Fusion-3DCNN-max3P: A dynamic system for discovering patterns of predicted congestion

Nowadays, resolving chaotic traffic situations, which usually link to traffic congestion, is an essential need. It poses many risks to commuters like traffic accidents, especially during bad weather situations. Besides, owing to the exponential growth of IoT technologies, it is easier than ever to collect a huge amount of urban sensing data. Therefore, building a system to anticipate congestion from the collected data could enhance public safety and give traffic police forces enough time to handle traffic flows in potentially dangerous areas. Moreover, if we can discover patterns in which predicted congestion usually happens, we can build reaction plans with various alert codes. They create dynamic risk maps that can provide useful knowledge to both authorities and travelers to make rescue and travel plans effectively. This paper proposes a novel framework to address these problems. The proposed framework employs the Enhanced-Fusion-3DCNN deep learning model to predict future long-term traffic congestion on a particular mesh-code at a particular time instance. The predicted traffic congestion data is later transformed into a temporal database and feed to the maximal periodic-frequent pattern algorithm to identify the sets of mesh-code in which regular congestion may happen in the predicted data. Experimental results on real-world traffic congestion data demonstrate that the proposed framework is efficient.