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Article

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

by 1,2, 1,3,*, 1,2, 1,2, 1,2 and 1
1
Faculty of Information Engineering, China University of Geosciences (Wuhan), 388 Lumo Road, Wuhan 430074, China
2
National Engineering Research Center of Geographic Information System, Wuhan 430074, China
3
State Key Laboratory of Geo-Information Engineering, Xi’an 710054, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(12), 472; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7120472
Received: 2 October 2018 / Revised: 3 December 2018 / Accepted: 5 December 2018 / Published: 7 December 2018
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. View Full-Text
Keywords: parallel computing; road-network matching; GPU architecture; PSO parallel computing; road-network matching; GPU architecture; PSO
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MDPI and ACS Style

Wan, B.; Yang, L.; Zhou, S.; Wang, R.; Wang, D.; Zhen, W. A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture. ISPRS Int. J. Geo-Inf. 2018, 7, 472. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7120472

AMA Style

Wan B, Yang L, Zhou S, Wang R, Wang D, Zhen W. A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture. ISPRS International Journal of Geo-Information. 2018; 7(12):472. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7120472

Chicago/Turabian Style

Wan, Bo, Lin Yang, Shunping Zhou, Run Wang, Dezhi Wang, and Wenjie Zhen. 2018. "A Parallel-Computing Approach for Vector Road-Network Matching Using GPU Architecture" ISPRS International Journal of Geo-Information 7, no. 12: 472. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7120472

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