R vs QGIS for sustainable transport planning

The 23rd iteration of the GIS Research UK conference (#GISRUK) conference was the largest ever. 250 researchers, industry representatives and academics attended from the vibrant geospatial research communities in the UK, Europe and beyond. GISRUK has become a centrepoint for discussion of new methods, software and applications in the field. I was on the organising committee, reviewed some excellent papers for the event (a full list of these is available for download here) and attended some truly ground-breaking talks. This experience has shown that the geospatial community in the UK is strong, especially with regards to growth in open access data and open source software in the field.


This article is about one part of GISRUK and insights gleaned from it about R, QGIS and other tools for sustainable transport planning. GIS for Transport Applications (#GIS4TA for short) was a practical day-long workshop that preceded the main event. I organised the workshop and (with help from Eusebio Odiari, The Transport Geography Research Group and the Royal Geographical Society) it seems to have been a great success. More than 30 people attended, including a decent portion from transport consultancies such as Integrated Transport Planning Ltd TRP Consulting and the European Railway Association (ERA). Specifically, it is about the use of R and QGIS tools for transport planning and the potential for their adoption in academic, public and private-sector transport planning. The focus of the workshop was deliberately on open source software and sustainable transport because these are growth areas in the field that are essential for democratic and healthy transport systems compatible with the science of climate change (Tamminga, 2012). A recent report, for example, suggests we need to almost completely transition away from fossil fuels by 2050 (McGlade et al., 2015). New datasets and methods for analysing and modelling them can help get us there in the recalcitrant transport sector (Gossling, 2014).

R for transport applications

The workshop kicked-off with a short talk on ‘R and QGIS for transport applications’, which laid out some of the motivations for running the workshop outlined above. Other than a few ’early adopters’, the transport modelling community is generally conservative, based largely on mature proprietary products such as SATURN and Vissim.

The slides from this talk are available here:

Tutorial: Introduction to R and QGIS for transport applications

Routing with R

The second talk was by Nick Bearman, who provided an overview of routing in R, as well as an excellent practical tutorial.

The practical demonstrated 2 ways of routing in R:

  1. Using ggmap. The following code was used to navigate to the event!
from <- 'Leeds station, New Station Street, Leeds LS1 5DL, United Kingdom'
to <- 'LS2 9JT'
route_df <- route(from, to, structure = 'route', mode = 'walking')
qmap('Merrion Centre', zoom = 15) +
    aes(x = lon, y = lat),  colour = 'red', size = 1.5,
    data = route_df, lineend = 'round')

  1. The package I created, stplanr, to get routes optimised for cyclists (see transport-workshop.Rmd for a working version):
rquiet <- gLines2CyclePath(l = rlines, plan = "quietest")
plot(rquiet[1,]) # route from Leeds station to Leeds University (North - South)
plot(rquiet[2,]) # route from Leeds to Manchester!

Tutorial: Route analysis using R

Large gps datasets with PostGIS

The most technical session involved using R to query huge datasets storing GPS data containing 100,000+ rows. Amazingly, Richard and Adrian Ellison set up a remotely accessible database instance from their laptop which participants queried via RPostgreSQL. Their session information can be seen here:


A hackathon

Finally there was a miniature hackathon organised by Godwin Yeboah. Participants made progress in better understanding the travel patterns of cyclists using real data. The hackathon notes can be found here:



GIS is a field of knowledge that has a huge amount to offer transport planners and researchers, especially regarding new and open source software tools that can effectively generate, process and analyse transport-related data. R is well-suited to fill this research gap and has a wide range of tools to help. Packages such as ggmap (Kahle and Wickham 2013), RPostgreSQL and the new stplanr have great potential to help plan the transport systems of the future. QGIS is also increasingly attractive for transport applications, with it inbuilt support for PGRouting, flow analysis and a friendly user interface that many will be used to.

Photos taken during the hackathon are testament to its role as a forum for not only learning but also debate about the future of GIS in transport. These can be seen here:


Hearing feedback from users new to R using it to solve transport problems provided an insight into how it compares to traditional tools. The removal of ‘glass ceilings’ imposed by restrictive licenses or the need to buy ‘add-on’ features was one comment, but that applies equally to QGIS and other FOSS4G offerings. The steep learning curve of R seems to still be an issue compared with QGIS, although this is becoming less of an issue with the evolution of RStudio as an GUI for R. In conclusion, both R and QGIS are coming of age as tools in the transport planner’s ‘war cabinet’. The latest evidence unequivocally shows the impact of transport decisions on obesity, environmental degradation and quality of life. So it is time, surely, to harness this new open source software to ‘save the world’!


Thanks to the Consumer Data Research Centre and the Royal Geographical society for subsidising the event. Thanks to all the participants and especially the demonstrators Godwin, Nick, Adrian and Richard for making it happen.


Gössling, Stefan, and Scott Cohen. 2014. “Why sustainable transport policies will fail: EU climate policy in the light of transport taboos.” Journal of Transport Geography 39 (July). Elsevier Ltd: 197–207. doi:10.1016/j.jtrangeo.2014.07.010.

Kahle, D, and Hadley Wickham. 2013. “ggmap: Spatial Visualization with ggplot2.” The R Journal 5: 144–61. citeseerx.ist.psu.edu.

McGlade, Christophe, and Paul Ekins. 2015. “The geographical distribution of fossil fuels unused when limiting global warming to 2 °C.” Nature 517 (7533). Nature Publishing Group: 187–90. doi:10.1038/nature14016.

Tamminga, Guus, Marc Miska, Edgar Santos, Hans van Lint, Arturo Nakasone, Helmut Prendinger, and Serge Hoogendoorn. 2012. “Design of Open Source Framework for Traffic and Travel Simulation.” Transportation Research Record: Journal of the Transportation Research Board 2291 (-1): 44–52. doi:10.3141/2291-06.

Robin Lovelace
Robin Lovelace
Associate Professor of Transport Data Science

My research interests include geocomputation, data science for transport applications, active travel uptake and decarbonising transport systems