subway Archives

December 27, 2006

15K people per hour, by transport mode

From Vuchic's Transportation for Livable Cities, what does it take to move 15,000 people/hour using different modes of transport:

The remarkable thing is that the 7 lanes of freeway in each direction have zero extra capacity at that bandwidth, while the single track of rail rapid transit has, theoretically, at least another 66% to spare (see whole table).

Origin and Destination Estimation In New York City with Automated Fare System Data

[Syndicated from CiteULike: fruminator's library]

Transportation Research Record, Vol. 1817 (2002), pp. 183-187.

New York City Transit's automated fare collection system, known as MetroCard, is an entry-only system that records the serial number of the MetroCard and the time and location (subway turnstile or bus number) of each use. A methodology that estimates station-to-station origin and destination (O-D) trip tables by using this MetroCard information is described. The key is to determine the sequence of trips made throughout a day on each MetroCard. This is accomplished by sorting the MetroCard information by serial number and time and then extracting, for each MetroCard, the sequence of the trips and the station used at the origin of each trip. A set of straightforward algorithms is applied to each set of MetroCard trips to infer a destination station for each origin station. The algorithms are based on two primary assumptions. First, a high percentage of riders return to the destination station of their previous trip to begin their next trip. Second, a high percentage of riders end their last trip of the day at the station where they began their first trip of the day. These assumptions were tested by using travel diary information collected by the New York Metropolitan Transportation Council. This diary information confirmed that both assumptions are correct for a high percentage (90%) of subway users. The output was further validated by comparing inferred destination totals to station exit counts by time of day and by estimating peak load point passenger volumes by using a trip assignment model. The major applications of this project are to describe travel patterns for service planning and to create O-D trip tables as input to a trip assignment model. The trip assignment model is used to determine passenger volumes on trains at peak load points and other locations by using a subway network coded with existing or modified service. These passenger volumes are used for service planning and scheduling and to quantify travel patterns. This methodology eliminates the need for periodic systemwide O-D surveys that are costly and time-consuming. The new method requires no surveying and eliminates sources of response bias, such as low response rates for certain demographic groups. The MetroCard market share is currently 80% and increasing. MetroCard data are available continuously 365 days a year, which allows O-D data estimation to be repeated for multiple days to improve accuracy or to account for seasonality.

May 7, 2009

Spark it Up

This post is literally 2 years in the making. In the Spring of 2007, Jeff "You Don't Mess With the" Zupan gave me a spreadsheet with the annual 'registrations' (i.e. recorded entries) at each station in the NYC subway system going back to the beginning (1905). At the time, I was heavy into the new open source geo stack, as is reflected in the main piece of work I did at RPA. Hammer in hand, I of course saw this spreadsheet as a bucket of nails.

The result, after much whacking, is, I think, compelling, but you'll have to see for yourself. The general idea it that the history of subway ridership tells a story about the history of a neighborhood that is much richer than the overall trend. An example, below, shows the wild comeback of inner Williamsburg, and how the growth decays at each successive stop away from Manhattan on the L train:

This is somewhat in contrast to the South Bronx, which is yet to see the resurgence in ridership, other than at Yankee Stadium and the Grand Concourse:

The stations around Wall Street tell a totally different story, in which the ups and downs of each dep/recession have more immediate but temporary effects:

My first stab at visualizing this data was a traditional cartographic approach, showing the overall growth from 1977 to 2006 at each station. This told an approximate story at the level of the whole the city, but did not leave much room for detailed exploration. Thanks to geoserver's awesome new(ish) dynamic symbolizers functionality, it was trivial to plot the station-by-station time series sparklines (generated in R of course) onto the interactive online map. (Originally I the plots were produced in Perl and placed onto the map with a Javascript WFS layer, but that is so 2005.)

For all this, I never really felt like this little experiment was ready for an audience. That all changed when OpenGeo put up its Open StreetMap base layer for the web, giving fancy overlays like this one the context they need.


At least 2 people have taken the data I put out there and used it to make some zippier interactive flash apps:

  • The first is very polished, but I think the designer is quite misled in his desire to not plot dots on a map, and thus to plot what looks like a network flow diagram but with totally bogus data
  • The second is a little rougher around the edges, but I'd say is much more honest, and thus useful

Not sure if anyone knows, but I also have GIS files for the subway here:

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