Metro is coordinating with other regional agencies to release a single data file that will contain schedule data for all transit operators in the Washington DC Metropolitan Area.
Over 10 years ago, Metro began coordinating with local bus operators and commuter rail agencies to incorporate all of their transit schedules into wmata.com Trip Planner. It took some time and effort, but eventually Metro reached agreements with all the operators in the region and began to consolidate transit schedules in one online, searchable data source. In fact, Metro’s Trip Planner is the most comprehensive online data source for regional transit trip planning. So much so, that when the Transportation Planning Board (TPB) needs to update their four-step travel demand model they request all of the region’s transit schedules from Metro and we deliver them as a General Transit Feed Specification (GTFS) file.
Sites and app developers can load one data file for all the region’s transit instead of downloading separate files for each agency.
Only some agencies in the region publish their own GTFS files, and releasing this file will make several agencies’ schedule data available online for the first time.
Over the past two years, Metro staff have worked to negotiate the release of this GTFS file. We were pleased reach out to the other regional operators in July requesting sign-off on a regional data-sharing agreement that would permit Metro to release the other agencies’ data online in this GTFS format. We are excitedly awaiting executed agreements from the operators, and we’ve received one back already, thanks RideOn! Once we have received a few more replies, we will begin to publish a regional file including the data of all agencies that have executed the agreement.
In the meantime, feel free to contact your local bus, commuter bus or commuter rail operator and ask that they expedite the signing of this regional transit schedule data sharing agreement.
May 2013 and 2014 Metrorail ridership data is available: what patterns do you see?
Following up on our last data download of rail ridership from May 2012, 2013 and 2014 are now available. These data now represent three “snapshots” in time of rail ridership, at a very fine level of detail. This data can help answer questions, such as: where is ridership growth the strongest? Which destinations are becoming more or less popular? How has off-peak vs. peak ridership changed?
Improving pedestrian connectivity takes cars off the road at a formidable clip – rivaling the power of all of the region’s planned roadway additions and “last mile” transit connections. Cheaply and quickly.
The data is finally in, and we now know that walkable station areas result in fewer motorized trips, fewer miles driven, fewer cars owned, and fewer hours spent traveling. And when we improve the pedestrian and bicycle access and connectivity to Metrorail station areas, ridership goes up, putting a major dent in congestion by taking trips off the roadways. Earlier, we discussed what it means to build walkable station areas and research shows the tremendous benefits to the region of making this a priority.
First, our data confirms that when walking access to transit is improved, transit ridership goes up – way up. In the 2040 Regional Transit System Plan (RTSP), we stress tested TPB’s transportation model to improve walkability to the transit network and saw huge increases in transit linked trips. These trips go up by about 10% region-wide and we get an increase in transit mode share for all regional trips by 0.5%. That’s over and above the roughly one percent increase in mode share we anticipate occurring as a result of building the entirety of the CLRP, an impact about half that of constructing all of that transit.
In general, Metrorail ridership increases on average by 7% on weekdays and up to 53% on Saturdays during the festival. On days with nice weather, ridership has increased up to 10% on weekdays and 70% on Saturdays!
As the figure below shows, during the weekdays there is no impact in the morning, a large (21%) increase of activity during the mid day and then a 7% increase thereafter.
Saturdays are another story all together. Ridership increases up to 63% during mid day and afternoon periods on days during the festival, with a total ridership increase above 50%. Even morning and “late” night ridership increases significantly during this period.
Metrorail system entries by quarter-hour interval, Regular Weekday, Cherry Blossom Weekday, Regular Saturday and Cherry Blossom Saturday. Click chart for larger version.
When looking at change in ridership by station in the maps below, some obvious conclusions can be drawn. Read more…
I was invited to present a wide variety of data visualizations featured on the blog at a recent meeting of transportation techies.
I had the honor of being invited to present at the 2nd meeting of the Transportation Techies Meetup group, Metro Hack Night on January 2, 2014. I used this opportunity to illustrate some of the data visualizations I’ve developed using Metro data and talk a bit about the technology behind them.
Proximity to transit, especially high-quality, frequent, high-capacity rail, increases property values, attracts development and provides mobility choices. Property values are higher near Metro’s high-quality, high-frequency, high-capacity services, and deliver an incremental increase in total tax revenue to the Compact jurisdictions.
Property taxes on land around Metrorail stations generate $3.1 billion annually in revenues to the jurisdictions.
Of these revenues, $224 million is extra value that would not exist without Metro. This amount is equivalent to providing the following public services.
PlanItMetro saw great interest in our last full data download of O/D data last year. We thought we’d provide an update with data from October of 2012. This enhanced data download contains the following files:
Full O/D trip data MS Access, including service type (weekday, Saturday, Sunday Special), travel period (AM Peak, mid-day, etc.), entry hour, Origin Station, Destination Station, rider class (full fare or discounted), media type (SmarTrip vs paper farecard), fare instrument type (stored value vs benefits vs pass), average travel time and average number of trips.
86 MB compressed zip file containing an MS Access Database
Metro, in conjunction with Traffax, Inc., recently hosted a Bluetooth traffic monitoring test at Fort Totten station. Bluetooth technology has been used for years now, for monitoring vehicular traffic. Specifically, it has been used to provide travel time and origin-destination data, mostly in vehicular settings. Some pedestrian monitoring has been tested as well.
For Metro’s recent pilot, Bluetooth traffic monitoring was used to study pedestrian movements within a multi-level environment. The hope was that the Bluetooth data captured could tell stories about pedestrian flow within the station (including vertical movement), the train dwell times, train volumes, and the transfer rate between the Yellow/Green and Red lines. The latter is an area that WMATA is most interested in, since it is difficult to predict how people will ride Metro when given options. In this case, do people prefer transferring between Red and Yellow/Green at Fort Totten or at Gallery Place? This kind of data would make it easier for WMATA’s planning staff to better serve its customers by understanding true crowding levels on trains at peak load points.
It was estimated that 1 in 20 passengers’ movements would be captured in the pilot. The Bluetooth data sensors were placed in backpacks that Traffax employees were wearing. This initial data collection test will be used primarily to develop appropriate methods for analyzing such data, and to see what potential the data has for WMATA.
Metro planning staff understand that a picture is worth at least a thousand words, and often more. (And that a video is worth 1000 * 30 words per second.) As such, we are always looking to increase our ability to create compelling graphs, charts and video simulations.
Sample image of the Metrorail Ridership Visualization. Click the image to open the viz in a new window.
Each year is a horizontal stripe, sectioned off into months that go across. Both years and months are labeled.
The days within each month are transposed, so start at the left and read down, then move right. In the sample image, the leftmost column of January 2004 is the first week, with Thursday January 1 being the darkest red square. A graphical example is also displayed in the legend at the top of the visualization.
Each day is colored according to the ridership on that day, with darkest red being the smallest range (0 to 99,999) and the darkest green being the highest range (greater than 1,000,000).
If you move your mouse pointer over any individual day, a small “tool tip” appears showing the date and the ridership for that day, rounded to the nearest 1,000.