The deep learning models have the ability to learn complex features of traffic flow pattern under various rainfall conditions. Inspired by deep learning, this paper investigates the performance of deep belief network (DBN) and long short-term memory (LSTM) to conduct short-term traffic speed prediction with the consideration of rainfall impact as a non-traffic input. Furthermore, environmental factors, such as rainfall influence, should also be incorporated to improve accuracy. However, accurate prediction is challenging, due to the stochastic feature of traffic flow and shallow model structure. The successful prediction of traffic speed is increasingly significant for the benefits of both road users and traffic authorities. Traffic information prediction is one of the most essential studies for traffic research, operation and management. IET Generation, Transmission & Distribution.IET Electrical Systems in Transportation.IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing.CAAI Transactions on Intelligence Technology.
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