ADL based Framework For Multimodal Data Fusion in Traffic Jam prediction

Abstract

Recently, intelligent transportation system (ITS) is considered as one of the most important issues in smart city applications. Its supports urban and regional development and promotes economic growth, social development, and enhances human well-being. ITS integrates new technologies of information and communication including sensors, social media IoT devices which can generate a massive amount of heterogeneous and multimodal data known as big data term. In this context, Data Fusion techniques (DF) seem promising and have emerged from transportation applications and hold a promising opportunity to deal with imperfect raw data for capturing reliable, valuable and accurate information. In literature many DF techniques based on machine learning remarkably renovates fusion techniques by offering the strong ability of computing and predicting. In this paper, we propose new Hybrid method based on Deep Learning combine two independent model such as CNN, LSTM models to fuse multimodal and spatial temporal data. The proposed model uses Extended Kalman Filter (EKF) to combine result of the proposed DL classifiers. In the other side, the proposed approach uses CBOA algorithm for feature selection in order to provide effective exploration of significant features with faster convergence