A new traffic model which makes use of robotic cars with altered reaction times, may be able to reduce congestion by providing a more true-to-life simulation.
By controlling the reaction times of robotic cars, researchers at the University of Bristol, have figured out ways of possibly reducing traffic jams. The research team, led by Dr. Róbert Szalai, subsequently created a mathematical model which can help them predict if a traffic jam has a chance of forming in a uniform flow of traffic.
The difficulty in predicting traffic patterns is that it involves using models composed of large numbers of identical vehicles. In real life however, the traffic involves cars which are similar in their behavior, but not identical. Models using identical cars will be able to pinpoint places where road infrastructure falls short, but in real life, jams also occur because of the people behind the wheel: If a few drivers have lower reaction times than average, perhaps a couple of cars won’t get through the green light that were supposed to in ideal conditions. This starts a domino effect, and by the next time the light turns red, four cars will be held back, and so on.
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The study makes use of a design program that alters the reaction time of robotic cars and the researchers believe their new models can reduce traffic jams in the future. The research team hopes that their model can be applied to other fields as well: “The analytical nature of our research means it could be used to understand all sort of systems, such as to design driverless cars that could work well in a mixed human-robotic environment or control gene regulation in living organisms, for example to suppress the expression of some harmful gene,” says Dr. Szalai.