Vehicle Trajectory Prediction based on LSTM Recurrent Neural Networks

This work presents an effective tool to predict the future trajectories of vehicles when its current and previous locations are known. We propose a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) prediction scheme due to its adequacy to learn from sequential data. To fully learn the vehicles' mobility patterns, during the training process we use a dataset that contains real traces of 442 taxis running in the city of Porto, Portugal, during a full year. From experimental results, we observe that the prediction process is improved when more information about prior vehicle mobility is available. Moreover, the computation time is evaluated for a distinct number of prior locations considered in the prediction process. The results exhibit a prediction performance higher than 89%, showing the effectiveness of the proposed LSTM network.