Map Matching System Grid-Based Event Data Processing
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Solution Overview
Problem
Existing map matching systems face challenges in processing large volumes of vehicle event data at high speeds while maintaining analysis precision, particularly when dealing with numerous vehicles and varying road types, as they often rely on specific methods that limit the use of event data for purposes beyond tracking vehicle travel or estimating traffic congestion.
Innovation Solution
A map matching system that employs a road network storage unit, event data processing units, and a grid-based road dividing and combining mechanism to efficiently correlate event data with road networks, allowing for high-speed processing and analysis by assigning common IDs to adjacent grids with matching arc sets and selecting relevant event data based on threshold values, thereby reducing processing load and maintaining precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If event data from numerous vehicles is processed using traditional map matching methods, then comprehensive traffic analysis can be performed, but processing speed decreases and system complexity increases
Solution Approach 1:
The patent divides the service area into multiple grid regions, where each grid independently manages and processes event data within its boundaries. This segmentation allows parallel processing across grids, significantly improving processing speed while reducing the complexity burden on any single grid unit.
Solution Approach 2:
The patent introduces a spatial dimension by organizing event data according to grid coordinates (latitude/longitude ranges). This dimensional organization enables efficient indexing and retrieval operations, transforming complex multi-vehicle data processing into structured spatial queries that can be executed rapidly.
2Measurement precision
If all event data from vehicles is retained and processed, then analysis precision is maintained, but processing load and time consumption increase
Solution Approach 1:
The patent applies different processing strategies to different grids based on their specific characteristics (event data volume, vehicle density, road complexity). High-traffic grids with sufficient event data undergo full processing to maintain precision, while low-traffic grids use simplified processing, optimizing the balance between precision and processing time locally across the entire service area.
Solution Approach 2:
The patent processes event data selectively rather than uniformly across all grids. By applying full processing only where necessary (grids with sufficient event data and high analysis value) and using simplified processing elsewhere, the system maintains required analysis precision while significantly reducing overall processing time and computational load.
3Productivity
If event data is filtered to reduce processing load, then processing speed improves, but data completeness and analysis accuracy may deteriorate
Solution Approach 1:
The patent performs preliminary organization of event data by grid region before detailed processing. Event data is pre-grouped and indexed according to spatial coordinates, allowing the system to quickly identify which grids contain sufficient data for full processing. This preliminary action enables efficient subsequent processing while preserving all necessary data for accurate analysis.
Solution Approach 2:
The patent introduces grid-based spatial indexing as an intermediary layer between raw event data and the analysis process. This intermediary structure organizes data by geographic regions and provides metadata about data volume and characteristics, enabling intelligent processing decisions without losing any underlying event data. The intermediary facilitates both speed optimization and data completeness preservation.
Data Source
AI summary
Provided is a device, method and program enabling high-speed map matching while maintaining a precision of an analysis based on a map matching result even when event data is transmitted from numerous vehicles.A grid is obtained by the division of a region in which a road network exists into a latitude direction and a longitude direction by equal spaces based on data stored in the road data storage unit. A grid ID is unitarily assigned to each set of grids whose sets of arcs which are roads are common and arc passing the grid.An event grid matching unit correlates event data collected from a vehicle and a grid. When the number of pieces of event data correlated with the grid is large, a data selecting unit selects a part of the data. An event road matching unit correlates the selected event data and a road in the grid.