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Multi-Layer Data Model of a Complex Transportation Network

Multi-Layer Data Model of a Complex Transportation Network

Anton Ivaschenko, Sergey Maslennikov, Anastasia Stolbova, Oleg Golovnin
Copyright: © 2021 |Volume: 12 |Issue: 2 |Pages: 16
ISSN: 1947-3176|EISSN: 1947-3184|EISBN13: 9781799861652|DOI: 10.4018/IJERTCS.2021040102
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MLA

Ivaschenko, Anton, et al. "Multi-Layer Data Model of a Complex Transportation Network." IJERTCS vol.12, no.2 2021: pp.21-36. http://doi.org/10.4018/IJERTCS.2021040102

APA

Ivaschenko, A., Maslennikov, S., Stolbova, A., & Golovnin, O. (2021). Multi-Layer Data Model of a Complex Transportation Network. International Journal of Embedded and Real-Time Communication Systems (IJERTCS), 12(2), 21-36. http://doi.org/10.4018/IJERTCS.2021040102

Chicago

Ivaschenko, Anton, et al. "Multi-Layer Data Model of a Complex Transportation Network," International Journal of Embedded and Real-Time Communication Systems (IJERTCS) 12, no.2: 21-36. http://doi.org/10.4018/IJERTCS.2021040102

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Abstract

The paper presents an original multi-layer data model for software solutions to be used in transportation logistics. Versus the known technologies of data formalization based on their classification by similarity and conceptual consistency, there is proposed a new approach of information adaptive management oriented to application of parallel computing. As a basic decomposition approach, it is recommended to consider processing capacity characteristics and performance measures instead of objects and subjects classification typical for human perception. Two algorithms were proposed based on the method of criteria comparison. The sequential algorithm implements the classical approach to find a path on a graph, while the parallel one uses an approach that makes it possible to increase the efficiency of layer-by-layer task separation, taking into account the capabilities of computing system for simultaneous parallel calculations. A study conducted on real data showed an advantage in the execution time of the parallel computing algorithm over the usual sequential search of 38%.

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