Insights From Urban Sensing Data: From Chaos to Predicted Congestion Patterns

Abstract

Monitoring traffic congestion in smart cities is a challenging problem of great importance in Intelligent Transportation Systems (ITS). Most previous works focused on developing machine learning models that can predict traffic congestion on a meshcode (i.e., a portion of an earth's surface) at a particular time instance. The key limitation of these studies is that they fail to provide holistic information regarding the sets of meshcodes in which regular congestion may happen in the forecasted data. This paper proposes a novel framework to address this problem. The proposed framework employs a 3DCNN multi-source deep learning model (hereafter, called Fusion-3DCNN) to predict traffic congestion on a particular meshcode at a particular time instance. The predicted traffic congestion data is later transformed into a temporal database and feed to the periodic-frequent pattern mining algorithm to identify the sets of meshcode in which regular congestions may happen in the predicted data. Experimental results on real-world traffic congestion data demonstrate that the proposed framework is efficient.