Journal article
Obstacle-avoiding shortest path derivation in a multicore computing environment
Computers, environment and urban systems, v 55, pp 1-10
01 Jan 2016
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The best obstacle avoiding path in continuous space, referred to as the Euclidean shortest path, is important for spatial analysis, location modeling and wayfinding tasks. This problem has received much attention in the literature given its practical application, and several solution techniques have been proposed. However, existing approaches are limited in their ability to support real time analysis in big data environments. In this research a multicore computing approach is developed that exploits spatial knowledge through the use of geographic information system functionality to efficiently construct an optimal shortest path. The approach utilizes the notion of a convex hull for iteratively evaluating obstacles and constructing pathways. Further, the approach is capable of incrementally improving bounds, made possible through parallel processing. Wayfinding routes that avoid buildings and other obstacles to travel are derived and discussed. (C) 2015 Elsevier Ltd. All rights reserved.
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Details
- Title
- Obstacle-avoiding shortest path derivation in a multicore computing environment
- Creators
- Insu Hong - W Virginia Univ, Eberly Coll Arts & Sci, Dept Geol & Geog, Morgantown, WV 26506 USAAlan T. Murray - Drexel UniversitySergio Rey - Arizona State University
- Publication Details
- Computers, environment and urban systems, v 55, pp 1-10
- Publisher
- Elsevier
- Number of pages
- 10
- Resource Type
- Journal article
- Language
- English
- Web of Science ID
- WOS:000367857000001
- Scopus ID
- 2-s2.0-84944447198
- Other Identifier
- 991019357770304721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Engineering, Environmental
- Environmental Studies
- Geography
- Operations Research & Management Science
- Regional & Urban Planning