Generating Geospatial Trip DataUsing Deep Neural Networks

Abstract: Synthetic data provides a good alternative to real data when the latter is not sufficientor limited by privacy requirements. In spatio-temporal applications, generating syntheticdata is generally more complex due to the existence of both spatial and temporal dependencies.Recently, with the advent of deep generative modeling such as GenerativeAdversarial Networks (GAN), synthetic data generation has seen a lot of development andsuccess. This thesis uses a GAN model based on two Recurrent Neural Networks (RNN)as a generator and a discriminator to generate new trip data for transport vehicles, wherethe data is represented as a time series. This model is compared with a standalone RNNnetwork that does not have an adversarial counterpart. The result shows that the RNNmodel (without the adversarial counterpart) performed better than the GAN model dueto the difficulty that involves training and tuning GAN models.