A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture

Abstract

The road-network matching method is an effective tool for map integration, fusion, and update. Due to the complexity of road networks in the real world, matching methods often contain a series of complicated processes to identify homonymous roads and deal with their intricate relationship. However, traditional road-network matching algorithms, which are mainly central processing unit (CPU)-based approaches, may have performance bottleneck problems when facing big data. We developed a particle-swarm optimization (PSO)-based parallel road-network matching method on graphics-processing unit (GPU). Based on the characteristics of the two main stages (similarity computation and matching-relationship identification), data-partition and task-partition strategies were utilized, respectively, to fully use GPU threads. Experiments were conducted on datasets with 14 different scales. Results indicate that the parallel PSO-based matching algorithm (PSOM) could correctly identify most matching relationships with an average accuracy of 84.44%, which was at the same level as the accuracy of a benchmark—the probability-relaxation-matching (PRM) method. The PSOM approach significantly reduced the road-network matching time in dealing with large amounts of data in comparison with the PRM method. This paper provides a common parallel algorithm framework for road-network matching algorithms and contributes to integration and update of large-scale road-networks.

Multi-Scale Road Matching Based on the Summation Product of Orientation and Distance and Shape Descriptors

“…Zhang, J. et al [17] proposed an improved probabilistic relaxation method, considering both local and global optimizations for the matching of multi-scale of road networks. Wan, B. et al [18] developed a particle-swarm optimization (PSO)-based parallel road-network matching method on a graphics-processing unit (GPU). Chehreghan, A. et al [19] investigated the efficiency of the most important and applicable spatial distances (13 types of distance methods) in vector datasets with different scales and sources.…”

“…The traditional F-score is the harmonic mean of precision and recall. Formula (18) shows the F-score [38,41,42].…”