Seattle DOT Case Study

Feb 13, 2017•Case Study

This story originally appeared as a case study on the Strava Metro Website.

BACKGROUND

In 2007, the city of Seattle created its first Bicycle Master Plan, and it laid out an aggressive goal: to triple ridership in 10 years. The challenges for the Department of Transportation were two fold: accurately measuring bicycling activity on Seattle’s network of roads and bike paths, and making the city’s streets more inviting to bicyclists.

At first, Seattle performed a manual bike count for a two-hour period in the downtown area on the same day each year, and consulted the American Community Survey for statistics on bike commuters. After a few years, the city added spot counts at 50 locations, and then permanent counters at a dozen other intersections.

In 2015, the city incorporated Strava Metro data into its quiver. “What we’ve really focused on is combining our count data with Strava to give us a broader picture of what’s happening with cycling across the city,” said Craig Moore, who manages the traffic data and records group for SDOT. “The combination has really proved valuable because it’s allowing us to say things about parts of the network we didn’t have any data on.”

OBJECTIVES

SDOT had two major projects that could benefit from more detailed bicyclist data.

The Center City planning project sought to locate new protected bicycle facilities in the downtown core, an area where the Bicycle Master Plan was vague. The city knew it needed north-south and east-west corridors, so it undertook a study to determine the preferred routes.

The Safety Analysis and Risk Exposure study, performed in partnership with Toole Design and the University of North Carolina, wanted to establish risk factors for roadway designs by correlating collision rates (between cars and bikes, and cars and pedestrians) with infrastructure characteristics.

OUTCOMES

The transportation planners on the Center City project used Strava Metro data to see overall bicycling trends in the downtown. Employing visualizations of Metro data, they 1 examined traffic volumes on Second Avenue both before and after a bike lane pilot project was implemented. They wanted to see how many bicyclists were diverted from other streets to the new bike lane, and how many new cyclists the bike lane attracted. “The Strava data helps us see that,” Moore said. “It paints the picture along with our counts, and we’ve been able to document that change of pattern.”

In the Safety Analysis and Risk Exposure study, Toole Design combined the SDOT bicycle count and Strava Metro data to create a more accurate citywide model of risk. Importantly, the dual-sourced data allowed the team to get beyond the misleading collision hotspots — where high traffic volumes cause high collision volumes — and establish a true rate of collisions that accounts for volume. “This gives us the ability to say, This street has a problem and this street doesn’t,” Moore said. Furthermore, the analysts could compare the characteristics of the high risk streets, identify the types of intersections or infrastructure that were most problematic, and look for those characteristics in other parts of the city. Once those areas are catalogued, they can be improved before the collision rate rises.

The Strava Metro data was a key element in SDOT’s ability to both analyze ridership in specific areas and to put other data in context.

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