Lovelace, R orcid.org/0000-0001-5679-6536 and Ellison, R (2018) stplanr: A Package for Transport Planning. The R Journal, 10 (2). pp. 7-23. ISSN 2073-4859
Abstract
Tools for transport planning should be flexible, scalable, and transparent. The stplanr package demonstrates and provides a home for such tools, with an emphasis on spatial transport data and non-motorized modes. The stplanr package facilitates common transport planning tasks including: downloading and cleaning transport datasets; creating geographic “desire lines” from origin-destination (OD) data; route assignment, locally and interfaces to routing services such as CycleStreets.net; calculation of route segment attributes such as bearing and aggregate flow; and ‘travel watershed’ analysis. This paper demonstrates this functionality using reproducible examples on real transport datasets. More broadly, the experience of developing and using R functions for transport applications shows that open source software can form the basis of a reproducible transport planning workflow. The stplanr package, alongside other packages and open source projects, could provide a more transparent and democratically accountable alternative to the current approach, which is heavily reliant on proprietary and relatively inaccessible software.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license. https://creativecommons.org/licenses/by/4.0/ |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Funding Information: | Funder Grant number Department of Transport RM5019 SO7766 Innovate UK fka Technology Strategy Board (TSB) 102426 Department of Transport RM5019 SO7766 Phase 2 Department of Transport No External Reference Department of Transport No External Reference |
Depositing User: | Symplectic Publications |
Date Deposited: | 10 Oct 2017 10:32 |
Last Modified: | 16 Jun 2020 13:05 |
Status: | Published |
Publisher: | R Foundation for Statistical Computing |
Identification Number: | 10.32614/RJ-2018-053 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:122024 |