An Imputation-Enhanced Hybrid Deep Learning Approach for Traffic Volume Prediction in Urban Networks: A Case Study in Dresden

Abstract

Advanced traffic management systems rely heavily on accurate traffic state estimation and prediction. Traffic prediction based on conventional road-based sensors faces considerable challenges due to spatiotemporal correlations of traffic flow propagation, and heterogeneous, error-prone, and missing data. This paper proposes a hybrid deep learning approach for online traffic volume prediction in an urban network. The approach ensembles the long short-term memory (LSTM) neural network and the convolutional neural networks (CNN) in a parallel way. In order to deal with missing data, a state-of-the-art Bayesian probabilistic imputation method is employed in the overall prediction pipeline. The hybrid traffic prediction structure can capture the spatiotemporal characteristics of traffic volume. The proposed prediction model is verified by the loop and infrared sensor data in the inner city network of the City of Dresden. Experimental results show that it can achieve superior volume prediction compared with baseline methods.