This paper presents a novel approach for pedestrian trajectory prediction. In particular, we developed a novel attention model using bidirectional recurrent neural networks (BiRNNs). The difficulty of incorporating social interactions into the model has been addressed. Thanks to the special structure of BiRNNs enhanced by the attention mechanism, a proximity-independ model of the relative importance of each pedestrian. The main difference between our and the previous approaches is that BiRNN allows us to employ information on the future state of the pedestrians. We tested the performance of our method on several public datasets. The proposed model outperforms the current state-of-the-art approaches on most of these datasets. Furthermore, we analyze the learnt attention scores to prove the advantages of BiRNNs on recognizing social interactions.