Geotab Incorporated

Robert Bradley

Daniel Jacob Lewis

Terence Michael Branch

Chien An Liu

Jean Pilon-Bignell

Pooria Poorsarvi Tehrani

Shweta Pravinchandra Shah

Robert Spencer Hockin

Willem Petersen

Javed Siddique

Xiaochen Zhang

Shadi Mahdiani

The present disclosure relates to systems and methods for retrieving telematics data. A method for retrieving telematics data may comprise operating at least one processor to: provide a plurality of databases, each database storing at least telematics data originating from a plurality of telematics devices installed in a plurality of vehicles; receive a natural language request from a user; generate, using a large language model (LLM) that does not have access to the plurality of databases, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the database by inputting into the LLM at least: a contextual prompt, and the natural language request; execute the executable query; and return at least the portion of the telematics data to the user.

Systems and methods for retrieving telematics data

Methods, systems, and devices for data capture instructions for asset tracking are provided. An example method for capturing data involves obtaining raw data from a data source onboard an asset and monitoring the raw data for satisfaction of a simplified data capture trigger. When the simplified data capture trigger is satisfied, a dataset simplification algorithm is performed on the raw data to generate a simplified set of raw data, and the simplified set of raw data is logged. The method further involves monitoring the raw data for satisfaction of a rich data capture trigger. When the rich data capture trigger is satisfied, an unsimplified block of raw data is identified and logged for rich data analysis. The data is transmitted to a server. The unsimplified block of raw data contains raw data that is additional to the raw data contained in the simplified set of raw data.

Data capture instructions for asset tracking

A traffic analysis system analyzes location data from a plurality of vehicles to determine journeys made by the vehicles. Vehicles may make one or more rest stops during a journey. The traffic analysis system compares rest periods to journey criteria to determine whether a rest period delineates the end of a journey, or whether a rest period is still within the journey. In this way, a plurality of trips can be chained together into a journey to provide more accurate analysis of traffic patterns.

Methods for analyzing vehicle traffic between geographic regions

Systems for analyzing vehicle traffic between geographic regions

Disclosed are systems and methods relating to providing corridor metrics based on road network data and telematic data.

Method for providing corridor metrics for a corridor of a road network

System for providing corridor metrics for a corridor of a road network

Disclosed are systems and methods relating to providing intersection metrics based on road network data and telematic data.

System for determining traffic metrics of a road network

Methods, systems, and devices for data capture trigger configuration for asset tracking are provided. Another example method capturing raw data involves obtaining a rich data capture trigger that defines when a controller of an asset tracking device onboard an asset is to identify and log an unsimplified block of raw data in raw data on the asset tracking device for rich data analysis, transmitting data capture instructions to the asset tracking device that contains the rich data capture trigger, and receiving the simplified set of raw data and the unsimplified block of raw data from the asset tracking device.

Data capture trigger configuration for asset tracking

System for automatically classifying vehicle vocation and benchmarking vehicle performance relative to other vehicles having the same vocation classification, independent of vehicle fleet groupings, industry vehicle application groupings and vehicle type groupings, is disclosed. The system includes a vehicle vocation classifier in communication with a data management system to store historical vehicle data including recurring vehicle usage data, and assign one or more predicted vocations for each vehicle based on the recurring vehicle usage data using a machine learning technique. The system also includes a benchmarking management system for grouping the historical vehicle data for vehicles of same determined predicted vocation, determining therefrom benchmarking vehicles having better performance characteristics than other vehicles of the same determined predicted vocation, and benchmarking performance of the other predicted vocation vehicles relative to the benchmarking vehicles.

Vehicle vocation system

Disclosed are systems and methods for determining a geographical area occupied by an intersection.

Method and system for determining a geographical area occupied by an intersection

Disclosed are systems and methods relating to providing intersection metrics based on telematic data.

Method for determining traffic metrics of a road network

Systems for analyzing vehicle journeys

Automatic classifying vehicles by vocation and benchmarking vehicle performance relative to other vehicles of the same vocation are disclosed. Vehicles having a same vocation relative to each other and independent of vehicle fleet, industry vehicle application and vehicle type are benchmarked. Historical vehicle data that includes recurring vehicle usage data is stored. Using a learned trained classifier for each vehicle of the plurality of vehicles, a predicted vocation based on vehicle usage data may be obtained. A grouping based on the predicted vocations and the historical vehicle data is made. A determination from the benchmarked vehicles having better performance characteristics than other vehicles of the same predicted vocations is made. Performance of the other vehicles relative to benchmarking vehicles is made.

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Oakville, Canada

Robert Bradley