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IndeTorch: Enhancing Biker Safety with Data & the Environment

Hello World!

We were challenged to identify a problem facing cities that might be addressed by using lost-cost sensors and re-imagining the way we interact with our environment. After many iterations, our team (Sutong Jiang, Yishi Liu, and Deon Provost) developed a prototype to increase safety for bikers by logging unsafe areas and rethinking how we might illuminate the physical environment for cyclists when lighting isn’t ideal.

What’s the Problem?

On the one hand, biking is an awesome way to promote public health and reduce metropolitan congestion. These and other benefits are well documented. On the other hand, safety concerns can threaten these potential social outcomes. In 2016 alone, 2,579 bikers were involved in crashes in Philadelphia. We thought, is there a way for bikers to identify unsafe areas in real-time or near real-time?

Additionally, we saw an opportunity to increase safety at a particularly vulnerable time for bikers. The photo below was taken at dusk in a high-traffic biking corridor (South St. bridge area of Philadelphia), but the same conditions might also exist early in the morning. We thought, is there a way to make these areas brighter using the built environment?

The Solution: A Bike that is Also a Torch

One definition of a torch is “a portable means of illumination.” We particularly like this notion because we see both the data generated by our device and responsive lights in the built environment as providing different types of illumination.

Below is a diagram of the components of our device that will be attached to Indego bikes. We surmise that, while biking, a single button to log activity that bikers can classify later is the best way to solve the problem of identifying unsafe areas in near real-time. As you will see in the live demonstration below, we envision the device being attached to the handle bars and positioned near the basket on the Indego bike (for ease of access).

The radio transmitter in our device would trigger lights in poorly lit areas as bikers approach. Below is a diagram of the components that would be distributed in the built environment (on buildings, signs, the bridge in the image above, etc.).

Combined, the process looks like this:

Awesome, right? But we know what you want: less tell, more show. Here is a demonstration of the product working in action.

Why Indego?

Some might be wondering, “Why Indego?” We think a partnership with Indego makes a lot of sense. For starters, Indego already has a partnership with the city of Philadelphia. Indego also already possesses the infrastructure  that would be required  to deploy our solution (distributed throughout the city).  Additionally, we think Indego has an interest in the data that would be created and leveraging it to keep its riders safe (not to mention potentially expanding utilization at times and in areas where lighting is poor).

Potential Next Steps:

Our initial concept included a kiosk where users might be able to extract and mounting our device on Indego bikes with velcro straps. However, we think that there may be ways to permanently mount the device and rethink how we would power the device.

As for the built environment, if we’re successful, we think that local businesses may want to use our model of radio-transmitter-responsive lighting  to potentially target advertisements to riders. Further, there is no reason why similar devices can’t be designed for runners and walkers (with the corresponding lighting projects to encourage exploration, public health and safety, etc.).

Additionally, we think that there is an opportunity to expand the use of our button. For example, we see no reason why (in addition to logging data) that we can’t use our device to light the bike itself. We think that this would be visually stunning and help cars to identify cyclists.

Lastly, we didn’t go much into the companion app to classify the logged data. We think applications like BSafe offer a comparable template for how this technology might be deployed.

Thanks for reading!

 

(And a special thanks to our Professor,  Allison Lassiter,  at the University of Pennsylvania [CPLN571] for motivating a fun and challenging project!)

 

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