Dynamic Location Clustering for Transportation Services
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Solution Overview
Problem
Current transportation service systems face increased processing overhead due to the growing number of transportation service requests and vehicles, leading to inefficient vehicle allocation, as they rely on static clustering of latitude-longitude coordinates which limits dynamic adaptability.
Innovation Solution
A method and system for location clustering that groups geographical locations into clusters based on distance and travel time thresholds, generating fully-connected maximal sub-graphs to optimize vehicle allocation, reducing processing overhead and eliminating hard boundaries on vehicle allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If latitude-longitude coordinates are clustered into multiple clusters, then processing overhead is reduced, but vehicle allocation flexibility is limited due to hard boundaries
Solution Approach 1:
The patent transforms static clustering into dynamic sub-graphs that can adapt to real-time conditions. Instead of fixed cluster boundaries, the system creates fully-connected maximal sub-graphs where nodes represent locations and edges represent connectivity based on distance and travel time thresholds. This dynamic structure allows vehicles to be allocated across traditional cluster boundaries when conditions permit, resolving the contradiction between processing efficiency and allocation flexibility.
Solution Approach 2:
The patent segments the service area into fully-connected maximal sub-graphs rather than traditional rigid clusters. Each sub-graph is a maximal connected component where every node is reachable from every other node within the sub-graph through edges satisfying the threshold parameters. This segmentation approach maintains computational efficiency while eliminating hard boundaries, as vehicles can serve any location within a sub-graph regardless of traditional cluster assignments.
2Quantity of substance
If the number of transportation service vehicles is increased, then service coverage is improved, but processing overhead increases
Solution Approach 1:
The patent merges multiple traditional clusters into fully-connected maximal sub-graphs by connecting clusters that satisfy distance and travel time thresholds. This merging reduces the total number of discrete processing units from individual clusters to fewer sub-graphs, thereby reducing processing overhead even as the number of vehicles increases. The sub-graph structure allows efficient batch processing while maintaining comprehensive service coverage.
3Productivity
If static clustering is used, then processing overhead is reduced, but dynamic adaptability is lost
Solution Approach 1:
The patent changes the parameters used for grouping locations from fixed geographic clusters to dynamic sub-graphs defined by distance and travel time thresholds. These parameters can be adjusted based on real-time conditions such as traffic patterns, vehicle availability, and demand intensity. The fully-connected maximal sub-graph structure maintains computational efficiency while allowing continuous adaptation to changing conditions, resolving the contradiction between processing overhead and dynamic adaptability.
Data Source
AI summary
A method and a system for location clustering for a transportation service are provided. A plurality of locations are clustered into a plurality of clusters, each having one or more locations of the plurality of locations. A graph is generated by connecting the plurality of clusters. A first cluster of the plurality of clusters is connected to one or more second clusters of the plurality of clusters that satisfy one or more threshold parameters. The graph is segmented into a plurality of fully-connected maximal sub-graphs based on one or more connections between the plurality of clusters. One or more fully-connected maximal sub-graphs of the plurality of fully-connected maximal sub-graphs have a set of common clusters. The plurality of fully-connected maximal sub-graphs are used for performing one or more transportation service operations of the transportation service.