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MIT Algorithm Predicts Red Light Runners
adeelarshad82 writes "Researchers at MIT have developed an algorithm that determines which drivers will run a red light, within one to two seconds before a potential collision. The research, based on 15,000 cars at a busy intersection, monitored various factors to determine which cars were were likely to run a red light. They found that their predictions were correct about 85 percent of the time, which is about 15-20 percent better than existing traffic prediction algorithms."
For anyone confused; a bollard is a retractable concrete or metal post that comes out the ground to block traffic. They seem to be popular in Europe.
http://www.youtube.com/watch?v=KIqlkPhDfwM
http://www.youtube.com/watch?v=4ZdLjKl0lHc
Karma: Can only be portioned out by the Cosmos.
Who cares if we can "catch" more people?
The people who add the fines to their revenue.
As far as I'm aware, the only thing that's been proven to reduce the number of accidents at stop lights is to make the orange phase longer. This is why cities that want to increase revenue have often been found to have made the orange phase shorter.
Of course, you are making an error of assumption in assuming that people who run lights generally do it willfully by thought, and not negligently by distraction, or though misjudgment.
Actually, thats one of the few things that I remember from taking the one social psych course that I took.... they called it the "fundamental error of assumption". That is, that people tend to ascribe internal motivations to other people's actions, and external ones to our own. So, you ran the red light because you are impatient and try to cut it as close as you can. I ran the red light because the yellow was excessively short, and you were sitting in the passenger seat talking to me and distracting me.
Sounds ridiculous when you say it like that but, its actually pretty common.
"I opened my eyes, and everything went dark again"
The paper is here, and it gives ROC curves. They used two approaches, a hidden Markov model and a support vector machine Bayesian filter.
"they called it the "fundamental error of assumption""...
I think you mean the fundamental attribution error?
"Before God we are all equally wise - and equally foolish"
Albert Einstein