Better Know A Bike Counter: the Burke-Gilman near Sand Point

Remember Stephen Colbert’s thrilling 434-part series? This is just like that, only with Seattle area bicycle counters. Once upon a time I wrote about the Fremont Bridge bike counter at the Seattle Bike Blog. Now SDOT publishes data from nine bike counters, and the plan is to run through them one at a time, breaking down the trends and complaining about the poor quality of Seattle Open Data

I thought that I would start with the Burke-Gilman Trail near Magnuson Park in honor of the recent opening of the Montlake bike/walk bridge. The completion of Burke-Gilman construction through the UW will certainly be a relief for a lot of people. As the Seattle Bike Blog says, “With the addition of the bridge, biking and walking in the area will be essentially unrecognizable from just a few years ago.” After checking it out this week, I completely agree! Now let’s head a few miles northeast and check out the measured bicycle traffic along the Burke-Gilman trail near Magnuson Park.


Below is a Google Maps image of northeast Seattle with bicycle routes highlighted in green. Allegedly the counter is somewhere around NE 70th Street near Sand Point, which is a few miles from the UW and the Montlake Triangle mentioned above. I don’t know enough about how the counters work, but at the very least there’s no obvious visible sign such as the totems at the Fremont and Spokane St bridges, which is why I’m not sure about the exact location. The general location is marked on the map.


This bicycle counter started reporting data on January 1, 2014. It wouldn’t be a Seattle Open Data project, though, without data quality problems. The bicycle trip records are missing for June 2015 and dubious for May 2015. For example, on May 17, 2015, the counter recorded 2078 northbound bike trips and 4 southbound bike trips. This seems highly unlikely. Throughout basically the rest of the data the northbound and southbound trips roughly balance by day, so it would suggest that the extreme imbalances in May 2015 represent data errors. Because the total counts look plausible, though, I’ve left in the daily totals from May 2015 for this analysis, and chalked that up to an apportionment error.

Continue reading “Better Know A Bike Counter: the Burke-Gilman near Sand Point”

Boomtown Seattle

Paul Allen and monolithic development corporation Vulcan are evidently constructing an apartment building near where I work. The Puget Sound Business Journal has details and a rendering from Runberg. Apparently the lot once contained the Computer Center Corp where Bill Gates and Paul Allen coded as youngsters. There were two junky old buildings on that lot — one brick and one wood — and I assume that one of those contained the computer center. Earlier this summer chain link appeared around the parking lot and an excavator demolished both with surprising speed.

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Herbicide: Trees on the Street

I’d like to introduce a topic: street trees! At the perennially amusing, under the “Transportation” category, lies a dataset titled ‘Trees in the Public Right of Way Diameter >= 40″‘. As an admitted tree enthusiast this sounded tremendously exciting (treemendously exciting?). Let’s take a look at where the large street trees supposedly reside, with a little help from the Google Maps API.


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Ferry Curious about WSDOT Sailing Times

A summary of this analysis was published on the Seattle Transit Blog, but here is a lengthier version of the write-up. Data, analysis, and writing by Zach Stednick and Mike Logsdon.


Although coverage of transit often revolves around Metro or Sound Transit, we wanted to shift the focus a bit to look at another method – ferries. It seems that the majority of the coverage of WSDOT ferries focuses on the fiscal side such as costs of ferry maintenance and replacement as well was critical examinations of ferry employee salaries. We wanted to change the conversation slightly and focus on the logistical aspect of the ferry system. Are the ferries generally on time? Are certain routes or vessels unusually punctual or unusually late? Are some days or times better or worse than others? To achieve this we filed a Public Records Request to gain access to this data. Here we present findings for on-time rates for calendar year 2014, where we specifically investigated the difference between actual and scheduled departure times. For simplicity this report considers only Puget Sound ferry routes. This work considers the data in an exploratory fashion – investigating patterns in the data but not considering statistical models or prediction algorithms.


Overall we find broad reliability of the WSDOT Puget Sound ferries, with a few minor trouble spots. Across the approximately 133,000 sailings throughout Puget Sound in 2014, average departure occurred 2.9 minutes after the scheduled departure. The three legs of the Fauntleroy – Southworth – Vashon triangle showed the lowest reliability, with average departure occurring 3.9 minutes after scheduled departure, a minute slower than the system average. Those routes experienced delays of at least five minutes on approximately one quarter of all sailings. The most punctual routes, in order, were Point Defiance – Tahlequah, Mukilteo – Clinton, and Edmonds – Kingston. Those routes were delayed approximately two minutes on average, and left within five minutes of schedule on 89 – 95% of sailings. While minor delays of five or ten minutes were somewhat common (15% of all sailings were delayed by at least five minutes), longer delays proved extremely rare. Only 19 out of the 133,158 sailings we investigated were delayed by more than an hour.

In addition to route, we also explored trends by vessel, month, weekday, and time of day. Not surprisingly, there was qualitatively a strong link between well-perfoming vessels and well-performing routes, and vice versa, and attempting to pull apart features of the route and dock from features of the vessel was beyond the scope of this write-up. The monthly pattern was also fairly intuitive, with the largest delays occurring in July and August around five minutes per sailing, and the smallest delays occurring in the depth of winter around two minutes per sailing. By day of week the delays peaked on Friday and Saturday at over three minutes per sailing, and were the lowest on Mondays at 2.4 minutes per sailing. Finally, by time of day, morning sailings were most reliable, followed by nighttime sailings, then daytime and evening sailings.

Preliminaries – Data Oddities

As is the custom, daylight savings time tangles up the recording a few hours of the year. For example, a sailing from Seattle to Bainbridge island was scheduled for 2:10 AM on the morning of November 2, 2014, right at the end of daylight savings time. The actual departure time was recorded at 1:11 AM. This likely reflects the manner in which sailing times were reported during the daylight savings switch, rather than an instance of a boat leaving one hour ahead of schedule. For simplicity the small number of late night sailings at the margin of daylight savings time were excluded from the analysis.

In addition there were several records of boats leaving inexplicably early. The most extreme was a sailing from Vashon to Fauntleroy, scheduled for 8AM on the morning of April 26, 2014. The actual departure time was recorded at 1:21 AM, over six hours before the scheduled sailing. These records were attributed as data entry errors and also excluded from analysis. (Wouldn’t you be disgusted to show up to the dock at 7:30 AM, only to be told that the 8AM boat already left… in the middle of the night?)

Routes and Vessels

Of primary interest is a look into whether certain routes or vessels are exceptionally reliable or frequently delayed. It’s a bit tricky to separate routes and vessels, as they are intertwined by scheduling, and either one could plausibly influence on-time rates. One could imagine logistical complications from either a high-traffic route, or poorly functioning vessel. Table 1 shows the number of sailings by route and by vessel for 2014. You can click on the table to bring up a full-sized version in a new tab.


Continue reading “Ferry Curious about WSDOT Sailing Times”

We made a fucking R package, MacGruber!

Welcome to the world’s best and most succinct R package. Have you ever been sitting at your computer, maybe with a spreadsheet open, maybe crunching some numbers, and thought, gee I sure wish I could make this computer randomly print lines from the script to the feature film MacGruber… Maybe it’s 2:30 PM, time for a cup of coffee, and you just want something a bit more entertaining than the afternoon news roundup. I’ve certainly been there, and am here to help.


We’re not sure if this will get past Ripley onto CRAN, with his totally capricious editorial decisions, but I assure you that all four lines of code work exactly as intended. Subtext: Rstudio may have lowered the barrier to entry for creating R packages a little too much.


The Golden State Warriors won the NBA Championship last night (Go Cougs! Klay Thompson!!). I’ve been crunching the numbers and determined that it’s because they have the #MambaMentality. (Also it’s because Steph Curry can get off a quality 3 from inside a phone booth, Golden State plays with a preposterous amount of depth, and the injury stricken Cavs were down to such notables as Matthew Dellavedova, whose talents include hustle, errant lobs, and looking more like a media member than a professional basketball player.)

You can always #countonkobe for wisdom. The man is NOT soft like Charmin

You can always #countonkobe for wisdom. The man is NOT soft like Charmin