Geo-spatial cluster analysis for traffic region boundary classification
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Solution Overview
Problem
Current geo-spatial analysis methods fail to effectively determine and manage drop-off/pick-up traffic regions, leading to inefficient traffic flow and misuse of city parking spaces, as they lack precise classification and pricing mechanisms based on real-time demand and location-specific factors.
Innovation Solution
A computer program product and system that uses geo-spatial cluster analysis to classify traffic regions based on drop-off/pick-up point densities, applying demand pricing through a cost map that adjusts prices dynamically based on time, location, and vehicle type, redirecting traffic to lower demand areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If geo-spatial analysis is used to analyze vehicle trajectories to determine traffic hot spots, then traffic activity information can be obtained, but precise classification and pricing mechanisms based on real-time demand and location-specific factors are lacking
Solution Approach 1:
The patent segments the urban area into multiple traffic regions based on geo-spatial cluster analysis of drop-off/pick-up points. Each region is further divided into boundaries with specific classifications (high demand, medium demand, low demand). This segmentation enables precise classification of traffic regions while managing complexity through hierarchical organization of geographic data.
Solution Approach 2:
The patent implements dynamic pricing mechanisms that adjust in real-time based on demand conditions. The cost map dynamically updates pricing for different traffic regions and boundaries based on real-time drop-off/pick-up activity, time of day, and location-specific factors. This dynamic approach enables precise demand-based pricing without requiring permanently complex system configurations.
2Productivity
If demand pricing is implemented through a cost map that adjusts prices dynamically based on time, location, and vehicle type, then traffic flow can be optimized, but the system complexity increases
Solution Approach 1:
The patent pre-calculates and stores pricing information in a cost map for different traffic regions, boundaries, and vehicle types. The system performs preliminary geo-spatial cluster analysis to establish baseline pricing zones before real-time operations. This preliminary action reduces real-time computational complexity while maintaining dynamic pricing capabilities based on pre-established classifications.
Solution Approach 2:
The patent introduces a cost map as an intermediary data structure that mediates between complex geo-spatial analysis and simple pricing decisions. The cost map stores pre-processed pricing information that can be quickly queried during real-time operations, acting as a buffer that simplifies the interface between complex analysis systems and straightforward pricing applications.
3Ease of operation
If geo-spatial cluster analysis is applied to determine boundaries of traffic regions, then drop-off/pick-up traffic can be controlled, but the analysis and processing time increases
Solution Approach 1:
The patent performs geo-spatial cluster analysis periodically to update traffic region boundaries and classifications, rather than continuously in real-time. The system establishes boundaries based on historical and current drop-off/pick-up patterns at scheduled intervals. This periodic approach reduces processing time requirements while maintaining effective traffic control through regularly updated regional classifications.
Data Source
AI summary
Provided are a computer program product, system, and method for geo-spatial analysis to determine boundaries of traffic regions and classifications of the boundaries for controlling drop-off/pick-up traffic. A determination is made of drop-off/pick-up points for vehicles in traffic regions in which vehicles dropped-off and/or picked-up passengers. Geo-spatial cluster analysis is applied to the determined drop-off/pick-up points to determine boundaries of the traffic regions having drop-off/pick-up points forming clusters of similar drop-off/pick-up point densities. A determination is made of a respective classification for each respective one of the boundaries. Each respective classification indicates a relative density of drop-off/pick-up points in its respective boundary relative to densities of drop-off/pick-up points in other one of the boundaries. Classification related information based on the determined classifications is communicated to use to control drop-off/pick-up traffic in the traffic regions.