Open AccessGeospatially Partitioning Public Transit Networks for Open Data Publishing
Author(s) -
Harm Delva,
Julián Andrés Rojas-Morales,
Pieter Colpaert,
Ruben Verborgh
Publication year - 2021
Publication title -
journal of web engineering/journal of web engineering on line
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.151
H-Index - 13
eISSN - 1544-5976
pISSN - 1540-9589
DOI - 10.13052/jwe1540-9589.2045
Subject(s) - computer science , geospatial analysis , tree traversal , graph traversal , process (computing) , publication , data mining , graph , operator (biology) , data science , world wide web , theoretical computer science , geography , cartography , advertising , business , programming language , operating system , biochemistry , chemistry , repressor , transcription factor , gene
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.