If you’re the type of person who enjoys going out for a drive in places with beautiful scenery such as Southern California, then you probably know just how bad traffic can get.
Based on a report by INRIX, Los Angeles has the worst traffic in the world, with a record of 102 hours of congestion during peak hours in 2017. It’s why two Loyola Marymount University students decided to use machine learning in order to better understand traffic.
This meant analyzing elements such as road damage from potholes and cracks, with the help of TensorFlow, Google’s open-source machine learning platform. The goal is to train a model that can identify potholes and other road imperfections quickly using camera footage.
According to the students, both construction companies as well as cities could use such technology to identify which specific roads are in need of maintenance, which means that they would be spending more time fixing the problem instead of scrambling and trying to first identify it.
In a future where such measures are implemented, safer driving conditions and more efficient roadworks should lead to a better flow of traffic, not to mention happier drivers. Not just because of reduced traffic, but also because they would be spending less money on gas and fixing damages caused by poorly maintained roads on their vehicles.