Journey Destination Endpoint Determination via Stop Classification
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Solution Overview
Problem
Existing navigation systems require users to manually input destinations and fail to automatically delete obsolete data, leading to storage issues and inefficient journey planning.
Innovation Solution
A system and method that uses a communication module, stop classification module, and endpoint establishment module to determine journey destinations based on GPS data and sensor information, automatically classifying stops and establishing endpoints without user input, while also deleting obsolete data to manage storage space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the navigation system stores all journey data including obsolete destinations, then the system has complete historical records for analysis, but the storage space is depleted and system performance deteriorates
Solution Approach 1:
The system performs preliminary classification of journey data into current and obsolete categories using machine learning models before deletion occurs. This allows the system to maintain complete historical records for analysis purposes while proactively identifying and removing obsolete data that would consume storage space, thus resolving the contradiction between maintaining complete records and preserving storage capacity.
Solution Approach 2:
The system selectively discards obsolete journey data (destinations not visited in a predetermined time period) while recovering and maintaining important historical patterns through machine learning models. The ML models learn from historical data and continue to provide accurate journey predictions even after raw obsolete data is removed, effectively recovering the useful information while discarding the space-consuming redundant data.
2Device complexity
If the system requires manual user input for destination, then the navigation system maintains simple processing logic, but the ease of operation decreases and productivity is reduced
Solution Approach 1:
The navigation system performs self-service by automatically determining journey destinations using machine learning models that analyze sensor data, GPS information, and historical journey patterns. The system classifies stops and identifies destinations without requiring manual user input, thereby improving ease of operation while the added complexity is managed through automated algorithms rather than user actions.
Solution Approach 2:
The system uses feedback from sensor data, GPS tracking, and historical journey information to continuously improve destination prediction accuracy. The machine learning models learn from user behavior patterns and provide increasingly accurate automatic destination suggestions, reducing the need for manual input while maintaining or improving operational simplicity.
3Productivity
If the system automatically classifies stops and establishes endpoints, then the productivity and ease of operation improve, but the device complexity increases
Solution Approach 1:
The system segments the journey analysis process into distinct functional modules: sensor data acquisition, GPS data processing, stop classification, endpoint establishment, and machine learning prediction. This segmentation allows each module to be optimized independently and managed separately, improving overall productivity while making the complex system architecture more maintainable and understandable.
Solution Approach 2:
The machine learning models serve multiple functions simultaneously: they classify stops, establish journey endpoints, predict destinations, and learn from historical patterns. This multi-functionality reduces the need for separate specialized components, improving productivity while managing system complexity through versatile algorithms that handle multiple tasks.
Data Source
AI summary
A system and method for establishing a journey destination endpoint is disclosed. The system comprises a communication module, a stop classification module and an endpoint establishment module. The communication module receives a stream of data including a first data element and a second data element from a global positioning system. The communication module receives a set of sensor data from one or more sensors. The stop classification module detects a stop for a traveling vehicle based at least in part on the stream of data and the set of sensor data. The stop classification module applies one or more metric criteria to the first data element and the second data element to determine a type of the stop. The endpoint establishment module establishes a journey destination endpoint based on the type of the stop. The endpoint establishment module associates the journey destination endpoint with retrieval identification data.