A quick look at three years of commuting to UCL

A bit of fun

I love cycling and running, and around this time three years ago I purchased my first GPS watch to use as a training aid. As most who have purchased such a device will know, once you start using it you very soon start recording everything you do, even if it’s just commuting to and from work or going to the shops. If it isn’t recorded it didn’t happen, right?

Since I started using one, the popularity of GPS trackers has grown massively and a huge number of apps have been designed to cater for this demand including MapMyFitness, Endomondo and Strava to name a few. These apps, Strava in particular, have created a whole new social phenomenon whereby people can compete against one other to get the fastest time on particular ‘segments’ and earn the Kudos of being K or QOM (King or Queen of the Mountain).

What I find most interesting about this phenomenon is the vast amount of data that is being collected and stored on cyclists’ mobility patterns. As is the case with me, many people now track their daily commute by bicycle as a matter of course. This creates significant opportunities to research cycling commuting behaviour at the aggregate level.

There is a general feeling, I believe, that cyclists journey times are not affected by vehicular traffic. If there is a queue, a cyclist can just go up the inside, or overtake and bypass the queue completely. However, anyone who cycles often in London will know that it is often not that simple. London is an old city with narrow roads, and frequently it can be too dangerous to bypass traffic, or there is simply not enough space. This means that there can be considerable variation in the time it takes to commute the same route by bicycle.

As a start, I wanted to analyse this quantitatively by looking at my own tracks. The first thing I did was to plot the durations of the activities against their distances, which you can see below. I thought it was quite interesting that I was able to qualitatively identify different activity types visually. For example, the two clusters of points arranged horizontally are commutes between UCL and my current home and previous home.

A quick view of a few years of activity data


The smaller cluster at 5000 metres and just over 20 minutes is the Wimbledon Common Park Run, a weekly 5k race that I do often. You can also clearly see the two distinct profiles for running and cycling activities.

What is interesting about the commuting activities is their horizontal extent on the plot. With a cursory glance, the mean commuting time is approximately 40 minutes for my old home location and 45 for the new one, but there is considerable variation around this. There are many reasons why commuting times may vary like this, including level of effort, wind direction, precipitation, traffic congestion, cycle congestion (i.e. the number of cyclists occupying the space available for cyclists), the precise time at which the activity started and ended, and variations in the route amongst others. In future work, I hope to look in more detail into how to isolate these effects.



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