12.9 Future directions of travel

This chapter provides a taste of the possibilities of using geocomputation for transport research.It has explored some key geographic elements that make-up a city’s transport system using open data and reproducible code.The results could help plan where investment is needed.

Transport systems operate at multiple interacting levels, meaning that geocomputational methods have great potential to generate insights into how they work.There is much more that could be done in this area: it would be possible to build on the foundations presented in this chapter in many directions.Transport is the fastest growing source of greenhouse gas emissions in many countries, and is set to become “the largest GHG emitting sector, especially in developed countries” (see EURACTIV.com).Because of the highly unequal distribution of transport-related emissions across society, and the fact that transport (unlike food and heating) is not essential for well-being, there is great potential for the sector to rapidly decarbonize through demand reduction, electrification of the vehicle fleet and the uptake of active travel modes such as walking and cycling.Further exploration of such ‘transport futures’ at the local level represents promising direction of travel for transport-related geocomputational research.

Methodologically, the foundations presented in this chapter could be extended by including more variables in the analysis.Characteristics of the route such as speed limits, busyness and the provision of protected cycling and walking paths could be linked to ‘mode-split’ (the proportion of trips made by different modes of transport).By aggregating OpenStreetMap data using buffers and geographic data methods presented in Chapters 3 and 4, for example, it would be possible to detect the presence of green space in close proximity to transport routes.Using R’s statistical modeling capabilities, this could then be used to predict current and future levels of cycling, for example.

This type of analysis underlies the Propensity to Cycle Tool (PCT), a publicly accessible (see www.pct.bike) mapping tool developed in R that is being used to prioritize investment in cycling across England (Lovelace et al. 2017).Similar tools could be used to encourage evidence-based transport policies related to other topics such as air pollution and public transport access around the world.