Most navigation apps can show you the fastest possible route to your destination and some can even suggest an eco-friendly route calculated to produce the least amount of carbon emissions. But what if they could also map the safest route with the lowest possible risk of a crash?
A new algorithm developed by UBC researchers could make this a reality. Led by Dr. Tarek Sayed, professor in the UBC department of civil engineering, and PhD student Tarek Ghoul, the group developed a new approach which identifies the safest possible route in an urban network using real-time crash risk data, and can be incorporated into navigation apps such as Google Maps.
To conduct their research, the team used data from 10 drones hovering over downtown Athens, Greece, over multiple days and recording factors including vehicle position, speed and acceleration. They used this information to identify near-misses between vehicles and then predicted the risk of crashes in real-time.
“This research is the first to use real-time crash risk data to provide navigation directions and give you the safest possible driving route through a city,” said Dr. Sayed. “The algorithm is capable of adjusting directions in real-time, suggesting detours to avoid hazardous locations. This helps enhance road safety for all users. For instance, companies will be able to route their fleet efficiently, prioritizing safety and reducing crash risk.”
The researchers are currently extending their research into other cities, including Boston, where autonomous vehicles are being tested that produce not only information about themselves and their navigation, but also about traffic routes and crash risk.
The algorithm could also be used for bike routing, with cyclists and pedestrians being some of the most vulnerable users of road networks. “Including pedestrian and cyclist data in future algorithms or navigation tools will allow us to improve their safety significantly,” said Dr. Sayed.
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