Map-Centric Map Matching Using Pre-Computed Spatial Boundaries
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Solution Overview
Problem
Current map matching techniques are computationally expensive and inefficient, particularly for large datasets of probe points, as they require separate spatial searches for each point, leading to substantial execution time and costs, especially in real-time applications.
Innovation Solution
A map-centric map matching technique that pre-computes spatial boundaries for road links, reducing the need for distance calculations and allowing efficient matching of probe data points to road segments by using pre-computed polygons, stored offline for efficient post-processing and real-time applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If probe-centric map matching techniques are used to analyze each probe point individually, then map matching can be performed, but the number of spatial searches becomes proportional to the number of probe points, leading to substantial execution time and processing costs
Solution Approach 1:
The patent pre-computes spatial boundaries for road links before processing probe points. By calculating and storing these boundaries in advance, the system eliminates the need for expensive spatial searches during actual map matching operations. This preliminary computation allows rapid matching of probe points to pre-defined boundary regions, dramatically improving processing speed while maintaining accuracy.
Solution Approach 2:
Instead of searching for road segments around each probe point (probe-centric approach), the patent inverts the approach by defining spatial boundaries around road links and checking which boundary contains the probe point (map-centric approach). This inversion transforms the search process from active querying to passive containment checking, reducing computational complexity from O(n) spatial searches to O(1) boundary containment tests.
2Measurement precision
If separate spatial searches are conducted for each probe point to identify the closest road segment, then map matching can be performed, but computational expenses and execution time increase substantially for large probe data sets
Solution Approach 1:
The system pre-computes spatial boundaries for all road links and stores them in advance. This preliminary action eliminates the need for expensive real-time spatial searches during map matching. By having pre-computed boundaries ready, the system can rapidly determine which boundary contains each probe point without performing complex distance calculations or spatial queries, thereby reducing processing capacity requirements.
Solution Approach 2:
The patent creates simplified representations (copies) of road links as spatial boundaries. Instead of working with the full complexity of road network data during matching operations, the system uses these pre-computed boundary representations that capture the essential spatial information needed for probe point matching. This copying approach reduces the computational burden while maintaining sufficient accuracy for road segment identification.
3Adaptability or versatility
If probe-centric map matching is used, then each probe point can be processed independently, but the process becomes inefficient for real-time applications due to substantial execution time
Solution Approach 1:
By pre-computing spatial boundaries before processing probe points, the system eliminates time-consuming spatial searches during real-time operations. The preliminary computation of boundaries allows subsequent probe point matching to occur instantly by simple containment checking, dramatically reducing latency while maintaining the flexibility to handle both real-time and historical probe data.
Data Source
AI summary
A method, apparatus and computer program product are provided to process probe data in accordance with a map-centric map matching technique. Methods may include obtaining a road link from a database of a plurality of road links; calculating a boundary separation distance for spacing vertices along a length of the road link; determining a sequence of vertices along the road link according to the boundary separation distance; generating, for each vertex, a spatial boundary where an overlap between spatial boundaries of adjacent vertices extends a first distance from the road link, where the first distance is a minimum distance from the road link; and providing for storage of a spatial boundary structure for the road link including the plurality of spatial boundaries associated with the road link.