Discriminator conditioned on encoded map image restrains generated point sequences in case they deviate from corresponding road networks. In the second stage, inside each grid, we design an effective encoder-decoder network as the generator to extract road information from map image and then embed it into two parallel Long Short-Term Memory networks to generate GPS point sequences. In the first stage, we first transfer GPS points data to discrete grid representation as the input for a modified deep convolutional generative adversarial network to learn the general pattern. Considering the trajectory’s geographical and sequential features, we propose a map-based Two-Stage GAN method (TSG) to tackle the challenges above and generate fine-grained and plausible large-scale trajectories. Also, existing GAN-based methods are coarse-grained. But existing LSTM-based methods are not suitable for modeling large-scale sequences due to gradient vanishing problem.

Therefore, generating synthetic and plausible trajectory data is pivotal in many location-based studies and applications. A large volume of trajectory data collected from human beings and vehicle mobility is highly sensitive due to privacy concerns.

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