Bus Stop Detection via Acceleration Clustering
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Solution Overview
Problem
Users face inefficiency in querying bus information as they need to manually input start and destination bus stops, leading to long waiting times at bus stops.
Innovation Solution
A method and device that automatically determine the geographical location for a user to take a bus by detecting user movement, eliminating the need for manual input of bus stop locations, and providing accurate trip information based on clustering and neural network analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If real time bus query software is used to provide accurate bus location information, then waiting time at bus stop is reduced, but manual input of bus stop locations increases operation complexity
Solution Approach 1:
The system automatically detects user's current location using GPS and identifies the bus stop based on pre-stored bus stop location data, eliminating the need for manual input. The terminal self-services by autonomously querying bus location information based on the detected bus stop, thus resolving the contradiction between reducing waiting time and simplifying operation.
2Ease of operation
If automatic location detection and clustering is implemented to eliminate manual input, then ease of operation is improved, but device complexity increases
Solution Approach 1:
Bus stop location data is pre-stored in the terminal before actual use. When the user needs bus information, the system directly matches the detected current location with the pre-stored bus stop data using clustering algorithms, avoiding the need for complex real-time analysis and reducing computational complexity during operation.
3Device complexity
If manual input of bus stop locations is required, then device complexity is reduced, but productivity in querying bus information decreases
Solution Approach 1:
The manual mechanical input process is replaced with automatic electronic location detection using GPS and automated data processing through clustering algorithms and neural networks. This substitution eliminates manual typing operations and enables instant bus information retrieval, significantly improving query productivity while the system complexity is managed through efficient algorithm implementation.
Data Source
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AI summary
This application discloses a method and a device for determining a geographical location for a user to take bus. The method includes: obtaining, by a terminal, a first time period during which a user arrives from a first geographical location to a second geographical location; collecting, by the terminal in the first time period, accelerations of the user in a horizontal direction, and determining, based on an acceleration change of the user in the horizontal direction, a second time period from a moment at which the user departs from the first geographical location to a moment at which a bus begins to move after the user takes the bus; collecting, by the terminal, a plurality of geographical locations of the user in the second time period; and performing clustering, by the terminal, on the plurality of geographical locations to determine a geographical location whose collection quantity meets a condition, and using the geographical location whose collection quantity meets the condition as a geographical location at which the user takes the bus. Trip information determining efficiency of the user can be improved by using the method and the device of this application.