Updates in the latest Model Minimum Uniform Crash Criteria (MMUCC) provide guidance for states to improve crash data collection and traffic safety. While the Nevada Department of Public Safety recently announced that fatal crashes are on the decline, quality crash data can help make roadways safer and reduce deaths and serious injuries.
Using Data and Predictive Analysis to Save Lives
The World Health Organization (WHO) states that car crashes are responsible for 1.25 million deaths each year. To cut deaths by half, the WHO acknowledged the importance of big data and predictive analytics.
Computers can produce a predictive crash map from collected car crash data. This data includes information about where, when, and why accidents happen by looking at factors such as:
- Crash identifier and classification
- Date and time of the crash
- Location of the crash
- Manner of crash
- Source of information- civilian or law enforcement
- Condition of the roadway surface
When a computer analyzes this historical data, predicting what roads or highways are high-risk areas for accidents is possible. With this information, it is possible to improve emergency response times and alert law enforcement to patrol these areas or set up safety checkpoints.
How Is the Data Collected?
Car crash data is manually gathered at the scene of an accident. Big data has not yet reached most personal vehicles for data collection. However, there are commercial apps available that are in use monitoring the safety of driver employees. These apps collect information about the driver’s speed, braking and acceleration actions, seat belt usage, and after-hour vehicle use. This information can help reduce commercial truck accidents.
In the previous edition of the MMUCC, data elements were divided into categories such as information collected at the scene of an accident, other data sources, links to EMS, driver licensing, roadway, or hospital databases. The latest MMUCC streamlines data collection from multiple sources and provides more in-depth information. Enhanced information provides categories for crashes involving commercial vehicles, fatal crashes, and non-motorist crashes.
Looking to the Future with Autonomous Vehicles
Safety is a big concern with autonomous vehicles, with nearly 10 million self-driving cars on the road. These vehicles are outfitted with multiple sensors that continuously collect data, including road conditions, pedestrians’ movements, other vehicles on the road, and bicyclists. The data obtained is fed into machine learning, where it is analyzed in nanoseconds to cause the vehicle to react accordingly in a safe manner.
