Omnitracs, LLC

Lauren K Domnick

Patrick M Ritto

Jeffrey Gilcrease

Budi Tan

Dean Croke

Pratyush Verma

Andrew Old

John A Old

The described features of the present disclosure generally relate to one or more improved systems for analyzing the electronic information associated with driving activities (e.g., driver log information) obtained from the one or more mobile computing platforms (ELDs) associated with one or more vehicles to identify a likelihood of a driver resigning or deserting his or her position. Accordingly, features of the present disclosure may identify 'at-risk' drivers for the fleet operators to trigger remedial measures to prevent such adverse event (e.g., driver quitting).

Driver log retention system

Aspects of notification of a predicted driving event include receiving driver log information associated with one or more drivers respectively corresponding to one or more vehicles, and aggregating the driver log information for a predetermined time period. Further, the aspects include extracting derivations from the aggregated driver log information in accordance with one or more derivation rules associated with an event prediction model operable to predict an adverse driving event. Also, the aspects include applying the event prediction model to the derivations to generate an adverse driver event prediction for each of the one or more drivers. Additionally, the aspects include transmitting a notification message including one or more of the adverse driver event predictions.

Driver log analytics system

Methods, system, and devices for improving pre-modeling processing of data for use in predictive modeling. A computing device may be configured to perform operations for processing client data using metadata describing various client-specific parameters. Received client data may be loaded within stage tables based on the client-specific parameters, such flat file locations. The computing device may perform various validation checks on the loaded data, such as checks to identify duplication data records associated with client data received at different times, duplication data records occurring within an overlap period, and deleted records. In an embodiment, the computing device may perform additional checks to validate data records that include changing status information, such as marital status data of an employee. The computing device may use processed client data to generate weekly aggregated tables based on custom aggregation timeframes. In an embodiment, the computing device may archive aggregated data for subsequent use.

Flexible time-based aggregated derivations for advanced analytics

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Muskego, WI, United States of America

Lauren K Domnick