Academic Bibliography

 Search 200 years of publications by Ghent University researchers.
Advanced search
1 file | 1.52 MB
Add to list
  1. Search results
  2. Geospatially partitioning public tran...

Geospatially partitioning public transit networks for open data publishing

Harm Delva, Julian Andres Rojas Melendez (UGent) , Pieter Colpaert (UGent) and Ruben Verborgh (UGent)
(2021) JOURNAL OF WEB ENGINEERING. 20(4). p.1003-1026
Author
Organization
Abstract
Public transit operators often publish their open data in a data dump, but developers with limited computational resources may not have the means to process all this data efficiently. In our prior work we have shown that geospatially partitioning an operator's network can improve query times for client-side route planning applications by a factor of 2.4. However, it remains unclear whether this works for all network types, or other kinds of applications. To answer these questions, we must evaluate the same method on more networks and analyze the effect of geospatial partitioning on each network separately. In this paper we process three networks in Belgium: (i) the national railways, (ii) the regional operator in Flanders, and (iii) the network of the city of Brussels, using both real and artificially generated query sets. Our findings show that on the regional network, we can make query processing 4 times more efficient, but we could not improve the performance over the city network by more than 12%. Both the network's topography, and to a lesser extent how users interact with the network, determine how suitable the network is for partitioning. Thus, we come to a negative answer to our question: our method does not work equally well for all networks. Moreover, since the network's topography is the main determining factor, we expect this finding to apply to other graph-based geospatial data, as well as other Link Traversal-based applications.
Keywords
Linked data, open data, mobility, maintainability, web API engineering

Downloads

  • Download
    (...).pdf
    • full text (Published version)
    • |
    • UGent only
    • |
    • PDF
    • |
    • 1.52 MB

Citation

Please use this url to cite or link to this publication:

MLA
Delva, Harm, et al. “Geospatially Partitioning Public Transit Networks for Open Data Publishing.” JOURNAL OF WEB ENGINEERING, vol. 20, no. 4, 2021, pp. 1003–26, doi:10.13052/jwe1540-9589.2045.
APA
Delva, H., Rojas Melendez, J. A., Colpaert, P., & Verborgh, R. (2021). Geospatially partitioning public transit networks for open data publishing. JOURNAL OF WEB ENGINEERING, 20(4), 1003–1026. https://doi.org/10.13052/jwe1540-9589.2045
Chicago author-date
Delva, Harm, Julian Andres Rojas Melendez, Pieter Colpaert, and Ruben Verborgh. 2021. “Geospatially Partitioning Public Transit Networks for Open Data Publishing.” JOURNAL OF WEB ENGINEERING 20 (4): 1003–26. https://doi.org/10.13052/jwe1540-9589.2045.
Chicago author-date (all authors)
Delva, Harm, Julian Andres Rojas Melendez, Pieter Colpaert, and Ruben Verborgh. 2021. “Geospatially Partitioning Public Transit Networks for Open Data Publishing.” JOURNAL OF WEB ENGINEERING 20 (4): 1003–1026. doi:10.13052/jwe1540-9589.2045.
Vancouver
1.
Delva H, Rojas Melendez JA, Colpaert P, Verborgh R. Geospatially partitioning public transit networks for open data publishing. JOURNAL OF WEB ENGINEERING. 2021;20(4):1003–26.
IEEE
[1]
H. Delva, J. A. Rojas Melendez, P. Colpaert, and R. Verborgh, “Geospatially partitioning public transit networks for open data publishing,” JOURNAL OF WEB ENGINEERING, vol. 20, no. 4, pp. 1003–1026, 2021.
@article{8729517,
  abstract     = {{Public transit operators often publish their open data in a data dump, but developers with limited computational resources may not have the means to process all this data efficiently. In our prior work we have shown that geospatially partitioning an operator's network can improve query times for client-side route planning applications by a factor of 2.4. However, it remains unclear whether this works for all network types, or other kinds of applications. To answer these questions, we must evaluate the same method on more networks and analyze the effect of geospatial partitioning on each network separately. In this paper we process three networks in Belgium: (i) the national railways, (ii) the regional operator in Flanders, and (iii) the network of the city of Brussels, using both real and artificially generated query sets. Our findings show that on the regional network, we can make query processing 4 times more efficient, but we could not improve the performance over the city network by more than 12%. Both the network's topography, and to a lesser extent how users interact with the network, determine how suitable the network is for partitioning. Thus, we come to a negative answer to our question: our method does not work equally well for all networks. Moreover, since the network's topography is the main determining factor, we expect this finding to apply to other graph-based geospatial data, as well as other Link Traversal-based applications.}},
  author       = {{Delva, Harm and Rojas Melendez, Julian Andres and Colpaert, Pieter and Verborgh, Ruben}},
  issn         = {{1540-9589}},
  journal      = {{JOURNAL OF WEB ENGINEERING}},
  keywords     = {{Linked data,open data,mobility,maintainability,web API engineering}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{1003--1026}},
  title        = {{Geospatially partitioning public transit networks for open data publishing}},
  url          = {{http://doi.org/10.13052/jwe1540-9589.2045}},
  volume       = {{20}},
  year         = {{2021}},
}
Altmetric
View in Altmetric
Web of Science
Times cited: