Turning Big Data into Smart Data
At Azuga, we turn vehicles in use into a stream of data that can be used to enhance product performance, improve workforce productivity, understand traffic flows and routing, enable better risk management and more. Our Azuga One data platform, encompassing Intelligent Mobility and Fleet Safety Intelligence, supporting tens of thousands of commercial and consumer vehicles using our services and has real-world data from millions of trips and billions of data points.
Ingested into a state-of-the-art distributed cloud computing environment with advanced capabilities for large-scale batch and real-time processing as well as advanced statistical modeling and machine learning, Azuga One is the leading source of real-world commercial fleet data and insights for fleets safety, vehicle health and fleet efficiency.
Azuga Data Science—Where Fleet Data Meets Big Data
We’ve built a distributed and highly scalable cloud computing environment for large scale batch and real-time data processing. Integrated with advanced tools for machine learning and AI, this enables time series analysis and other approaches across incredibly large and varied data sets.
Data Science Example: Battery Health
Check engine lights, memory loss, stalling and anti-theft no-start problems all have a relationship to a weak vehicle battery. Though there have been advances in battery research, it remains a black box with unexplainable behaviors. Laboratory test data can only be useful to a certain extent since other real-world conditions have a significant impact on battery health. Azuga has developed longitudinal machine learning models along with vehicle usage data and weather to provide a deeper understanding of precursors to battery failures using real-world data.
Data Science Example: Driver Behavior Patterns
Understanding typical driving patterns of individual drivers from historical behavior has many useful applications in traffic safety, navigation, mobility and in influencing driver behavior. Knowledge of a driver’s expected route and likely destination are important parameters for delivering useful information during the drive and in assessing risk, delays and conditions. Using historically mined data at an individual level, Azuga has developed a model to predict likely destinations, trajectories, stop locations, speeding and braking events at different days of the week and times of day for individual drivers.
Data Science Example: Predictive Fleet Maintenance
A fleet’s bottom line is impacted by the way it’s vehicles are maintained. A proactive preventive maintenance program can help fleet managers keep repair costs to a minimum while lowering vehicle downtimes. Preventive maintenance is typically limited to scheduled maintenance specified by OEM’s. Using PID codes and DTC data combined with usage patterns, Azuga has created an innovative Vehicle Health Report that measures and reports the status of key parameters of each vehicle so that maintenance issues can be identified and corrected before they become failures.