Map Matching Trip Data to Road Network Using Partitioning
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Solution Overview
Problem
Existing systems face significant processing burdens when matching large volumes of trip data to transportation network data, leading to slow performance.
Innovation Solution
The method involves partitioning trip data and transportation network data across multiple processing units, where local matching is performed to identify subsets of paths traversed by trips, using a Hilbert space filling curve and Rtree indexing for efficient data organization and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a system performs mapping between a large number of trips and road network data, then the mapping accuracy is improved, but the processing time increases substantially
Solution Approach 1:
The patent divides the road network data into multiple partitions, each assigned to a different processing unit. Trip data is also partitioned and distributed across multiple processing units. Each processing unit performs local map matching independently on its assigned subsets, significantly reducing the processing time while maintaining mapping accuracy through distributed parallel processing.
2Quantity of substance
If a system processes large volumes of trip data to match with transportation network data, then the completeness of matching is improved, but the processing burden increases substantially
Solution Approach 1:
The system partitions both trip data and transportation network data into multiple subsets, distributing them across numerous processing units. This segmentation allows the system to handle large volumes of data by processing smaller subsets in parallel, reducing the processing burden on any single unit while maintaining complete matching coverage across all data.
Solution Approach 2:
The patent implements a scalable distributed processing architecture where multiple processing units perform identical map matching operations on different data subsets. This universal approach allows the system to handle varying data volumes by simply adding or removing processing units, making the system adaptable to different data scales without increasing individual unit complexity.
3Measurement precision
If a system performs comprehensive map matching on all trip data with the complete road network, then the accuracy of path identification is improved, but the processing speed decreases
Solution Approach 1:
The patent divides the road network into multiple partitions and assigns specific subsets to different processing units. Each unit performs comprehensive map matching on its assigned subsets, ensuring accurate path identification within each partition while achieving high overall processing speed through parallel execution across multiple units.
Solution Approach 2:
The patent transitions from a single-dimensional sequential processing model to a multi-dimensional distributed parallel processing model. By adding the dimension of spatial distribution across multiple processing units, the system maintains comprehensive matching accuracy while achieving significant speedup through concurrent processing of multiple data subsets simultaneously.
Data Source
AI summary
Trip data that includes points traversed during corresponding trips is received. Transportation network data that describes geographic locations and links between the geographic locations is received. The trip data and transportation network data are partitioned across a plurality of processing units executing on one or more processors. At each of the plurality of processing units, a subset of the trip data at the corresponding processing unit is locally matched to a subset of the transportation network data at the corresponding processing unit to identify a subset of paths of the transportation network data traversed by one or more of the trips.