Could a small £20 sensor eventually lead to the creation of safe cycling maps in cities, helping people on bikes avoid risky areas populated by close passing motorists?

That’s what a team of researchers from the University of Washington believe anyway, after they developed the ‘ProxiCycle’ system, a sensor that plugs into a bike’s handlebar and tracks every time a car driver passes within four feet (1.2m), sending it to your phone, and which they reckon could ultimately facilitate the establishment of a close pass risk map for cyclists.

According to the project’s lead author, Jospeh Breda, a University of Washington doctoral student in the Paul G. Allen School of Computer Science and Engineering, the idea for the sensor stemmed from a desire to inform beginner cyclists of the safest routes to ride, encouraging them to cycle without the seemingly constant presence of poor and dangerous driving.

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“Experienced cyclists have this mental map of which streets are safe and which are unsafe, and I wanted to find a simple way to pass that knowledge down to novice cyclists,” Breda said.

“It’s hard to identify the safest routes to ride, especially for beginner cyclists, and a key way to flag dicey streets involves time and injury: waiting until cars have hit several cyclists at a given location.”

To counter this cycling catch-22, Breda and his team developed the ProxiCycle sensor to track the number of close passes cyclists experience in any location.

ProxiCycle close pass sensor
ProxiCycle close pass sensor (Image Credit: University of Washington)

“The point of the project is to create a map of where it’s safe and not safe to bike,” he says. “I was thinking about the things that are correlated with car crashes, or which could indicate that a car crash might happen.

“One of those things is obviously how close cars pass bikes. If a car passes a bike extremely close they might actually hit them.”

Testing the system for two months with 15 cyclists in Seattle, the researchers concluded that they found a “significant correlation” between the location of close passes and those of other indicators of poor safety for people on bikes, particularly collisions with motorists.

According to Breda, if deployed at scale, this information could provide the ability to map out where close passes are mostly likely to take place, while also directing cyclists onto statistically safer routes.

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Presenting their research at a conference on ‘Human Factors in Computing Systems’ in Yokohama, Japan, last month, Breda and his team, which includes academics from the Georgia Institute of Technology and a scientist from software company Gridware, said they started by surveying 389 people, of varying degrees of cycling experience, in Seattle.

According to respondents from across the experience range, the threat from drivers was ranked as the biggest factor which discouraged them from cycling. Those surveyed also said that they would be very likely to use a safety-focused navigation map, a concept the researchers noted was currently hindered by limited data on road safety.

The team then built a small proximity sensor system, which cost around £20 to manufacture, designed to plug into the bar end of the left side of a bike handlebar. The sensor consists of 3D-printed plastic casing housing pair of sensors and a Bluetooth antenna, which transmits to the rider’s phone when an object passes at close proximity.

The algorithm designed by the team then figures out whether the object in question was a passing car – instead of a tree or person, for example – a system validated by a car park test, as well as a thorough review of close pass footage captured on GoPros, which was compared to the sensor output.

Following these initial tests, 15 cyclists were recruited through the newsletter of Seattle Neighbourhood Greenways, a local advocacy group, with each cyclist receiving a sensor, a custom phone app, and instructions.

Over the space of two months, the cyclists rode their bikes 240 times collectively, recording 2,050 close passes in total.

The researchers then compared the locations of these close passes with the cyclists’ “perceived safety” at different locations in the city, which they measured by showing the riders images of locations and having them rate how safe they felt at those particular areas. They then compared the close pass locations with the locations of known collisions involving cyclists and motorists in the last five years.

While the team found a strong correlation between close passes and both risk indicators, they discovered that the location of close passes was more closely linked to the location of collisions than the “perceived safety”, which Breda noted “is the current standard used by policymakers to study safety when collisions aren’t enough”.

“When you overlay the map of these five-year historic collisions and the close passes over two months, there are a lot of places where the signal is pretty much the same,” the doctoral student notes.

Breda says he hopes to potentially “scale up” in the future by taking into account other cycling risk factors, such as the possibility of ‘dooring’ incidents involving drivers or passengers opening car doors into riders’ paths.

He also aims to deploy ProxiCycle in other cities, setting the wheels in motion for existing mapping apps, such as Strava or Google Maps, to include safer route suggestions for cyclists based on his data.

“One study participant, who was living down by Seattle Center, was biking down Mercer all the time,” Breda said, noting the already apparent real-life benefits of his study.

“It’s this busy, multi-lane road. But just before the study, they found out that there’s a great bike lane on a quieter street, just one block north.

“People don’t really feel comfortable getting on a bike, and I want to change that.”