Map Matching Probe Data Using Radius-Based Filtering and Parallel Processing
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Solution Overview
Problem
Conventional map matching processes for vehicle and traffic data require substantial processing capacity and time due to the vast amount of probe data, making it inefficient to accurately match crowd-sourced data from mobile devices to corresponding map elements.
Innovation Solution
A method that involves receiving probe data points with location and time information, establishing temporal sequences, filtering redundant points, and subdividing the data into subsets for parallel processing to efficiently match probe data points to road segments based on correspondences between map elements within a predefined radius.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional map matching processes are used to accurately match all probe data points to map elements, then matching accuracy is improved, but processing time and computational resources increase substantially
Solution Approach 1:
The patent applies preliminary action by performing pre-matching operations before the main map matching process. Specifically, it pre-identifies map elements within a radius of each probe data point and pre-establishes correspondences between consecutive data points. This preliminary processing reduces the computational burden during the actual map matching execution, thereby decreasing processing time while maintaining accuracy.
Solution Approach 2:
The patent segments the probe data processing into distinct phases: filtering data points, pre-identifying map elements within radii, establishing correspondences between consecutive points, and performing final map matching. This segmentation allows each phase to be optimized independently, improving overall processing efficiency without sacrificing matching accuracy.
2Measurement precision
If all probe data points are processed through traditional map matching, then matching completeness is improved, but processing complexity increases substantially
Solution Approach 1:
The patent extracts and processes only the essential information needed for map matching. It filters probe data points to retain only those necessary for accurate matching, pre-identifies relevant map elements within specific radii, and extracts correspondence relationships between consecutive data points. This extraction reduces processing complexity by eliminating unnecessary computations while maintaining matching completeness.
Solution Approach 2:
By performing preliminary filtering and pre-identification of map elements, the patent reduces the complexity of the main map matching process. The preliminary actions prepare the data in advance, so that the final matching step operates on pre-processed, organized information rather than raw, unprocessed data.
3Measurement precision
If the radius for identifying map elements is increased to improve matching accuracy, then more map elements are found, but the volume of data to be processed increases
Solution Approach 1:
The patent applies local quality by using a radius-based approach that adapts to local conditions. Each probe data point has its own search radius, and map elements are identified locally within that radius. This local processing ensures that only relevant map elements near each data point are considered, improving accuracy without proportionally increasing overall data volume.
Solution Approach 2:
The patent performs preliminary identification of map elements within radii before the main matching process. By pre-identifying and storing only the relevant map elements within each radius, it reduces the data volume that needs to be processed during the actual map matching, while still maintaining comprehensive coverage for accurate matching.
Data Source
AI summary
A method, apparatus, and computer program product are provided for map matching probe data to map elements. Methods may include: receiving a plurality of probe data points, where each probe data point includes location information associated with the probe apparatus and time information associated with the location information; establishing locations of the probe data points of a probe apparatus temporally sequenced along a path; establishing a radius around each of the probe data points; identifying map elements found within each radius; filtering probe data points to obtain a subset of probe data points; establishing correspondences between map elements found within each radius of the subset of probe data points; and map matching the subset of probe data points to one or more road segments based on the correspondences between map elements found within each radius of the subset of probe data points.