A savvy computer scientist has developed an algorithm to figure out just how bad New York City's bike lanes really are. Using machine learning, New Yorker Alex Bell discovered precisely how often parked cars, delivery trucks and waiting cabs are guilty of illegally blocking the city's bike lanes and bus routes, according to the New York Times. The results showed that it's not just disgruntled bikers and bus drivers imagining the problem -- New York City's cycling and bus lanes are routinely obstructed. Using machine learning, Alex Bell discovered precisely how often parked cars, delivery trucks and waiting cabs are guilty of illegally blocking the city's bike lanes and bus routes Bell trained the algorithm with about 2,000 images of buses, cars and trucks to differentiate between vehicles that were allowed to idle there legally and those that weren't. Specifically, he collected 10 days worth of publicly available footage from a traffic camera in Harlem fixed on one city block.

It took Bell around three weeks to develop the algorithm and his system took about a day to analyze the traffic cam footage. The results showed that the bus lane on the block covered by the camera was blocked 57 percent of the time while the two bike lanes were blocked 40 percent of the time. Based on those numbers, Bell determined that approximately 850 vehicles had blocked the bike lane during those 10 days and 1,000 blocked the bus lane. And while these findings are based on just one city block and a short period of time, the other 101 miles of bus lanes and 435 miles of bike lanes throughout the city suffer from the same issues. It's a problem many people want a solution to.