How do car-dominated streets inform how people use bikeshare? How can we estimate the purpose behind certain bikeshare trips?
New tools to help planners and the community answer some of these daunting questions, as well as to best visualize Capital Bikeshare usage across the Washington, D.C., region, debuted at the recent “CaBi Hack Night” installment of the Mobility Lab-sponsored Transportation Techies meetup group, hosted at WeWork’s new Crystal City location.
Stress-testing streets
A revealing presentation from Stephen Tu and Alex Rixey of the Montgomery County, Md., Planning Department and Fehr & Peers respectively, showcased a bicycling stress map of Montgomery County’s streets and sought to determine how these traffic stress levels affect connectivity to Metro and bikeshare stations.
Tu and Rixey’s stress levels stem from the four types of potential cyclists and their corresponding levels of confidence: “strong and fearless,”“enthused and confident,” “interested but concerned” and “no way, no how.”
This view of the stress map shows major gaps in low-stress routes (green and blue) around Bethesda, Md.
Following these levels, Tu and Rixey assigned streets a stress level from one to four, one being most comfortable and four being highly stressful, based on factors such as levels of traffic, speeds, and availability of protective bike infrastructure. Under this breakdown, only “strong and fearless” riders would feel comfortable on the stressful level-four streets.
While their analysis is still ongoing, the two discussed how bikeshare stations are often located on streets around the most confident and strong cyclists who can deal with higher stress roads (about 12 percent of potential riders) rather than those who are “interested but concerned,” about 51 percent of potential cyclists.
Tu and Rixey’s stress and connectivity map, on which users can adjust their own level of bicycling confidence, exemplified how the county’s bike network effectively disappears for riders who would only ride on low-stress roads. It is even impossible to travel between certain bikeshare stations without traversing stressful (levels three-to-four) roads, and many other station pairs require routes at least twice as long in order to stick to low-stress streets. As an individual’s stress tolerance drops, so does the feasibility of much of Capital Bikeshare – and by extension, bicycling – in Montgomery County.
However, Tu and Rixey pointed out that there are pockets of highly ridable streets throughout Montgomery County that, with only one or two low-stress connections, could dramatically expand bicyclists’ accessibility and connectivity. Better connections like these are vital to promoting the growth of bikeshare and bicycling as reliable transportation options, and maps like Tu and Rixey’s can be immensely useful tools for planners to best improve this network and make it accessible to more riders.
The stress-level analysis is still ongoing as the county identifies its bicycling infrastructure and connectivity priorities.
Everything you could ever want to track
Software engineer David Erickson showcased his Kibana dashboard, which offers a highly granular level of insight into patterns of Capital Bikeshare use. The visualization dashboard exemplified how possible it is to dig deeply into Capital Bikeshare’s open data by calculating average trip durations, and the most popular stations. One example even examined trip starts down to the minute over the course of four years, and further broke it down between casual and registered users.
Erickson also explained his dashboard’s ability to seek explanations for anomalies of bikeshare usage, which he calls the “surprise factor.” Dips in overall ridership are visible on federal holidays and even at certain times of those days, such as during the Fourth of July fireworks. The display dug deeper and was able to pick out home games at Nationals Stadium based on spikes in usage from stations near the ballpark.
The where and the why
Other presentations throughout the night showcased Capital Bikeshare data’s accessibility and the potential for citizen participation in creating useful tools from their information.
Georgetown Professor Hans Engler’s project looked into one of the most crucial questions for bikeshare researchers by trying to extract patterns in time and space – the purpose behind usage trends. In his visualizations posted at the Shiny Apps website, produced with former students, Engler determined the overall pattern of bikeshare rides with trip histories at various times during the day.
The station at 22nd and I St NW, for example, shows a number of off-peak trips (in green, below) that are more likely to be GW students.
Bikeshare ridership clusters.
From this, he developed different clusters of trip purposes based on the morning, midday, evening, and late night (above), and found that 85 to 90 percent of rides are easily classified into these categories. Not surprisingly, most trips appeared to be commuting to or from work, and the directions of trips from many stations supplemented gaps in existing transit, such riders from Union Station traveling east-west.
Anna Petrone unveiled a station-popularity visualization she had made using Amazon Web Services and Leaflet, offering a how-to for others looking to draw on the tool’s processing power for their own data projects. In this case, Petrone cataloged the most popular bikeshare stations at any given time of day.
Transportation Techies’ own host, Michael Schade rounded out the evening by presenting his “Day with No Metro” map, which explores how the Metro shutdown in March affected Capital Bikeshare usage, compared to the Wednesday prior. Since conclusions are difficult to draw over just one day of data, Schade explained he made the map available for people to figure out what stories can be told with it. From this, it’s possible to hypothesize a number of conceivable explanations for increases and decreases in ridership: an exploration-in-miniature of the possibilities granted by Capital Bikeshare’s open data.
Additional links:
- District Ninja determined the neighborhoods with the most bikeshare use, and their post includes a number of insightful graphs and visualizations.
- David Erickson wrote up his bikeshare usage anomaly method on his blog.
- Read about Daniel Gohlke’s presentation of the new standard of open bikeshare data, GBFS.
Photo, top: A man rides Capital Bikeshare in a major Silver Spring, Md., thoroughfare (Dan Reed, Flickr, Creative Commons). Tu and Rixey presenting at Transportation Techies (M.V. Jantzen, Flickr, Creative Commons).
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