Lovelace, R orcid.org/0000-0001-5679-6536, Fridman-Rojas, I and Long, R (2017) New tools of the trade? The potential and pitfalls of ’Machine Learning’ and ’DAGs’ to model origin-destination data. In: GeoComputation 2017. 2017 International Conference on GeoComputation, 04-07 Sep 2017, Leeds, UK. Centre for Computational Geography, University of Leeds
Abstract
This paper explores the potential for emerging methods Machine Learning and Directed Acyclic Graphs (DAGs) to be applied to transport modelling at the origin-destination (OD) level. OD data is inherently spatial and is complex, due to the multitude of ways of allocating geographic attributes to the OD pairs (e.g. buffers and intersections with geographic representations of OD data generated using straight desire lines, shortest path algorithms or probabilistic routing). This makes their analysis an interesting geocomputational challenge, seldom tackled by geographers. The application of Machine Learning and DAG methods, developed in other fields, to this geographical data holds great potential to improve the ability to infer causality in mode split from OD data. However, there are also pitfalls to using these methods which can be black boxes, even if the code is open source, if the analyst does not understand what they are doing with the data. Based on the work we discuss ways to ensure new methods in the field are used wisely and set-out next steps for our own research.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Keywords: | Machine Learning; Causal Inference |
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 No External Reference |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Nov 2017 13:04 |
Last Modified: | 17 Jan 2018 21:01 |
Published Version: | http://www.geocomputation.org/2017/papers/73.pdf |
Status: | Published |
Publisher: | Centre for Computational Geography, University of Leeds |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:124286 |