Predicting Destinations from Partial Trajectories Using Recurrent Neural Network

Abstract

Predicting a user’s destinations from his or her partial movement trajectories is still a challenging problem. To this end, we employ recurrent neural networks (RNNs), which can consider long-term dependencies and avoid a data sparsity problem. This is because the RNNs store statistical weights for long-term transitions in location sequences unlike conventional Markov process-based methods that count the number of short-term transitions. However, how to apply the RNNs to the destination prediction is not straight-forward, and thus we propose an efficient and accurate method for this problem. Specifically, our method represents trajectories as discretized features in a grid space and feeds sequences of them to the RNN model, which estimates the transition probabilities in the next timestep. Using these one-step transition probabilities, the visiting probabilities for the destination candidates are efficiently estimated by simulating the movements of objects based on stochastic sampling with an RNN encoder-decoder framework. We evaluate the proposed method on two different real datasets, i.e., taxi and personal trajectories. The results demonstrate that our method can predict destinations more accurately than state-of-the-art methods.