Tagged: mobility

bikesI love animating bikesharing systems, but without GPS data it looks like people travel in straight lines, from bike-out to bike-in. So to get a better idea of how cyclists really travel across the city, I wanted to investigate mapping multiple GPS tracks.

The first step is finding data. Strava has a huge repository of bike data, but you can’t access a trips’s GPS data unless you are connected to that person, or if they otherwise grant you access. And even then when you download the GPS data the GPX file doesn’t include timestamps. You can get an idea of the potential of Strava’s data from this “Beautiful Weekend” video made by BikeArlington using VeloViewer.

I decided to try to collect my own GPS data by asking a local monthly bike ride, the DC Bike Party, to record their outing and send me the data. Their April ride attracted 650 riders, but I got only 5 responses, and one of those I had to reject for not having timestamps. But four cyclists is good enough for an experiment to learn more about the process.

The next step was data munging. One participant sent me two separate GPX files, for before and after the break at the bar. It was easy enough to merge them by taking the trkpt tags from one file’s trkseg section and adding them to the other file’s trkseg section. Another participant’s GPX file wasn’t syncing up with the others. To correct it, I just manually edited the timestamps using a global search & replace for the hour field. » Continue Reading…

Metro PlacesWhat would the Washington, DC region look like if you never went further than 500 meters from a Metro station? Well, there’s an app for that! I was inspired by a car-free friend who pointed out the difficulty of finding a Metro-accessible dentist when moving to DC. So, let’s put the Internet to work to make that simpler.

I used the Places Library of the Google Maps API to discover dentist locations for a geographic region. To connect to Metro stations, I submitted a separate search for each Metro station. Of course, “dentist” is just one option for a type of place. The API has 96 Supported Place Types, from airport to zoo.

Try the Metro Places app to discover businesses near your favorite Metro station. To make your own search, select the type of place from the drop-down list, which station you want and how many stops you’re willing to travel (I assume no one wants to transfer), and how far you’re willing to walk from the end-station. You can display the results as a collection of icons or a heat map, or both. The icons returned are part of the Places API, such as a giant tooth for dentists, and a martini glass for bars. » Continue Reading…

Washington via WMATA

Should the bikeshare industry adopt an open data standard? As bikesharing spreads to more cities, having a common method for accessing and analyzing data will become more important. We know that transit systems work best when agencies concentrate on their core mission. Transit agencies are not in the information technology business; all they should do is release their data to let third parties build apps that let passengers use the systems.

To use open data, programmers need to know: Where is the data? What are the files called? Which fields are available? What are the fields called?

Bikesharing systems should adopt the standard of having a “data” page which can be found by appending “data” immediately after the main URL. This is what many U.S. government web sites are doing (like justice.gov/data, dot.gov/data, state.gov/data, etc.) It would be awesome to have consistent URLs like capitalbikeshare.com/data and velib.paris.fr/data. » Continue Reading…

You may visit Aspen, Colorado to go skiing, but how do you get around when visiting in the summer? We-Cycle has introduced bikesharing to this mountain town, giving residents and tourists a new option for getting around. They recently shared some trip history data with me, letting me create this short animation of how cyclists travel between the 13 stations.


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Bikesharing at a Ski Resort

The San Francisco region has joined the bikesharing movement, with the introduction of Bay Area Bikeshare in August 2013. I wanted to see if I could adapt any of my CaBi tools for the “BABS” system, but their open data is too limited to be of much use. They have a System Metrics page which offers only ridership and membership data, which is not very interesting. To analyze the system we need trip history data, like Capital Bikeshare shares every quarter.

Luckily, I discovered Eric Fischer, who has been tracking station statuses since late August. Every minute, he records the number of available bikes and docks at each station. While not as valuable as trip history data, this data does let us discover when stations are either full or empty.

The data he records is a copy of the current station data, available at bayareabikeshare.com/stations/json. I had to reduce the size of the file by writing a Java program to remove redundancy and unnecessary fields. Still, storing data for a single day takes a megabyte of space for even the condensed JSON file. » Continue Reading…

If you’re a total transit nerd, this will be exciting. To prepare for a bus-themed event for the Transportation Techies meetup group, we’re making public APC data sets. That’s automated passenger counter; electronic devices that measure people boarding and alighting. We’re sharing it in hopes that local programmers will use it to create visualizations of how people use the bus.

2013-09 Raw Stop Data.xlsx is from Arlington Transit. It has 12 columns and 20,460 rows (1.2MB). The data is for weekdays in September 2013. I’ve created a CSV version, 2013-09 Raw Stop Data.csv. Here’s what 3 sample rows looks like: » Continue Reading…

Force Diagram of WMATA Metro StationsWhat is the minimum information you need when planning a trip on the Metro system? If all you want to see is which stations are connected, the Force Diagram of WMATA Metro Stations is the Metro map for you.

This visualization was designed using the JavaScript library D3, which includes the Force Layout design. I was inspired to do a version for Washington, DC after seeing Muyueh Lee‘s visualization of the Taipei MRT system. You can click-and-drag stations to try to reposition them. The layout pays no attention to the geographic locations of the stations. The distribution starts off as a random mess, and then coalesces into positions based on simulating physical properties of the links between stations. This is an even-more-severe rendering than my isochronal Metro Distortion Map.

The code is relatively compact, and customizing it was a good way for me to learn D3. That’s the same tool I used to create the Voronoi Diagram of CaBi Stations and the interactive bar chart I used for Looking Back at 2013 CaBi Data.

A Bare-Minimum Metro Map

visualizerThe Trip Visualizer has been updated with new data for Capital Bikeshare. Instead of just posting the new quarter, I made all of 2013 a single data set. That’s over two-and-a-half million trips (2,585,010 bikeouts) using 309 stations, summarized into a big origin-destination table.

Montgomery County joined the network in late September, introducing bikesharing to four regions in Maryland: Rockville, Bethesda/Chevy Chase, Silver Spring, and Takoma Park. 73% of bikeouts from Maryland went to other stations in Maryland, with 26% headed to DC. Just under 0.5% (21 trips out of 4,675) went from Maryland to Virginia. The fastest Maryland-to-Virginia ride took 33 minutes, from Friendship Heights Metro to Rosslyn Metro, a trip that takes 27 minutes on Metro. The longest MD/VA trip was 1 hour and 48 minutes, when someone biked from Crystal City Metro to Battery Lane in Silver Spring.

The Trip Visualizer lets you select a single station to see the most-significant trips to/from that station. You can use some hidden features to select clusters of stations, to examine networks. Hitting “M” will select all stations in Maryland. You can see how isolated Rockville is, with its closest station to Bethesda still over five miles away. » Continue Reading…

When I saw Dan Macy’s aerial photo of Washington, DC, I knew I had to turn it into the background for a map. Dan’s plane was flying into National Airport from New York City, and made an unusual entry over the Anacostia River, giving him a spectacular view of East Capitol Street, the Anacostia and Potomac Rivers, and the National Mall.

I’d been looking at the 2013 data from Capital Bikeshare, and made a little app that shows you the total number of bikeouts from each station, the Birds-eye view of CaBi stations. (A bikeout means someone rented a bike from that station.) The program was mostly an excuse to play with JavaScript and SVG graphics, without using a mapping or graphics library.

I had to figure out a not-too-complex way of mapping the latitude and longitude coordinates to the oblique photo. Unlike a map, the meridians (longitudes) aren’t vertical and the parallels (latitudes) aren’t horizontal. The bird’s-eye perspective also meant the meridians and parallels would be skewed. » Continue Reading…

Want to look back at 2013 using Capital Bikeshare data? I’ve put together an interactive tool to examine the 2013 daily ridership statistics for Capital Bikeshare. The data looks at daily “bikeout” totals, that is, how many bikes were checked out each day. You can summarize the data into weeks, months, quarters, and days of the week. The weekly view ignores December 31, in order to avoid having a 53rd week with only a single day in it.

You can compare the difference between bikeouts from subscribers (those with memberships for a month or a year) and casual riders (those with memberships for 1 or 3 days). You can also look at bikeout stats for any of the 306 individuals stations.

The program lets you find the correlation between any two data sets. You can use a second data set to color the bars of the first data set. The correlation is automatically calculated. It ranges from 1, a perfect positive correlation, to -1, a perfect negative correlation. 0 means there is no correlation. » Continue Reading…

Looking Back at 2013 CaBi Data