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TRAN5340M
Transport Data Science Reading List

Transport Data Science, 2019/20, Semester 2
Dr Robin Lovelace
r.lovelace@leeds.ac.uk
Tutor information is taken from the Module Catalogue

Essential

  • Paper on the stplanr paper for transport planning (available online) (Lovelace and Ellison 2017)
  • Introductory and advanced content on geographic data in R, especially the transport chapter (available free online) (Lovelace, Nowosad, and Meunchow 2018)
  • Paper on analysing OSM data in Python (available online) (Boeing 2017)

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Core

  • Introduction to data science with R (available free online) (Grolemund and Wickham 2016)

  • Introductory textbook introducing machine learning with lucid prose and worked examples in R (available free online) (James et al. 2013)

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Optional

  • Book on transport data science in Python (Fox 2018)
  • For context, a report on the ‘transport data revolution’ (Transport Systems Catapult 2015)
  • Seminal text on visualisation (available online, style available in the tufte R package) (Tufte 2001)   
  • A paper on the use of SmartCard data (Gschwender, Munizaga, and Simonetti 2016)
  • An academic paper describing the development of a web application for the Department for Transport (Lovelace et al.

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Specific/online resources

  • It’s worth thinking about what you want to do next so I recommend taking a look for ‘data science’ and transport jobs on sites such as www.cwjobs.co.uk

  • For a refresher on maths it may be useful to have a maths text book on hand. This should cover mathematical concepts including vectors, matrices, eigenvectors, numerical parameter optimization, calculus, dierential equations, Gaussian distributions, Bayes’ rule, covariance matrices.

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Bibliography

Boeing, Geoff. 2017. “OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks.” Computers, environment and urban systems. ISSN: 0198-9715 65 (September): 126–39. https://doi.org/10.1016/j.compenvurbsys.2017.05.004.

Fox, Charles. 2018. Data science for transport : a self-study guide with computer exercises ISBN: 9783319729527. 1st ed. 2018 edition. New York, NY: Springer.

Grolemund, Garrett, and Hadley Wickham. 2016. R for data science : import, tidy, transform, visualize, and model data ISBN: 9781491910368 (e-book) 1 edition. O’Reilly Media.

Gschwender, Antonio, Marcela Munizaga, and Carolina Simonetti. 2016. “Using Smart Card and GPS Data for Policy and Planning: The Case of Transantiago.” Research in Transportation Economics, Competition and ownership in land passenger transport (selected papers from the thredbo 14 conference), 59 (November): 242–49. https://doi.org/10.1016/j.retrec.2016.05.004.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An introduction to statistical learning : with applications in R ISBN: 9781461471370 (acid-free paper); 1461471370 (acid-free paper); 9781461471387 (eBook); 1461471389 (eBook). Springer Science & Business Media.

Lovelace, Robin, and Richard Ellison. 2017. “Stplanr: A Package for Transport Planning.” The R Journal. https://github.com/ropensci/stplanr.

Lovelace, Robin, Anna Goodman, Rachel Aldred, Nikolai Berkoff, Ali Abbas, and James Woodcock. 2017. “The Propensity to Cycle Tool: An Open Source Online System for Sustainable Transport Planning.” Journal of Transport and Land Use 10 (1). https://doi.org/10.5198/jtlu.2016.862.

Lovelace, Robin, Jakub Nowosad, and Jannes Meunchow. 2018. Geocomputation with R. CRC Press. http://robinlovelace.net/geocompr.

Transport Systems Catapult. 2015. “The Transport Data Revolution.” Government. Transport Systems Catapult. https://ts.catapult.org.uk/wp-content/uploads/2016/04/The-Transport-Data-Revolution.pdf.

Tufte, Edward R. 2001. The Visual Display of Quantitative Information. 2nd ed. Cheshire, Conn: Graphics Press.

This list was last updated on 08/10/2018