This study is intended to uncover differences and similarities in route preference between morning and evening bicycle commuter journeys as recorded by the mobile phone application, Strava, in 2013. The analysis was performed using the 2013 Strava Metro data for Glasgow City and visualizations were generated using the GIS software, QGIS. Important findings include a difference in route choice on Edinburgh Road and a similarity in overall network preference between AM and PM bicycle commuters.
The Strava data used in this study can be found in Glasgow’s Open Launchpad. The Open Launchpad is one component of the Open Glasgow initiative intended to act as the nexus between real people and, “the raw data generated by countless Glasgow stories.” The Open Launchpad acts as a clearinghouse for public information to allow deeper insight into the way Glasgow works. You can access the 2013 Strava Metro data for yourself here.
The variables used in the study include the AMCOMMUTEN and PMCOMMETN which represent corresponding AM and PM commute journeys. The AMCOMMUTEN data is defined as, “Total number of commute bike trips on the piece of street regardless of direction of travel for the rolled-up date frame between 7am – 10am.” PMCOMMETN data is defined as, “Total number of commute bike trips on the piece of street regardless of direction of travel for the rolled-up date frame between 4pm – 7pm.”
In the 2015 Strava Metro Comprehensive User Manual, Strava indicates that commuter journeys are derived using three methods.
- The first is the commute flag that is native to the Strava experience. [This means the user ticked the commute button]
- The second is an automated process that locates point-to-point cycling and pedestrian trips that are within duration and distance constraints.
- The third is fuzzy name matching from the activity titles
The open source geographical information software QGIS was used here to generate compelling visualizations allow for insight into the way Strava users rode in Glasgow in 2013.
AM Commute Bike Trips Logged by Strava Users in Glasgow in 2013
PM Commute Bike Trips Logged by Strava Users in Glasgow in 2013
Differences
- Edinburgh Road, on the east side, was preferred by AM users.
The images above highlight one difference in route preference between AM riders and PM riders. The images suggest that Strava users who cycled between 7AM and 10AM used Edinburgh Road more often than users who road between 4PM and 7PM. Without more information (such as specific bike counts), it seems that this corridor was more popular with morning riders than with those that cycled in the evening.
Similarities
- Overall, AM and PM Strava users preferred similar route network.
We may conclude that in 2013, Strava users preferred very similar routes on the Glasgow commute network for both morning and evening networks (apart from Edinburgh Road). This finding highlights how two different cycling commuting populations (however similar) prefer the same routes for cycling regardless of time of day. Further, many of these same routes are popular for overall Strava users.
In a recent post that used the STATS19 data, some of the most popular routes for commuters are also hot spots for bicycle crashes.These roads include, but are not limited to:
- Eglinton Road
- Victoria Road
- Paisley Road West
- Edmiston Road/Shieldhall Road
- Byres Road
The Glasgow City Council stated that it would like to increase bicycle mode share to 10% of all trips by 2020. Increasing bicycle mode share is a priority that is becoming more and more popular across the world as the benefits of cycling become clear (in regards to physical health, vehicle decongestion, and so on). Understanding what projects to prioritise for resource allocation is a question that can now be answered using big data analysis alongside traditional planning and monitoring techniques to explain and justify improvements in the level of service cities, such as Glasgow, offer for cycling. By improving the overall quality of infrastructure, cycling will become increasingly attractive to a wider audience of urban dwellers.
Urban Data Cyclist 