Dynamic Vehicle Stop Location Clustering
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Solution Overview
Problem
Public transportation, such as shuttle buses, often face challenges in arriving at predetermined locations on time due to changing traffic conditions, and users may miss buses due to unfamiliarity with routes, leading to unsatisfactory stopping locations that do not meet user demands.
Innovation Solution
A method and device that determine a vehicle site location by obtaining user and central point location information, calculating distances, clustering users, updating central point locations, and determining a site based on user group weights, ensuring a reasonable and user-demand-satisfying stop location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a predetermined stopping location is used for shuttle buses, then the route planning is simple, but the stopping location cannot satisfy user demands and users may miss the bus due to unfamiliarity with roads
Solution Approach 1:
The stopping location is transformed from a fixed predetermined point to a dynamic location determined in real-time based on user positions. The system calculates optimal stopping locations by considering actual user locations, distances to central points, and clustering results, allowing the stopping location to adapt to different user distributions and road conditions dynamically.
Solution Approach 2:
The system enables users to automatically be assigned to groups and have their locations considered in the stopping location determination without requiring manual input or familiar knowledge of routes. Users simply need to provide their location information, and the system automatically processes the data to determine optimal stopping locations that serve their needs.
2Reliability
If the shuttle bus stops at a fixed predetermined location, then the operation is simple, but traffic condition changes may cause the bus to arrive late
Solution Approach 1:
The system performs preliminary calculations of distances between users and central points, and preliminary clustering of users into groups before determining the final stopping location. This preliminary processing allows the system to prepare location data and group assignments in advance, enabling more accurate and timely stopping location determination that accounts for current traffic conditions and user positions.
Solution Approach 2:
The system uses feedback from real-time user location data to adjust and determine optimal stopping locations. By continuously considering actual user positions and distances, the system can adapt stopping locations to current conditions, improving punctuality. The feedback loop involves calculating distances, clustering users, updating central point locations, and determining stopping locations based on these iterative results.
3Adaptability or versatility
If the stopping location does not consider user demands, then the operation is simple, but the stopping site cannot satisfy user demands
Solution Approach 1:
The system segments users into multiple groups based on their locations and distances to central points. This segmentation allows the system to consider different user needs and positions separately, determining optimal stopping locations for each group while maintaining overall system simplicity. The clustering process divides the user base into manageable segments that can be addressed with targeted location strategies.
Solution Approach 2:
The system changes the parameters used for determining stopping locations from fixed predetermined coordinates to dynamic parameters including real-time user positions, calculated distances, and group-based weights. By adjusting these parameters based on actual user distribution and demands, the system achieves higher adaptability and user satisfaction without requiring overly complex operations.
Data Source
AI summary
A method and a device for determining a vehicle site location are disclosed in the present disclosure. The method includes: obtaining location information of multiple users and location information of at least two central points; calculating a distance between each user and each of the at least two central points according to the location information of multiple users and the location information of at least two central points; clustering the multiple users into at least two groups according to the distance; updating the location information of the central point according to the location information of the users in each group; if the location information of the central point satisfies a preset condition, determining the vehicle site location according to the location information of the central points and the number of the users in each group.