Dublin bikes revisited

Same idea, more data and more cities

At the time when I wrote my previous post, October 2009, I only had 7 weeks of data (about the length of time the scheme had then been running). I was pleased to discover that it was possible to learn a bit about Dubliners' bike habits even with this limited quantity of data. Nevertheless, I wanted to return to the idea when I had a bigger data-set.

It has taken a while but I have finally collected over a year of data for the Dublin bikes scheme as well as almost a year of data for similar bike schemes (also run by J.C.Decaux) in Brussels, Lyon, Paris and Seville.

Questioning the data

Bike usage by time of day

In the below charts we plot the average number of bikes in use as a function of time of day minus the number that were in use at midnight.

Brussels's physical geography leads to a rather inefficient allocation of the bikes as the day goes on (ie. stations at the top of hills are empty while those at the bottom are full), which can be a little frustrating.

Returning to the original idea of comparing different European cities' apparent/implied working hours, let us gather our data in a table:

	City	Morning peak (local time)	Evening peak (local time)	Time between peaks
	Brussels	08:45	None	N/A
	Dublin	08:50	17:45	8 hrs 55 mins
	Lyon	08:50	18:10	9 hrs 20 mins
	Paris	08:55	18:50	9 hrs 55 mins
	Seville	07:55	20:25	12 hrs 30 mins

So what do we make of that!? Certainly the data makes no suggestion that our European counterparts are slacking off with a shorter working week. Indeed Dublin comes in bottom of the four cities. The peak times are much less well defined for the other cities than for Dublin (this is because there are far more bikes and stations) but even allowing for this, there is no way this data suggests we work any harder here in Dublin (indeed it is hard not to conclude the opposite!). Of course this is an extremely crude method and shouldn't really be taken very seriously for a whole host of reasons but I still thought it would be a fun to see what I might find.

Two other brief points here are

1. The reason the graphs for the other cities, especially Paris and Brussels, are so noisy is that there are a great many more stations in these cities than there are in Dublin. As a result, and because it takes time to scrape the data from each station, I have a similar number of records per day for these cities as I do for Dublin but a much smaller number of snapshots of the entire city. (E.g., if we have 100 records between 10 stations we have 10 snapshots of the city but if we have 100 records between 50 stations, we only have 2 snapshots of the city). Fewer data-points means more noise.
2. There appear to be two peaks around lunchtime for Lyon. I do not know why this is the case and would be interested if anyone has an idea. (Perhaps it is a seasonal effect and people try to avoid the hottest sun in the summer? I could look for this in the data but I haven't bothered.)

Seasonality

Regarding Summer vs. Winter, the situation is mildly interesting. The peaks in our usage by time-of-day graph occur at exactly the time and the usage peaks to the same value in the morning. However usage peaks at a significantly smaller value later in the day for Winter than for Summer. My guess is that people prefer using the bikes in Summer but not so much that they are willing to be late for work! (So morning usage is unaffected by the weather but not so for afternoon/evening.)

Bike usage by station

Busyness rank	Station name	Busyness
40	Hardwicke street	10436
39	Parnell Square North	13651
38	Georges Quay	15947
37	Blessington Street	16722
36	Greek Street	18594
35	Fitzwilliam Square West	19516
34	Bolton Street	20301
33	Custom House	20442
32	Ormond Quay Upper	20794
31	Eccles Street	21521
30	Christchurch Place	21957
29	St. Stephen's Green East	22104
28	Golden Lane	23741
27	Leinster Street South	23997
26	Merrion Square West	24129
25	Dame Street	24400
24	Cathal Brugha Street	24824
23	Parnell Street	26293
22	Mountjoy Square West	26692
21	Jervis Street	26733
20	James Street East	26879
19	Molesworth Street	27536
18	Earlsfort Terrace	28074
17	High Street	28082
16	Townsend Street	28662
15	Merrion Square East	29301
14	St. Stephen's Green South	29408
13	Fownes Street Upper	29743
12	Talbot Street	31604
11	Wilton Terrace	32323
10	Portobello Harbour	32326
9	Charlemont Street	32999
8	Exchequer Street	34557
7	Princes Street / O'Connell Street	34765
6	Smithfield	34829
5	Grantham Street	35034
4	Chatham Street	36681
3	Herbert Place	39368
2	Pearse Street	39857
1	Custom House Quay	40035

Weather effects

• Rainfall (in mm)
• Sunshine (in hours)
• Minimum day's temperature (in degrees Celsius)
• Maximum day's temperature (in degrees Celsius)

At first I expected the rainfall data to have the strongest predictive power, however this turned out not to be the case. Thinking about it, I should not have been surprised. Firstly, I only have one point in the scatter plot per day and only just over a year of data, so only about 400 data-points. While it might seem surprising to those of us who live in Dublin, it did not actually rain at all on the vast majority of days so most of the data-set is wasted on dry days. Secondly, while I believe rain is a very strong disincentive for people to cycle it usually only rains for relatively brief periods so we would probably need rainfall data on an much finer timescale like every 5 or 10 minutes to see the effect clearly. Finally, we have rainfall data for Dublin Airport, not Dublin City Centre. In any case, below are the charts.

Further thoughts

Although I like the Dublin bike scheme, I can't help wondering if it is good value for money. It may well be, but I can't help wondering. It seems to me that there is a potential conflict of interest for the city councilors who make the decisions (the scheme is popular so they could be biased in favour of it, even at a bad price) or at least that the best decisions may not be made because of a possible asymmetric perception of opportunity cost relative to upfront cost by either the councilors of the public. A significant part of the payment for the scheme is in advertising revenue forgone by providing J.C.Decaux with free hoardings throughout the city. I would be interested to see the pricing detail. I have been told by those in the know that the keyword when buying such advertising space is the rate card and it seems that this website has some figures for that. I have not quite found the time to look into this in detail myself but would be interested to hear others' thoughts. I'm sure that a few FOI requests could turn up some interesting figures.

For my own part, I feel like I have spent more than enough time analysing bike schemes for now (indeed I had to force myself to write this post). However Dublin Bus are still promising that they will be providing GPS data on the location of their buses any day (they missed their own 2010 deadline). They claim to have a pilot scheme running on the 123 route and I did manage to find one stop with a so called real time passenger information (RTPI) display display on this route, but the display was blank. However in the last few weeks I have noticed the posts for several such displays appearing beside stops on Nassau Street so I expect this data may be on the way. I look forward to gathering and analysing it to see if I can detect any correlation between timetables and the movement of the buses.

A post-script on technical details

  import urllib, time, csv, sys, datetime, gzip
  from xml.dom import minidom

  URL_station_list = 'https://abo-%s.cyclocity.fr/service/carto'
  URL_data = 'https://abo-%s.cyclocity.fr/service/stationdetails/%d'

  def prev_day_s(d_s):
     d = datetime.date(int(d_s[:4]), int(d_s[4:6]), int(d_s[6:8]))
     d -= datetime.timedelta(days=1)
     return '%04d%02d%02d' % (d.year, d.month, d.day)

  def yyyymmdd_from_epoch(t):
     tm = time.localtime(t)
     return '%04d%02d%02d' % (tm.tm_year, tm.tm_mon, tm.tm_mday)

  def get_stations(city, date, get_file = True):
     fname = '%s/%s.stations.xml' % (city, date)
     if get_file:
        urllib.urlretrieve(URL_station_list % city, fname)
     stations = map(lambda x: int(x.getAttribute('number')), minidom.parse(fname).getElementsByTagName('marker'))
     assert len(stations) > 0 # Horrible hack to quickfix case when web serves up incorrect stations data.
     return sorted(stations)

  if len(sys.argv) != 5:
     sys.stderr.write('Usage: python %s <city> <run time> <main loop delay> <request delay>\n' % sys.argv[0])
     sys.exit(1)

  city = sys.argv[1]
  (run_time, main_delay, req_delay) = map(float, sys.argv[2:])

  start_epoch = time.time()
  date = yyyymmdd_from_epoch(start_epoch)
  try:
     stations = get_stations(city, date)
  except:
     stations = get_stations(city, prev_day_s(date), False)

  out = csv.writer(gzip.open('%s/%s.out.csv.gz' % (city, date), 'a'))
  err = csv.writer(gzip.open('%s/%s.err.csv.gz' % (city, date), 'a'))
  t = time.time()
  while t < start_epoch + run_time:
     for i in stations:
        t = time.time()
        try:
           out.writerow([i, t] + map(str.strip, urllib.urlopen(URL_data % (city, i))))
        except:
           err.writerow([i, t, sys.exc_info()[0], sys.exc_info()[1]])
        time.sleep(req_delay)
     time.sleep(main_delay)
  

I also thought it would be worth recording the summary statistics for my various data-sets. So here are a few that seem relevant:

A record consists of a snapshot of a station and as such consists of a station ID, a date/time-stamp and a number which is the number of bikes docked at that station at that date/time. (It would be extremely interesting to get hold of J.C.Decaux's data in which they can actually track individual bikes.)

Also, in case it is useful to somebody, here is the weather data page on Met Eireann and the data which I have weather.csv.