Accurate traffic density estimations is essential for numerous purposes like the developing successful transit policies or to forecast future traffic conditions for navigation. Current developments in the 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 utilising causality relations between different data sources are still unsatisfying for many real-world applications . In this report, we have focused on a new method named joint fusion learning that uses underlying causality in time series. We test our method in a very detailed synthetic environment that we specially developed to imitate real-world traffic flow dataset. In the end, we use our joint-fusion 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 uses single time series to forecast the future.