Spanish researchers used AI to analyze common patterns in road accidents. Researchers say that the data collected could assist urban planners in finding ways to minimize collisions and create safer streets for drivers, commuters, passengers, and pedestrians.
Even with the pandemic driving some out of U.S. cities, congested urban areas are here to stay. Might AI be able to identify hazards and help traffic authorities reduce accidents?
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Despite pandemic-driven restrictions on movement, there were over 12,000 accidents in Madrid in 2020, leading to 31 fatalities. In Barcelona, there were more than 5,700 collisions, causing 14 deaths. Pedestrian and vehicle safety is a priority, which is why a research project at the Universitat Oberta de Catalunya (UOC) is harnessing artificial intelligence (AI) to make decisions that will make cities safer. The researchers have looked into the correlation between the complexity of certain urban areas and the likelihood of an accident occurring there.
According to the researchers, the data they have gathered can be used to train neural networks to detect probable hazards in an area and work out patterns associated with this high-risk potential. The researchers, headed by Cristina Bustos and Javier Borge, are working with algorithms that will aid traffic authorities in reducing the likelihood of accidents in urban environments.
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Javier Borge pointed out that “artificial intelligence strikes us as a very powerful tool for finding out where problems might occur, but it’s not going to solve them on its own”. Thus, the team has developed a heuristic method for improving urban scenes which, according to Borge, “is worthless without a human behind it,” such as an urban planner, an architect, or an engineer who can validate and implement changes based on the algorithm-driven data.
With artificial intelligence on their side, the researchers are looking at multiple hazardous urban patterns. Bustos said: “Right now we are analyzing how the visual scene affects drivers’ stress”.
