As traffic volumes in cities around the world are constantly growing we are faced with the challenge to track and control car movements in a more detailed and intelligent way to beat the traffic. Real-time information on traffic including automotive sensors and crowd-sourced data feeds are an interesting new source of data. However, to utilize this data to its full extent and turn it into valuable information, intelligent methods for analyzing and predicting traffic are needed.
Pivotal’s Data Science Team has developed several innovative methods to analyze this traffic flow information harvested from real-time and in-car data sources including GPS. These methods by themselves are highly useful for predicting future traffic conditions and dissecting traffic data. We will describe how we created these algorithms and show different interesting results from their application. This example demonstrates how deeper insights into a problem can be found by combining different machine learning methods.