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Large-scale test data set for location problems
KTH, School of Architecture and the Built Environment (ABE), Transport Science.
2018 (English)In: Data in Brief, E-ISSN 2352-3409, Vol. 17, p. 267-274Article in journal (Refereed) Published
Abstract [en]

Designers of location algorithms share test data sets (benchmarks) to be able to compare performance of newly developed algorithms. In previous decades, the availability of locational data was limited. Big data has revolutionised the amount and detail of information available about human activities and the environment. It is expected that integration of big data into location analysis will increase the resolution and precision of input data. Consequently, the size of solved problems will significantly increase the demand on the development of algorithms that will be able to solve such problems. Accessibility of realistic large scale test data sets, with the number of demands points above 100,000, is very limited. The presented data set covers entire area of Slovakia and consists of the graph of the road network and almost 700,000 connected demand points. The population of 5.5 million inhabitants is allocated to the locations of demand points considering the residential population grid to estimate the size of the demand. The resolution of demand point locations is 100 m. With this article the test data is made publicly available to enable other researches to investigate their algorithms. The second area of its utilisation is the design of methods to eliminate aggregation errors that are usually present when considering location problems of such size. The data set is related to two research articles: “A Versatile Adaptive Aggregation Framework for Spatially Large Discrete Location-Allocation Problem” (Cebecauer and Buzna, 2017) [1] and “Effects of demand estimates on the evaluation and optimality of service centre locations” (Cebecauer et al., 2016) [2]. 

Place, publisher, year, edition, pages
Elsevier Inc. , 2018. Vol. 17, p. 267-274
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-227360DOI: 10.1016/j.dib.2018.01.008Scopus ID: 2-s2.0-85041416854OAI: oai:DiVA.org:kth-227360DiVA, id: diva2:1212883
Note

Export Date: 9 May 2018; Article; Correspondence Address: Cebecauer, M.; Department of Transport Science, KTH Royal Institute of Technology, Teknikringen 10, Sweden; email: matejc@kth.se. QC 20180604

Available from: 2018-06-04 Created: 2018-06-04 Last updated: 2018-12-07Bibliographically approved

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  • apa
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