GPU-based Parallel Computing for Activity-based Travel Demand Models
Activity-based travel demand models (ABMs) are gaining popularity in the field of traffic modeling because of their high level of detail compared to traditional travel demand models. Due to this, however, ABMs have high computational requirements, making ABMs hard to use for analysis and optimization purposes. The authors address this problem by relying on the concept of parallel computing using a computer’s graphics processing unit (GPU). To illustrate the potential of GPU computing for ABM, the authors present a pilot study in which they compare the observed computation time of an ABM GPU implementation that they built using NVIDIA’s CUDA framework with similar, non-parallel implementations. The authors conclude that speed-ups up to a factor 50 can be realized, enabling the use of ABMs both for fast analysis of scenarios and for optimization purposes.
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- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/18770509
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Supplemental Notes:
- © 2019 H. Zhou et al. Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
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Authors:
- Zhou, H
- Dorsman, J L
- Snelder, M
- de Romph, E
- Mandjes, M
- Publication Date: 2019
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: pp 726-732
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Serial:
- Procedia Computer Science
- Volume: 151
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 1877-0509
- Serial URL: http://www.sciencedirect.com/science/journal/18770509
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Travel demand
- Uncontrolled Terms: Activity based modeling; Graphics processing units; Parallel computing; Speed-up
- Subject Areas: Highways; Planning and Forecasting;
Filing Info
- Accession Number: 01712338
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 24 2019 12:35PM