Machine-Learning Route Matching for Noisy GPS Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing map matching technologies struggle with noisy, inaccurate, or imprecise raw location data due to GPS signal fluctuations and environmental conditions, leading to inaccurate route reconstruction and failure to consider the logical flow of movement between GPS points.
Innovation Solution
A multi-criteria scoring system using machine-learning models to evaluate link candidates based on travel time, heading difference, speed conformity, road curvature, and road classification, incorporating multi-timestamp look-ahead and look-behind processes to enhance accuracy and consider contextual data for route matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional map matching is used with raw GPS location data, then the system is simple and fast, but the route reconstruction becomes inaccurate due to noisy and imprecise GPS signals
Solution Approach 1:
The patent segments the route matching process into multiple independent scoring components: travel time score, spatial distance score, heading difference score, speed conformity score, and road curvature score. Each component evaluates a specific aspect of the match between GPS points and road network, allowing the system to handle noisy data by considering multiple dimensions simultaneously rather than relying on a single complex algorithm.
Solution Approach 2:
The patent merges multiple scoring mechanisms into a unified route matching framework. By combining travel time calculations, spatial distance measurements, heading angle comparisons, speed conformity checks, and road curvature assessments into a single integrated evaluation system, the patent achieves higher accuracy without requiring separate complex processing systems for each factor.
2Reliability
If only single-point matching is used, then the processing is fast and simple, but the logical flow of movement between GPS points is not considered
Solution Approach 1:
The patent performs preliminary calculations of travel time, spatial distance, heading differences, and speed conformity for multiple potential route segments before final route determination. By pre-computing these scoring components for candidate links, the system can quickly evaluate and compare alternatives without performing complex real-time analysis during the actual route matching process.
Solution Approach 2:
The patent maintains continuous evaluation of movement logic throughout the entire route matching process. Instead of processing GPS points independently, the system continuously assesses the logical flow between consecutive points by comparing headings, speeds, and spatial relationships, ensuring consistent and reliable route reconstruction that respects the temporal and spatial continuity of movement.
3Measurement precision
If multiple criteria are considered for scoring link candidates, then the route matching accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent divides the comprehensive link candidate evaluation into five distinct scoring components: travel time score, spatial distance score, heading difference score, speed conformity score, and road curvature score. Each component independently evaluates a specific characteristic, making the complex multi-criteria assessment manageable and computationally efficient through modular calculation of separate factors.
Solution Approach 2:
The patent transforms the route matching problem into a parameter optimization problem by assigning quantitative values to multiple criteria. By converting travel time, spatial distance, heading differences, speed conformity, and road curvature into numerical parameters that can be systematically compared and weighted, the system achieves high-accuracy evaluation through mathematical computation rather than complex logical reasoning.
Data Source
AI summary
Implementations claimed and described herein provide systems and methods for map routing matching system, wherein the one or more machine-learning models scores link candidates of location data points to determine a most probable route. In one implementation, use a machine-learning model to calculate a probability score for a plurality of link candidates. The machine-learning model scores each link candidates based on weighting set by training data, wherein the weighting associated with at least one of travel time, match distance, heading difference, speed conformity, road curvature, road classification, or travel distance versus time delay. A plurality of total probability scores derived from one or more combinations of probability scores for respective link candidates is calculated, wherein one link candidate is selected for each location data point.