Abstract
Encouraging more cycling is increasingly seen as an important way to create more sustainable cities and to improve public health. Understanding how cyclists travel and how to encourage cycling requires data; something which has traditionally been lacking. New sources of data are emerging which promise to reveal new insights. In this paper, we use data from the activity tracking app Strava to examine where people in Glasgow cycle and how new forms of data could be utilised to better understand cycling patterns. We propose a method for augmenting the data by comparing the observed link flows to the link flows which would have resulted if people took the shortest route. Comparing these flows gives some expected results, for example, that people like to cycle along the river, as well as some unexpected results, for example, that some routes with cycling infrastructure are avoided by cyclists. This study proposes a practical approach that planners can use for cycling plans with new/emerging cycling data.
Highlights
Encouraging people to cycle rather than travel by motorised transport is seen as a way of achieving more sustainable urban environments
One intervention which has been deployed in many cities is improving cycling infrastructure e.g., segregated cycle lanes, bicycle parking etc
On links with cycling infrastructure, we found that in total, 8.2% more distance was travelled than was expected based on distance minimising behaviour
Summary
Encouraging people to cycle rather than travel by motorised transport is seen as a way of achieving more sustainable urban environments. Transport Scotland (Transport Scotland, 2017) have set a vision of “10% of everyday journeys to be made by bike, by 2020”. They note that cities will have to be the driver in achieving this ambitious aspiration. One intervention which has been deployed in many cities is improving cycling infrastructure e.g., segregated cycle lanes, bicycle parking etc. Such measures have generally been shown to have a positive effect, the evidence is somewhat mixed. Evaluation, can be difficult due to a lack of data (Hankey et al, 2012; Heesch and Langdon, 2016)
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