Accurate traffic density estimation is essential for numerous purposes such as transit policy development or forecasting future traffic conditions for navigation. Current developments in machine learning and computer systems bring the transportation industry numerous possibilities to improve their operations using data analyses on traffic flow sensor data. However, even state-of-art algorithms for time series forecasting perform well on some transportation problems, they still fail to solve some critical tasks. In particular, existing traffic flow forecasting methods that are not utilizing causality relations between different data sources are still unsatisfying for many real-world applications. In this paper, we have focused on a new approach named multi-stream learning that uses underlying causality in time series. We evaluate our method in a very detailed synthetic environment that we specially developed to imitate real-world traffic flow dataset. In the end, we assess our multi-stream learning on a historical traffic flow dataset for Thessaloniki, Greece which is published by Hellenic Institute of Transport (HIT). We obtained better results on the short-term forecasts compared the widely-used benchmarks models that use a single time series to forecast the future.