Rideshare Risk Profile Generation via Telematics Data
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Solution Overview
Problem
Transport network companies (TNCs) face challenges in assessing and pricing insurance for rideshare drivers due to variability in driving habits and geographic locations, leading to inefficient flat rate insurance models that fail to account for individual differences.
Innovation Solution
A cloud-based vehicular telematics system that collects data from rideshare vehicles to generate unique risk profiles for drivers, including a telematics mobile app and server that determine operating states and calculate risk scores based on telematics and rideshare data, enabling personalized insurance pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a flat rate insurance model is used for all drivers in all locations, then the ease of operation and simplicity of insurance pricing is improved, but the measurement precision and accuracy of risk assessment deteriorates because it fails to account for individual driver differences and geographic variations
Solution Approach 1:
The patent applies local quality by creating location-specific risk profiles and pricing models that account for geographic variations in driving conditions, traffic patterns, and accident rates. Instead of using a uniform flat rate for all locations, the system generates customized risk assessments for each geographic area, allowing insurance pricing to reflect local-specific risk characteristics while maintaining operational simplicity through automated profile generation.
2Measurement precision
If individual driver risk profiles are generated using telematics data, then the measurement precision and accuracy of risk assessment is improved, but the device complexity and system complexity increases due to data collection, processing, and analysis requirements
Solution Approach 1:
The patent applies segmentation by dividing the complex risk assessment system into modular components: telematics data collection module, data processing module, risk profile generation module, and insurance pricing module. Each segment handles specific tasks independently, making the overall complex system more manageable and maintainable while enabling accurate individual driver risk assessment through coordinated operation of these segmented functions.
Solution Approach 2:
The patent uses an intermediary risk profile database that stores processed telematics data and generated risk profiles. This intermediary layer simplifies the system architecture by buffering between raw data collection and final pricing decisions, allowing the system to handle complexity through structured data storage and retrieval mechanisms rather than direct complex processing at every stage.
3Reliability
If variable insurance pricing is implemented based on driver behavior and location, then the reliability and accuracy of insurance pricing is improved, but the loss of information and data processing requirements increase
Solution Approach 1:
The patent applies extraction by selectively processing and extracting only the most relevant telematics data points needed for risk assessment, such as driving behavior metrics, location information, and trip patterns. Rather than processing all available vehicle data, the system extracts and focuses on specific information elements that directly impact risk pricing, reducing unnecessary data processing while maintaining pricing accuracy and reliability.
Data Source
AI summary
Cloud-based and other vehicular telematics systems and methods are described for automatically generating rideshare-based risk profiles of rideshare drivers of a transport network company (TNC) platform. The systems and methods comprise receiving telematics data originating from sensor(s) traveling with a rideshare vehicle during an operating segment of the rideshare vehicle and rideshare data originating from a rideshare app configured to execute on a telematics device during one or more portions of the operating segment. The rideshare data indicates a rideshare app mode for each portion of the operating segment. The systems and methods include determining, based on the telematics data, operating state(s) of the rideshare vehicle during the operating segment of the rideshare vehicle, and generating, based on the telematics data and the rideshare data, a rideshare-based risk profile and driver score of a driver.