The evaluation of large cycling infrastructure investments in Glasgow using crowdsourced cycle data

Abstract

The benefits of cycling have been well established for several decades. It can improve public health and make cities more active and environmentally friendly. Due to the significant net benefits, many local governments in Scotland have promoted cycling. Glasgow City Council constructed four significant pieces of cycling infrastructure between 2013 and 2015, partly in preparation for the 2014 Commonwealth Games and partly to encourage cycling more generally. This required substantial capital investment. However, the effectiveness of these big new infrastructure investments has not been well examined, mostly due to data limitations. In this study, we utilised data from the activity tracking app Strava for the years 2013-2016 and fixed effects panel data regression models to examine whether the new cycling infrastructure has increased cycling volumes on these routes. Our results show that three of the infrastructure projects have a positive effect on the monthly total volume of cycling trips made by users of the app, with flows up by around 12% to 18%. Although this result is promising, it needs to be interpreted with care due to the characteristics of the data.

Accounting for Spatial Heterogeneity Using Crowdsourced Data

“…Previous AT-related studies have primarily employed traditional data sources such as cordon counts and non-spatial regression model techniques such as the Poisson (Hong, McArthur, and Livingston 2020;C. Chen et al 2020), mixed logit (Kang and Fricker 2013;Lind, Honey-Rosés, and Corbera 2020), negative binomial (NB) (C. Chen et al 2020;Raihan et al 2019) and ordinary least squares (OLS) (Hong, McArthur, and Stewart 2020;Boss et al 2018) models.…”

Crowdsourced data for bicycling research and practice

“…However, implementation of bias correction requires advanced programming and statistical expertise. The uses of these data are just beginning to be understood and patterns in data have potential to help stratify count programmes (Brum-Bastos, and monitor change (Boss et al, 2018;Hong, McArthur, & Livingston, 2019). At present, cell phone GPS is not accurate enough to pick up sidewalk riding (Wergin & Buehler, 2017) and using GPS/accelerometry data to differentiate multimodal trips is problematic (Brondeel et al, 2015).…”

Predicting cycling volumes using crowdsourced activity data

“…It shows how the method can be used to detect popular and unpopular routes, and what factors might influence cycling route choices. Hong et al (2019) consider how Strava data can be put into an econometric framework to evaluate the effect of new infrastructure on cycling volumes. How to robustly estimate these effects is the paper's main focus.…”

“…While the paper uses Strava data, it mostly utilises the Strava trip counts at a small geographical level (output area) to measure cycling activity rather than the link counts used in our present study. Hong et al (2019) do not consider what other factors might influence the strength of the relationship between counts of Strava cyclists and total counts. Our study considers how geography (used as a proxy for sociodemographic characteristics) and time period might influence the strength of the correlation.…”

“…Despite this, such data have been used to study areas such as routing and navigation (Hendawi et al, 2013;Keler and Mazimpaka, 2016;Prandi et al, 2014), disaster management, wheelchair routing and health (Griffin and Jiao, 2015). Importantly, for our purposes, the data have also been used to understand cycling (Boss et al, 2018;Hong et al, 2019;McArthur and Hong, 2019;Sun et al, 2017).…”