Personalized Navigation Routing Using Real-Time Traffic Data
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Solution Overview
Problem
Current methods for commuters to navigate through traffic-congested areas are inefficient, relying on trial-and-error and lack real-time, personalized routing solutions that account for varying traffic conditions.
Innovation Solution
A computer-implemented method and system that gathers past location data from GPS-enabled devices to determine travel objectives, generates optimized routes using real-time traffic data, and provides personalized navigation information to users, incorporating their travel patterns and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If trial-and-error routing methods are used, then commuters can find alternative routes, but the process is inefficient and time-consuming
Solution Approach 1:
The system performs preliminary actions by gathering location indicators and determining expected navigation points in advance, before the user actually needs routing information. This allows the routing system to be ready with pre-analyzed data, eliminating the trial-and-error process and reducing the time users spend finding routes.
Solution Approach 2:
The system uses feedback mechanisms by continuously monitoring real-time traffic data and comparing it against historical patterns. This feedback loop enables the system to dynamically adjust and optimize route recommendations, providing efficient routing solutions without requiring users to manually test multiple routes.
2Measurement precision
If real-time traffic data is collected and analyzed, then routing accuracy is improved, but system complexity increases
Solution Approach 1:
The system applies universality by using a multi-functional platform that handles diverse data sources (GPS location indicators, traffic reports, road-side signs, wireless services) through a unified architecture. This allows the system to process various types of transportation flow data without proportionally increasing complexity, as the same core infrastructure handles multiple data collection and analysis functions.
3Loss of time
If personalized routing based on user patterns is implemented, then travel time is reduced, but data processing requirements increase
Solution Approach 1:
The system extracts only the essential and relevant features from large volumes of location indicator data, rather than processing every single data point. By identifying and extracting key patterns in user travel behavior and expected navigation points, the system reduces the effective data processing volume while still achieving personalized routing that minimizes travel time.
Data Source
AI summary
A computer-implemented method of providing personalized route information involves gathering a plurality of past location indicators over time for a wireless client device, determining a future driving objective using the plurality of previously-gathered location indicators, obtaining real-time traffic data for an area proximate to the determined driving objective, and generating a suggested route for the driving objective using the near real-time traffic data.