A Non-Parametric Generative Model for Human Trajectories

Authors: Kun Ouyang, Reza Shokri, David S. Rosenblum, Wenzhuo Yang

IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We evaluate our method on realistic location trajectories and compare our synthetic traces with multiple existing methods on how they preserve geographic and semantic features of real traces at both aggregated and individual levels. Our empirical results prove the capability of our generative model in preserving various useful properties of real data.
Researcher Affiliation Collaboration 1 National University of Singapore 2 SAP Innovation Center Singapore
Pseudocode No The paper does not contain any structured pseudocode or algorithm blocks.
Open Source Code No The paper does not provide any concrete access information for open-source code for the described methodology.
Open Datasets Yes We use Nokia Lausanne location trajectories [Kiukkonen et al., 2010], and pre-process them the same way as in [Bindschaedler and Shokri, 2016] to construct the trajectories.
Dataset Splits No From the original dataset, we randomly select 50% trajectories of each user to construct the training set, and another 50% as the test set.
Hardware Specification No The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments.
Software Dependencies No The paper mentions general software components like 'Convolution Layers', 'Generative Adversarial Networks', and 'WGAN-GP', but does not provide specific version numbers for any software dependencies.
Experiment Setup No The paper describes general model components like 'Convolution Layers' and the use of 'WGAN-GP' for training, but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed architectural configurations.