Strava Metro at work in New Hampshire

Laura Getts
Oct 6, 2018 · 6 min read

I write to you as both a New Hampshire native and a Strava convert. I was originally introduced to Strava as a master’s student at Plymouth State University in New Hampshire, where I spent several years researching bikeability under Dr. Amy Villamagna. In this capacity, I had the pleasure working with regional planners, local advocacy organizations, bicycle enthusiasts, the Strava team, and NH’s Department of Transportation (NHDOT), to better understand statewide barriers to bicycling engagement.

NH has long struggled to understand both where its bicyclists ride, and how changes to the roadway impact bicycling engagement and route choice. To better answer these questions, NHDOT purchased several years’ worth of Strava ride data for the state. With a little tinkering and input from the planning community, we quickly identified the strengths of our dataset. Strava not only filled the gaps left by patchy ground counts, surveys, and anecdotal evidence, but also enabled us to get a sense of temporal trends and popular trip origins and destinations. This wholistic spatial picture provides a novel range of information and capabilities to our planners that will hereafter guide local and regional transportation planning initiatives.

Here are some examples of how we’ve used Strava Metro in our work.:

1. Key Destination Heat Mapping

We began with a standard Strava heat map and overlaid “key destinations”, such as schools, grocery stores, and municipal facilities. This helped us understand the proximity of community-defined key destinations to high use bicycle segments. While this map doesn’t highlight popular destination polygons, it does alert us to potential choke points near several bike-worthy destinations in Plymouth, NH.

2. Seasonal Trends

We assessed total Strava rides by both hour and season in Concord, NH, to better understand how bicycling engagement changes seasonally.

3. Ground Count Location Recommendations

Our Regional Planning Commissions (RPC) have a limited number of volunteers and electronic active transportation counters that they can deploy throughout the year. They wanted to maximize the placement of these counters to best capture bicyclist traffic flow in their communities. To help inform RPCs, we used Strava intersection and origin-destination polygon data to prioritize a series of intersections by total rides, unique riders, commuter rides, and destination polygon attraction.

4. Bicyclist Responses to Infrastructural Changes

We used Strava to evaluate cyclist responses to changes in bicyclist-specific infrastructure throughout the state. In this particular example, we quantified the number of total rides and unique bicyclists that altered their route following the completion of the pedestrian and bicyclist-specific Piscataquog Trestle Bridge in Manchester, NH in October of 2015.

5. Unique Origins Per Destination

By manipulating Strava’s origin-destination polygon data, we could visualize the destination polygons that were attracting the most spatially diverse range of Strava bicyclists. This image is a heat map of destination polygons. The darker the polygon, the greater the diversity of total trip origins tied to that destination in 2015.

6. Visualizing Destination Origins

Prior to the release of Strava’s fantastic origin-destination DataView tool, we visualized the spread of Strava bicyclist trip origins tied to specific destination polygons. This allowed us to see both the spatial diversity of origins tied to a specific destination, as well as the distances that some our of bicyclists were willing to travel to reach a part of town. When we performed this analysis for Allenstown, NH during a community charrette process, we could conclude that many bicycle trips ending in Allenstown had begun in the surrounding communities of Concord, Pembroke, and Deerfield, NH. Proof of Allenstown’s status as a Strava cyclist destination may provide help justify future inter-community bicycle connectivity infrastructure.

7. Social Vulnerability Mapping

We combined NH’s Social Vulnerability Index (SVI) census tract data with Strava trip origin data to better characterize Strava ridership in several of our communities. The higher the SVI, the more vulnerable the populations residing in a census tract.

8. Are Strava riders using “high stress” roads?

The state of NH adapted Merkuria et al.’s (2012) “Low-stress bicycling and network connectivity” framework, whereby road segments were classified into four levels of traffic stress (LTS). LTS 1 roadways have a low traffic stream and are suitable to all cyclists, LTS 2 segments are suitable to most, LTS 3 segments are suitable for more confident riders, and LTS 4 roads present risky riding conditions and are generally only used by the most confident riders.

We wanted to know which types of segments our Strava riders used most frequently. Many folks argue that Strava riders represent the most confident subset of cyclists in any given community. If this is the case, we may expect to see our Strava riders frequenting LTS 4 segments when it is most convenient to do so. After running the numbers, we determined that while our Strava riders in Manchester, NH do use LTS 4-rated roads, they spend most of their time on LTS 2 and 3-rated segments. This may support the argument that Strava cyclists prefer low stress routes when they are available and do not require undue detour.

9. Using Strava and LTS to Prioritize Improvements

We showed planners how they can link popular Strava road segments with Level of Traffic Stress ratings to help prioritize improvements to roadways — be it bike lane installations, intersection treatments, or pavement improvements. These charts show the LTS score of our most regularly cycled roads. If planners wanted to isolate popular LTS 3-rated roadways for safety improvements, they could easily do so, as seen above. While Strava ride-guided improvements do not necessarily represent improvements that benefit the bulk of your bicycling population, Strava offers a good starting point, and can be further validated by ground count data.

How have you been using Strava to make or inform planning decisions? We would love to hear from you!



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