| e_call_status_timestamp | long | Unix timestamp (ms) of the eCall status event, where applicable (null if no eCall was triggered) |
| e_call_status_activation_type | string | Indicates whether the eCall was triggered automatically or manually: NONE, AUTOMATIC, MANUAL, NULL |
| visibility_timestamp | long | Unix timestamp (ms) at which the visibility condition was recorded |
| visibility_type | string | Visibility condition at the time of the hazard: LOW_FOG, LOW_HEAVY_RAIN, NULL |
| country_code | string | ISO 3166-1 alpha-2 country code of the location where the hazard was detected |
| date | date | Calendar date of the hazard event (UTC), used as the partition key |
Data Sample
A small sample is available at the link below for download, which can help you better understand the data schema.
MOBITO Local Hazard Incidents (LHI) Data Sample Schema
Local Hazard Incidents Data Specs
Coverage
Approximately 581K events across Europe.
Location Granularity
Each incident is mapped to an exact GPS location, expressed as latitude and longitude coordinates.
Historicity
Historical data availability as of September 2025.
Delivery Frequency
Data is delivered with near real-time latency of less than 3 minutes from the time of detection. The data can also be offered as a historical batch delivery for analysis of past hazard events.
Vehicle Types
- Passenger car
Data Anonymisation
A data anonymisation is applied on the data. Key features include:
- Session-based ID rotation: Vehicle identifiers are rotated per session, with an average session duration of 2.5 minutes, preventing long-term tracking of individual vehicles.
- Random gaps: Deliberate gaps are introduced into the data stream to further prevent trajectory reconstruction.
- Timestamp shift: Timestamps are randomly shifted within a 1-second window to reduce the precision of event timing at the individual vehicle level.
FAQs
What types of hazard events are included?
The dataset currently covers four event types: BROKEN_DOWN_VEHICLE, ACCIDENT, SLIPPERY_ROAD, and EXCEPTIONAL_CONDITION_LOW_VISIBILITY. The visibility is further classified by type, including LOW_FOG and LOW_HEAVY_RAIN.
How quickly is the data available after an event is detected?
LHI data is delivered with a latency of less than 3 minutes from the time of detection, making it suitable for real-time and near real-time applications.
Can I access a sample of this data product?
Yes! Mobito offers a free 3-day data sample for your preferred location. Send us a message and a Mobito expert will assist you in arranging the details.
For details on how to access the sample data, see Mobito Data Delivery Methods.