A closer look at Arlington’s bike counters show how riders are using the trails and bike lanes

Other developers at Bike Hack Night VII presented sensor and mapping projects to understand the experience of urban biking

Arlington County, over the past several years, has strategically placed 30 permanent EcoCounters, bike- and pedestrian-counting sensors, to determine how many people are riding bicycles and walking on major trails and routes.

Permanent counters at these sites allow planners to understand the year-over-year trends of biking and walking in the county, according to EcoCounter’s Fraser McLaughlin. At January’s edition of the  Transportation Techies, sponsored by Mobility Lab, he also presented his data showing how special events, weather, and even the day of the week affect ridership.

FM counter type

Sensors by bicyclist type. Click to enlarge.

These counters have come to provide years of valuable data on bicycling behavior for the county. By focusing on annual data from April through October – since the colder months would skew the numbers – McLaughlin used the seasonal average daily traffic to identify different types of cyclists and where they ride.

McLaughlin found that access points to Washington, D.C., especially Key Bridge, were primarily commuter routes, with high volume during peak hours and major lulls in afternoons and weekends. Areas deeper within the county, in contrast, appeared to be more recreational, and had more consistent volumes on the weekend. Counters in the middle detected more mixed usage.

McLaughlin also noted that a major benefit of a network of permanent counters is the ability to fill in gaps in data for other locations. With this knowledge, he explained, planners can better understand how people are using bike infrastructure throughout Arlington to make informed decisions for future route developments.

Brain games

Other presenters at the meetup, the first time Techies has taken place in conjunction with the annual Transportation Research Board conference, showed off a mix of tools and visualizations that planners and hobbyists alike can use to better understand cycling in their community.

Andrew Lovett-Baron shared Pedal Pedal Club, an app he built to game users’ behavior and encourage them to bike more. Using route-mapping app Strava to track miles, Pedal Pedal allows users to set incentive benchmarks – achievements at which the app encourages them to treat themselves. In Lovett-Baron’s case, he uses it to discipline himself from impulse-buying bike parts, but he also explained that for people like his wife, the app reassures them that they deserve a treat with hard work. Regardless of motivation, he hopes Pedal Pedal can create a bridge for folks outside the bicycling community to create structured motivations to ride more.

Tom Lee shared the results of his do-it-yourself project, which measures the distance at which cars passed him during his bike commute. Using an ultrasonic sensor and a WiFi chip, Lee built a small device that attached to the side of his bike’s frame. Despite having a small dataset and plenty of moments when cars passed within D.C.’s legal minimum of three feet, Lee was surprised to find that, on average, drivers gave him about 3.5 feet when passing.

Drew Dara-Abrams of MapZen explained how the mapping platform’s “Turn-by-Turn” can help developers with routing and navigation for bicyclists. With Valhalla, an open-source routing engine, the service incorporates MapZen’s TransitLand and local elevation data into Open Street Map. It allows users to optimize directions based on the type of rider they are, accounting for numerous factors like elevation change or road surface. From a wider perspective, Dara-Abrams described how these tools explore the potential of the local bike network, and where it might take cyclists, such as in his isochrone map visualizing bike sheds based on riders’ different stress tolerance levels.

miovision test

Timo Hoffman and Justin Eichel of Miovision explained their video-based traffic-counting technology (right), which uses a machine learning algorithm to classify types of street users, including bikes and pedestrians, by their outlines. By combining this with analysis of each vehicle type’s directional volume in key areas, this tool could help cities develop solutions to traffic problems, regardless of the modes involved.

Maps are especially useful for understanding the environment bicyclists deal with. Stuart Lynn, a map scientist at Carto, shared bike experience maps that cover this information. From an animation of three days of Barclays bikeshare volume in London to phone measurements of road quality from a bumpy bike ride through New York City, these visualizations can fill in gaps on how bicyclists typically travel. They can also provide valuable insight into traffic behaviors and infrastructure that affect them, such as a map of bike crashes throughout London.

Arlene Ducao describes her MindRider project, which evaluates bicyclists’ stress levels as they bike, as a “location intelligence product.” Originally hoping to understand the experience of novice female bicyclists in Manhattan, Ducao developed a helmet fitted with biosensors, which measure brain activity and skin conductivity during a ride. On a map, this data is represented as colors, where red means high concentration and green is relaxed. Analyzing this information against municipal data, Ducao could could pick out regular hotspots and sweet spots that suggested higher- and lower-level stress areas.

chrome_2017-01-19_13-10-31

This has led to the Ducao’s Multimer Experience Map (above), in which different colors represent specific mindsets, and the project has expanded to other modes beyond biking. Ultimately, Ducao would like to analyze the built environment around these routes, which would be immensely helpful to finding what spaces are most enjoyable for bicyclists.

By getting into the patterns and conditions that affect biking behaviors, planners and advocates are building tools that can contribute to better bike infrastructure and a better understanding of how people ride.

Graphics: Arlington counter maps by Fraser McLaughlin. Multimer screenshot from http://dukodestudio.com/mindriderdata

Share this item

Be the First to Comment