Automated Routing System for Destination Uncertainty
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Solution Overview
Problem
Conventional GPS-based systems require users to manually input their destination, leading to uncertainty and a lack of relevant information for frequent or unknown locations, resulting in inaccurate route planning.
Innovation Solution
An automated routing system that performs probabilistic analysis to suggest opportunistic diversions and computes expected costs for uncertain destinations, using probabilistic modeling and user data to identify optimal waypoints, such as refueling stations or rest stops, while minimizing deviation from the primary destination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional GPS-based systems require manual destination input, then system simplicity is maintained, but destination uncertainty increases and relevant information is lost
Solution Approach 1:
The system automatically infers the user's destination by analyzing current location, historical data, and contextual information without requiring manual input. The routing system serves itself by autonomously determining destination probabilities and generating appropriate route suggestions, thereby preventing information loss while maintaining simplicity for the user.
Solution Approach 2:
The system performs preliminary destination inference and probability analysis before the user actually needs route information. By pre-computing destination likelihoods and preparing route options based on inferred destinations, the system ensures relevant information is ready before the user explicitly requests it, preventing information loss in advance.
2Measurement precision
If probabilistic analysis is performed for each unplanned waypoint, then route planning accuracy improves, but computational complexity increases
Solution Approach 1:
The system segments the route planning problem into discrete decision points where probabilistic analysis is performed only at specific unplanned waypoints rather than continuously throughout the entire route. This segmentation allows for high precision at critical decision points while limiting computational complexity to manageable segments of the overall journey.
Solution Approach 2:
The system performs probabilistic analysis selectively at partial locations (unplanned waypoints) rather than comprehensively at every possible point along the route. This partial action approach achieves sufficient route planning accuracy at critical decision points without the excessive computational burden of analyzing every possible location.
3Adaptability or versatility
If the system suggests opportunistic diversions to uncertain destinations, then user needs are better met, but deviation from primary destination increases
Solution Approach 1:
The system dynamically changes route parameters by suggesting opportunistic diversions only when destination uncertainty exists and when such diversions are likely to benefit the user. The diversion suggestions adjust route length and deviation based on probabilistic destination analysis, providing adaptability to user needs while minimizing unnecessary route extension through conditional parameter modification.
Data Source
AI summary
The subject disclosure is directed towards resolving an uncertain transportation context by suggesting one or more potential diversions. An automated routing system may generate routing information that includes map data as well as a location of a diversion having an expected cost in compliance with the uncertain transportation context. Such a diversion may be a waypoint that satisfies one or more user needs given an unknown/uncertain destination.