Oyster Flowprints

LivingCity-Slide10

Depiction of flows on the London Underground network during a typical weekday (top) and weekend (bottom)

We can use Oyster data to produce flowprints based on real flows of passengers on the London Transport network. Here we see the London Underground network. Along the top you see a typical weekday and along the bottom a weekend. The graph shows activity on the network, and you can see how weekday flows are characterised by the double-humped dynamic produced by commuting. The first hump, which peaks at about 8:40AM is far steeper than the second one, which peaks just after 6pm. This implies that Londoners all go to work at about the same time, but come home at a range of times between 17:30 and 19:00PM, presumably based on whether they go out for a pint after work, which Londoners are renowned for doing.

Along the bottom you see a Saturday which shows much lower use, but rises slowly till a peak around 6pm.

By visualising the flowprints throughout the day we can see them expand during rush hours, long tendrils reaching far up into London’s suburbs (or metroland), and then contract during the day, in which only really the central portion of the network is in use.

Watch the video.

Here you can see the london underground flowprint in action. Each trail is produced by an individual passenger touching in and out using their Oyster card. We construct a route for each journey based on a simple shortest path algorithm. This is necessary because we only have origin and destination data from the system.

Here we see the expansion of the flowprint as we reach morning rush hour, with these long tendrils heading northwards, the metropolitan, victoria, northern and central lines. London functions as a network of suburban villages and this underground network was designed to bring people into central london over a long distance.

We see the pulse of the city in this expansion/contraction movement. These diurnal patterns are the strongest signatures of the living city, in that they apply to most large cities in the world. We are analysing large-scale Oyster data sets at the moment, to help the transport authority understand how large groups of people react to disruption events. There are on average 200 simultaneous disruptions to the London transport network, so it’s always in an imperfect state.

 

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