GPU-accelerated parallel all-pair shortest path routing within stochastic road networks-Reference-Cited by-同舟云学术

GPU-accelerated parallel all-pair shortest path routing within stochastic road networks

Published:2024-09 Issue: Volume: Page:1-33
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Tang Wenwu¹²³^ORCID,Chen Tianyang¹²,Armstrong Marc P.⁴^ORCID

Affiliation:

1. Department of Earth, Environmental, and Geographical Sciences, University of North Carolina at Charlotte, Charlotte, NC, USA

2. Center for Applied Geographic Information Science, University of North Carolina at Charlotte, Charlotte, NC, USA

3. School of Data Science, University of North Carolina at Charlotte, Charlotte, NC, USA

4. The University of Iowa, Iowa City, IA, USA

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2024.2394651

Reference47 articles.

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3. Armstrong, M.P., 2020. High performance computing for geospatial applications: A prospective view. In: W. Tang, and S. Wang, eds. High performance computing for geospatial applications. Cham, Switzerland: Springer, 271–284.

4. Path finding under uncertainty

5. Reliable shortest path finding in stochastic networks with spatial correlated link travel times;Chen B.Y.;International Journal of Geographical Information Science,2012