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Predicting Destinations from Partial Trajectories Using Recurrent Neural Network

  • Yuki Endo
  • Kyosuke Nishida
  • Hiroyuki Toda
  • Hiroshi Sawada
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10234)

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.

Keywords

Recurrent Neural Network Sampling Simulation Trajectory Data Historical Trajectory True Destination 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yuki Endo
    • 1
  • Kyosuke Nishida
    • 1
  • Hiroyuki Toda
    • 1
  • Hiroshi Sawada
    • 1
  1. 1.NTT Service Evolution LaboratoriesNTT CorporationYokosuka-shiJapan

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