Dynamic Transit Route Generation From Telematics Travel Patterns
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Solution Overview
Problem
Current computing devices fail to utilize telematics data for optimizing bus routes and carpooling, leading to inefficiencies and discouragement of transit options that could reduce congestion, collision danger, and carbon pollution.
Innovation Solution
A transit analytics computing device generates dynamic transit routes based on telematics data, using machine learning and AI to build user models, predict travel patterns, and optimize routes for efficiency, time, and environmental impact, while providing real-time notifications and updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If bus routes are designed and altered manually based on costly studies, then route planning can be implemented, but the routes rapidly become obsolete due to ever changing commuter needs and cannot achieve optimized accessibility or ridership
Solution Approach 1:
The patent implements dynamic routing by continuously collecting telematics data from mobile devices and connected vehicles, then using machine learning models to generate real-time route adjustments. This transforms static manually-planned routes into dynamic routes that automatically adapt to changing commuter patterns, resolving the contradiction between route stability and adaptability.
Solution Approach 2:
The system establishes a feedback loop where telematics data from users is continuously collected, analyzed through transit models, and used to generate updated route recommendations. This closed-loop feedback mechanism ensures routes remain current with commuter needs, preventing obsolescence while maintaining adaptability.
2Ease of operation
If carpool routes are formed on an ad hoc basis between individuals who already know each other, then carpooling can occur, but potential riders are left out and routes are not optimized for efficiency
Solution Approach 1:
The patent creates a universal carpooling platform that serves multiple functions: matching existing carpool groups, identifying potential new riders through telematics analysis, and optimizing routes for overall efficiency. This multi-functional approach maintains the ease of ad hoc formation while eliminating the limitations of exclusive group-based routing.
Solution Approach 2:
The system enables self-service carpool matching by automatically analyzing telematics data to identify compatible riders and routes without requiring manual coordination. The machine learning models autonomously optimize carpool formations and routing, improving efficiency while maintaining operational simplicity for users.
3Productivity
If telematics data is collected from mobile devices and connected vehicles, then real-time transit optimization is enabled, but the complexity of processing and analyzing this data increases
Solution Approach 1:
The patent introduces an intermediary computing device that acts as a mediator between data collection and route generation. This intermediary processes telematics data through machine learning models and transit models, transforming raw complex data into simplified route recommendations. This intermediary layer manages processing complexity while enabling real-time optimization productivity.
Data Source
AI summary
A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: building, using a machine learning algorithm, a transit model based at least in part upon telematics data associated with a user; generating, based at least in part upon the transit model, a dynamic transit route for the user; calculating, based at least in part upon the telematics data associated with the user, a potential benefit comprising an amount of fuel cost savings for the user, reduced travel time for the user, insurance savings, or environmental pollution reduction for the user; generating a notification comprising the dynamic transit route and the potential benefit; and transmitting the notification to a mobile device of the user. Other embodiments are described.