Context-aware Traffic Flow Forecasting in New Roads

Abstract

This paper focuses on the problem of forecasting daily traffic of new roads, where very little data is available for prediction. We propose a novel prediction model based on Generative Adversarial Networks (GAN) that learns the subtle patterns of the changes in the traffic flow according to the various contextual factors. Then the trained generator makes a prediction via generating a realistic traffic flow data of a target new road given its weather and day type. Both the quantitative and qualitative results of our extensive experiments indicate the effectiveness of our method.