Led by Jonathan How, a professor of aeronautics and astronautics at MIT, some MIT undergraduates and grad students tested the algorithm using traffic data at a busy intersection in Christianburg, Va. The state department of transportation had set up several instruments to monitor cars as part of a safety prediction project. The MIT team analyzed more than 15,000 vehicles in this data set, and found the algorithm was able to spot red-light scofflaws 85 percent of the time. That's about 15 to 20 percent better than existing algorithms, they say. It also generated fewer false positives than other safety-prediction technologies, which could be helpful if it's ever implemented for human use.