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Short-term traffic flow prediction is one of the crucial issues in intelligent transportation system, which is an important part of smart cities. Accurate predictions can enable both the drivers and the passengers to make better decisions about their travel route, departure time, and travel origin selection, which can be helpful in traffic management. Multiple models and algorithms based on time-series prediction and machine learning were applied to this issue and achieved acceptable results. Recently, the availability of sufficient data and computational power motivates us to improve the prediction accuracy via deep-learning approaches. Recurrent neural networks have become one of the most popular methods for time-series forecasting; however, due to the variety of these networks, the question that which type is the most appropriate one for this task remains unsolved. In this paper, we use three kinds of recurrent neural …