Semi-supervised GANs to Infer Travel Modes in GPS Trajectories

@article{Yazdizadeh2021SemisupervisedGT,
  title={Semi-supervised GANs to Infer Travel Modes in GPS Trajectories},
  author={Ali Yazdizadeh and Zachary Patterson and Bilal Farooq},
  journal={ArXiv},
  year={2021},
  volume={abs/1902.10768}
}
Semi-supervised Generative Adversarial Networks (GANs) are developed in the context of travel mode inference with uni-dimensional smartphone trajectory data. We use data from a large-scale smartphone travel survey in Montreal, Canada. We convert GPS trajectories into fixed-sized segments with five channels (variables). We develop different GANs architectures and compare their prediction results with Convolutional Neural Networks (CNNs). The best semi-supervised GANs model led to a prediction… 

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