Dynamic Transit Routing Using Telematics and Behavior Prediction
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Solution Overview
Problem
Current computing devices fail to utilize telematics data to optimize 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 transit behavior, and optimize routes for efficiency, time, and environmental impact, with real-time notifications and autonomous implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If bus routes are designed and altered manually based on costly studies, then route planning can be performed, but the routes rapidly become obsolete and cannot achieve improved accessibility or optimized ridership
Solution Approach 1:
The patent implements dynamic bus routing that automatically adjusts routes in real-time based on changing commuter patterns, traffic conditions, and ridership data. The system continuously updates route parameters without manual intervention, making the routing system adaptive and responsive to current conditions rather than static and obsolete.
Solution Approach 2:
The system incorporates continuous feedback loops where telematics data from mobile devices and connected vehicles are collected, analyzed, and used to automatically adjust bus routes. This closed-loop system ensures routes are constantly optimized based on actual rider behavior and environmental conditions, preventing obsolescence.
2Ease of operation
If carpool routes are formed on an ad hoc basis between individuals who already know each other, then carpooling can be established, 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 riders based on compatibility, optimizing routes for efficiency, and dynamically adjusting to changing conditions. The system handles both established carpool groups and new riders uniformly, eliminating the limitation of ad hoc formation while maintaining ease of use through automated matching.
Solution Approach 2:
The system enables carpool participants to self-match and self-optimize routes based on their preferences and requirements. The automated algorithm handles the complex matching and optimization tasks, allowing users to benefit from efficient routing without manual coordination, thus improving both ease of operation and route efficiency.
3Loss of information
If telematics data is collected from mobile devices and connected vehicles, then real-time transit behavior can be analyzed, but the data must be processed and modeled to generate actionable transit predictions
Solution Approach 1:
The patent segments the complex data processing task into distinct components: data collection from multiple sources, data cleaning and validation, pattern recognition, model building, and prediction generation. This modular approach manages system complexity while ensuring comprehensive utilization of telematics data for accurate transit predictions.
Data Source
AI summary
A computing device comprising: obtaining telematics data generated by an autonomous vehicle; building, using a machine learning algorithm, a transit model based at least in part upon the telematics data; generating, based at least in part upon the transit model, a dynamic transit route; calculating a potential benefit comprising at least one of an amount of fuel cost savings, reduced travel time, insurance savings, or environmental pollution reduction when the dynamic route is used compared to a different route; transmitting a notification comprising the dynamic route and the potential benefit to a display or touchscreen of the autonomous vehicle; receiving, via the display screen or touchscreen, a selection input indicating acceptance or declination of the dynamic route; when the selection input indicates declination, modifying the route; and when the selection input indicates acceptance, instructing the autonomous vehicle to autonomously drive along the dynamic route.