Road Density Calculation for Map Matching Accuracy
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Solution Overview
Problem
Existing map matching techniques face challenges in accurately assigning probe data points to road segments due to inaccuracies caused by technical and environmental factors, and fail to account for the distribution of roads in an area, leading to ambiguity and errors in navigation services.
Innovation Solution
A method for calculating road density based on the average distance from randomly selected points to the nearest road segment, which generates a map matching parameter used to improve the accuracy of probe data point assignment to road segments, considering the distribution of roads in the area.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional map matching techniques are used, then the process is simple, but the accuracy of probe data point assignment to road segments is poor due to not accounting for road distribution
Solution Approach 1:
The patent calculates road density values for different map regions in advance, before actual map matching operations. These pre-calculated density values are stored and later used to adjust map matching parameters, eliminating the need for complex real-time road distribution analysis during matching operations.
Solution Approach 2:
The patent introduces road density as a new parameter that modifies traditional map matching parameters. By changing the matching threshold or search radius based on road density (higher density allows smaller search radius, lower density requires larger radius), the system adapts to different road distributions without fundamentally changing the matching algorithm.
2Reliability
If probe data points are matched without considering road distribution, then the calculation is fast, but errors and ambiguity increase in areas with varying road density
Solution Approach 1:
Road distribution characteristics are analyzed and stored as pre-computed data before matching operations. This preliminary analysis of road patterns, intersections, and density varies by region, enabling faster and more reliable matching without repeated real-time analysis.
Solution Approach 2:
The patent applies different matching strategies and parameters to different map regions based on their specific road density characteristics. Urban areas with high road density use different matching thresholds compared to rural areas with low density, optimizing both reliability and efficiency for each locale.
3Adaptability or versatility
If the search radius for map matching is fixed, then the algorithm is simple to implement, but it fails to adapt to areas with different road densities leading to matching errors
Solution Approach 1:
The patent dynamically adjusts the search radius or matching threshold parameter based on the road density value of the current map region. In high-density urban areas, a smaller radius suffices, while in low-density rural areas, a larger radius is automatically applied, with adjustments governed by pre-calculated density metrics.
Solution Approach 2:
The matching parameters transition from static fixed values to dynamic values that automatically adapt to local road conditions. The system continuously adjusts search parameters based on the road density characteristics of the region being processed, making the algorithm flexible across diverse environments.
Data Source
AI summary
A map region is analyzed by a mobile device or by a server. The map region including one or more road segments is identified. Multiple points are selected from the map region. From each of the selected points, a distance is measured from the selected point to a nearest road segment of the one or more road segments. A road density value is determined from the distances. The road density value may be based on an average of the distances. In one example, the road density value is an inverse of the average of the distances. The road density value may be a parameter for matching subsequent probe points to a road segment in the map region.