New Algorithm Uses Real-Time Crash Risk Data to Map Safest Routes for City Drivers

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A team of researchers from the University of British Columbia (UBC) has developed a new algorithm that can identify the safest routes for city drivers using real-time crash risk data. Led by Dr. Tarek Sayed and PhD student Tarek Ghoul, the team used data from 10 drones hovering over downtown Athens, Greece, to analyze vehicle position, speed, and acceleration, and identify near-misses that could lead to crashes. This information was then used to predict the risk of crashes in real-time and provide navigation directions to drivers.

According to Dr. Sayed, this is the first research to use real-time crash risk data to map the safest driving route through a city. The algorithm can adjust directions in real-time, suggesting detours to avoid hazardous locations and enhance road safety for all users. For example, companies can route their fleet efficiently, prioritizing safety and reducing crash risk.

The study also revealed that the fastest routes are not always the safest. The team analyzed a section of Athens’ urban road network and found that only 23% of the fastest routes were also considered the safest routes. On average, the safest route used 54% of the roads used in the fastest route, indicating that road users should consider a mix of safety and efficiency when choosing directions.

“There was a clear trade-off between safety and mobility in the network we looked at,” said Ghoul. “The safest route tended to be 22% safer than the fastest route, while the fastest route was only 11% faster than the safest route. This suggests that there are considerable gains in safety on the safest routes with just a small increase in travel time.”

The researchers are now extending their research to other cities, including Boston, where autonomous vehicles are being tested. These vehicles not only provide information about themselves and their navigation but also about traffic routes and crash risk. With access to new technologies such as autonomous vehicle data, cameras, and other sensing technologies, there are new possibilities for real-time safety measurement and effective routing.

Dr. Sayed also noted that the algorithm could be used for bike routing, as cyclists and pedestrians are some of the most vulnerable road users. Including pedestrian and cyclist data in future algorithms or navigation tools could significantly improve their safety.

This new algorithm has the potential to revolutionize navigation apps and improve road safety for drivers, cyclists, and pedestrians alike. By considering real-time crash risk data, it can provide the safest routes in a city, taking into account factors that traditional navigation apps may not consider. As technology continues to advance, these types of smart mobility applications hold great promise for the future.

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  1. Source: Coherent Market Insights, Public sources, Desk research
  2. We have leveraged AI tools to mine information and compile it