Geospatial Data Analysis and Simulation
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As part of the on-going ANTS project (Adaptive Networks for Complex Transport Systems) we’ve been tracking how many London buses are running during today’s bus strike. This is a very new development which we only just got working in time, so we don’t have any baseline data to compare against yet, but the two maps from this morning and lunchtime the day before show the geographical areas most affected by the strike.

Friday 22nd June 2012 09:00am showing the locations of buses as red markers with direction arrows

The above image shows all the buses running at 09:00am (BST) this morning when the strike was on. According to our data, there were 2,198 buses running in London at that time. We don’t yet have enough baseline data to compare this against, but by taking 13:21 (BST) on the previous day, we can say that there were 6,387 buses running then, giving 34% as a very rough guide.

Thursday 21 June 2012 13:18 (BST) showing the locations of buses as red markers with direction arrows

Comparing the two maps, it looks as though the worst affected area was the East of London. We can also show the density of buses using a heatmap:

Heatmap of bus locations for Friday 22nd June 2012 09:00 [Link to original map].

Following on from this, we also have data for the Underground, so this will enable us to analyse multi-modal flows and see how the bus strike has a knock-on effect on the tube.

Thanks to Steven Gray for drawing the bus icons as my original ones were useless.

The TfL Countdown API for buses was released a couple of weeks ago and I’ve been experimenting with it for the ANTS project so that we can add real-time bus tracking to the Tube, River Services and National Rail libraries. The ultimate aim of the ANTS project is to show how failures affect multi-modal flows, so integrating bus data into this system is the last major hurdle.

Buses on route 73, 17:22 on Monday 18th June 2012

The arrow icons need a bit of work as they are the ones I have been using to test the tube direction calculation code with, but they show which direction the bus is heading in quite effectively. In contrast to the tube data, the bus compass bearing is accessible from the API and seems to be taken directly from the GPS in the bus.

The position calculation is done by building up a passing points database of all the arrival times at bus stops as returned by the API. The aim was not to have to use any timetable data, or route data, so the locations can be calculated using only the data from the API. At the moment I’m only querying route 73 for debugging purposes, but the returned data contains the expected times of all buses for every stop along the route. This includes a unique trip code, a vehicle code and even the licence number of the actual bus. Unfortunately, the previous stop for a bus is missing, so we only know where the bus is heading, which makes interpolation along the route impossible. By building up the passing points database, we can query stopping points for the same bus route for a bus following behind the one we’re interested in and find out where it has come from. Then the position is a simple linear interpolation using the expected time, time now and run length for the link, also extracted from the passing points of a following service.