Navigation System Automatic Destination Selection
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Solution Overview
Problem
Existing navigation systems face challenges in providing accurate and real-time traffic information, leading to suboptimal route choices that waste fuel, cause delays, and increase congestion, due to outdated or inaccurate data.
Innovation Solution
A method and system that utilize a network of mobile devices with GPS capabilities to collect and share real-time traffic data, combining it with historical models to dynamically determine traffic conditions and provide personalized travel time estimates, while predicting destinations for ETA calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time traffic data collection using mobile devices is implemented, then accuracy of travel time estimates is improved, but device complexity increases
Solution Approach 1:
The system divides traffic data collection into multiple independent mobile device units, each contributing local data to the overall network. This segmentation allows accurate aggregate traffic information without requiring any single device to be overly complex
Solution Approach 2:
Mobile devices perform multiple functions: navigation, traffic data collection, historical pattern recording, and real-time condition reporting. This multi-functionality leverages existing device capabilities to achieve accurate travel time estimates without adding dedicated complex infrastructure
2Ease of operation
If personalized travel time estimates are provided, then user experience is improved, but data processing requirements increase
Solution Approach 1:
The system pre-collects historical traffic data and establishes baseline patterns during normal operation. This preliminary action allows rapid generation of personalized estimates without intensive real-time processing, improving user experience while managing power requirements
3Loss of energy
If dynamic route optimization is implemented, then fuel efficiency is improved, but calculation time increases
Solution Approach 1:
The system continuously monitors actual travel times and compares them with predicted times, using this feedback to dynamically adjust route recommendations. This feedback mechanism enables fuel-efficient routing by learning from real-world performance while adapting calculations to current conditions
Solution Approach 2:
The system transitions from static historical models to dynamic real-time optimization by incorporating live traffic conditions. This dynamic approach enables continuous route improvement based on current traffic patterns, enhancing fuel efficiency without requiring exhaustive recalculation
Data Source
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AI summary
When a user enters, initializes, or otherwise starts using a navigation function, such as a navigation function on a mobile phone or a stand-alone device, a destination is automatically selected for route generation and production of a navigation output comprising one or more of a route to the pre-defined destination and an Estimated Time of Arrival (ETA). The destination is selected based on current proximity to a location that has a pre-defined destination associated with it. Such pre-defined destination usually is associated with the location by the user. A time of day criteria can also be imposed, such as requiring that the time of day either by in the morning or afternoon, a work day, or a holiday, or the opposite. In one concrete example, locations proximate a work place can be associated with home as a pre-defined destination, and vice versa. A time of day criteria can be imposed, such that even if proximate work and it is in the morning, a route and ETA to home will not be generated.