Using Big Data and Cloud Computing to Predict Traffic Jams
Microsoft Research is working with Brazil's Federal University of Minas Gerais to tackle the seemingly uncontrollable problem of traffic jams. The Traffic Prediction Project plans to leverage all available traffic data - including both historic and current information gleaned from transportation departments, Bing traffic maps, road cameras and sensors, and the social networks of the drivers themselves. The immediate objective of the research is to predict traffic conditions over the next 15 minutes to an hour. By using algorithms to process all these data, the project team intends to predict traffic jams accurately so that drivers can make real-time choices. Achieving reliable predictions will involve processing terabytes of data, and the researchers are using the Microsoft Azure cloud computing platform for the service. To date, the researchers have tested their prediction model in New York, Los Angeles, London, and Chicago. The model achieved a prediction accuracy of 80 percent, and that was based on using only traffic-flow data. The researchers expect the accuracy to increase to 90 percent when traffic incidents and data from social networks are folded in.