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For road users, the prediction can provide information and guidance to reduce travel cost for authorities, the information will contribute to develop management strategies and operations. The successful prediction of traffic information is increasingly significant for the benefits of both road users and traffic authorities. Furthermore, the LSTM can outperform the DBN to capture the time-series characteristics of traffic speed data. The experiment results indicate that with the combination input of speed and additional rainfall data, deep learning models have better prediction accuracy over other existing models, and also yields improvements over the models without rainfall input. To validate the performance of rainfall-integrated DBN and LSTM, the traffic detector data from an arterial in Beijing are utilised for model training and testing. 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.