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Graphing trends in Fremont bike trips by the hour and in the rain – UPDATED

Image from the SDOT Blog
Images from the SDOT Blog

It’s a kick to watch the Fremont Bridge Bike Counter tick up one notch as you pedal past it on your way across the Ship Canal. But the counter is also a source of never-before-seen real-time bicycle travel data in Seattle.

A gift of Cascade Bicycle Club and the Mark & Susan Torrance Foundation, the counter was placed at the Fremont Bridge in October because it is likely the busiest bicycling crossing in the city. An electronic loop in the sidewalk (similar to technology that detects vehicles at stoplights) counts bicycles on both sides of the bridge, and the results are displayed on the northwest side of the bridge and recorded for study and display online.

The graph above, posted on the SDOT Blog, is likely not very surprising to most bicycle commuters. In fact, it looks very much like travel patterns you would expect for driving and taking the bus: Spikes during the morning and evening commutes. That’s because bicycling is a serious mode of transportation.

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Perhaps more interesting is the somewhat wishy-washy effect of rain on bicycling volumes:


Most interestingly, it appears heavy rain in the morning can have a significant impact on the number of people biking, cutting volumes by a third or more compared to a dry day earlier in the same week.

However, once somebody is already out and about with their bike, they are very likely to continue biking even if it rains. Once the day is going, rain does little to affect cycling volumes.

In fact, you might expect rain volumes to have a very clear effect on bicycling numbers, but as you can see in the second graph, it’s not so straightforward. While weekend data might be messing with the results (for example, a dry weekend day would likely see lower traffic than a wet weekday, throwing off the graph), it’s still a bit surprising to see such a relatively weak connection between rain and bike traffic.

Notice anything else in the data (play around with the data at the bottom of this page)? Any other graphs your would like to see?

UPDATE: I made the following graph using the Tableau online graph embedded at the bottom of the SDOT counter webpage (you can also download the raw data here). By taking away weekends, this graph shows a much clearer view of the effect of rain on ridership numbers. Interestingly, the rain had little effect in January (perhaps, as X points out in the comments below, that’s because most people cycling then are hardcore enough to bike through anything). What do you think?

Screen Shot 2013-03-22 at 11.56.03 AM

UPDATE x2: Interestingly, temperature plays less of a dramatic role (because December is all screwy due to the holidays, I would ignore those figures):

Screen Shot 2013-03-22 at 12.05.27 PM

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17 responses to “Graphing trends in Fremont bike trips by the hour and in the rain – UPDATED”

  1. X

    The correlation between rain and ridership looks strong to me. Your graph is muddled because you haven’t selected weekdays. The data is also contaminated by holidays; people don’t bike very much on Christmas or Thanksgiving, but that tells us nothing about the rain-ridership issue.

    I would like to do a multivariate regression to control for cold weather. My hypothesis is that there are two populations: hardy SoBs who will ride no matter what and us wimpy folk who will drop out if it’s cold or rainy. In the winter, you’ve already knocked out most of the weather-averse population, so that could make the rain trend look weaker than it is. Is there some way to download the whole data set?

    1. Tom Fucoloro

      Good questions. Check out my update above. I made a new graph that took out Saturdays and Sundays. Definitely a clearer trend. And you’re also right that January has much less of a correlation, perhaps because people who are willing to bike through the cold are also likely to bike in the rain.

      You can download the raw data here: https://data.seattle.gov/Transportation/Fremont-Bridge-Hourly-Bicycle-Counts-by-Month-Octo/65db-xm6k

      1. Gary

        “take out weekends”

        In November, you can probably drop Thursday and Friday from Thanksgiving week, and in December 25 -> January 1st. As many folks have that whole week off, or if not, take the few days to make it a long vacation.

    2. Gary

      Freezing temperatures, ie Black ice definitely plays a role in whether I commute or not. After falling on black ice, if I see it at the end of my driveway, that’s it, no bicycling that day.

      On rain, once I’m at work, I’m riding home no questions asked.

  2. Breadbaker

    I can see some sociology theses coming out of this. For instance, notice how the graphs are nearly identical by time on Saturday and Sunday. What does that say about the religiousness of cyclists? Why is Friday always just a tad lower than the other days (and I personally skew that number the other way, since if I do bike to work in the winter, it’s on Friday)? Interesting graphs. It will be fun to see how it changes as weather gets drier and warmer and the days lengthen.

    1. Tom Fucoloro

      I wonder if the four-day work week accounts for a big part of the lower Friday numbers. Also, is telecommuting more popular on Fridays? Anybody have stats on this?

      1. Breadbaker

        That’s why a study would be interesting. Friday might also be date night and people don’t bike because they’re dressed differently. I don’t know, but there are plenty of possibilities to look at.

      2. Mike H

        One interesting way to see how these relate is to superimpose the graph of motor vehicle traffic. I think you would likely see some similar attributes. Fridays are never good days to get a count of vehicles because of people taking it off or people who may work different schedules to have the day off. The same is true for Mondays.

  3. Fridays (and Mondays) are known to have different travel patterns to the mid-week, for a whole host of reasons, including need to carry things to/from work, the aforementioned date night, etc.. This is why most traffic counts are done mid-week and avoid the odd days.

  4. ChefJoe

    So, 50,000 average bike crossings per month, divide by two to presume a round trip using the same route, divide by 30 for the days in a month. I calculate 833 (and a third) riders a day passing in front of this thing. Some week days it may be around 1,000 bicyclists.

  5. A

    Does the counter count both sides of the bridge or just the west one it’s next to? I go over the Fremont bridge a few times a week but only on the east side, riding clockwise around the lake. I have never actually ridden past the counter.

    1. Tom Fucoloro

      Both sides.

      1. A

        Cool, thanks

  6. Hey Tom,

    A quick note – you have a screen shot at the top of the article. You might consider posting the actual visualization. The Viz is interactive, meaning people can actually look at what’s in the viz, as well as download it and modify it to answer questions they have. The viz is located here: http://public.tableausoftware.com/shared/78JJN5559?:display_count=yes

    Full disclosure – this direct traffic to Tableau Public, a free site which I work on. I also helped get this particular visualization together a teeny-tiny bit, so I have a vested interest in more people seeing it. Thanks!

  7. […] The Fremont bike counter is only the first in a series of planned counters across the city. A detector in the sidewalk counts bicycles on both sides of the bridge, providing our first ever look at 24/7 bicycle ridership data. We dove into some of the data (like the effect of weather on cycling) here. […]

  8. […] bored and decides to ride circles around Fremont for a day or two, perhaps it’s time to start analyzing the data collected and see if we can identify some issues that can be addressed via programs, events and education […]

  9. […] increase could be skewed to the low end due to unusually heavy rains this winter and early spring. Analysis has shown that a morning rain will suppress a day’s bike counts. If any of you are into crunching data, […]

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