Large Scale GPS Trajectory Generation Using Map Based on Two Stage GAN

Wang, Xingrui; Liu, Xinyu; Lu, Ziteng; Yang, Hanfang

doi:10.6339/21-JDS1004

Journal of Data Science

Large Scale GPS Trajectory Generation Using Map Based on Two Stage GAN

Volume 19, Issue 1 (2021), pp. 126–141

Xingrui Wang Xinyu Liu Ziteng Lu All authors (4)

https://doi.org/10.6339/21-JDS1004

Pub. online: 10 February 2021 Type: Data Science In Action

Received
1 October 2020

Accepted
1 January 2021

Published
10 February 2021

Abstract

A large volume of trajectory data collected from human beings and vehicle mobility is highly sensitive due to privacy concerns. Therefore, generating synthetic and plausible trajectory data is pivotal in many location-based studies and applications. But existing LSTM-based methods are not suitable for modeling large-scale sequences due to gradient vanishing problem. Also, existing GAN-based methods are coarse-grained. 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. 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. 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. Discriminator conditioned on encoded map image restrains generated point sequences in case they deviate from corresponding road networks. Experiments on real-world data are conducted to prove the effectiveness of our model in preserving geographical features and hidden mobility patterns. Moreover, our generated trajectories not only indicate the distribution similarity but also show satisfying road network matching accuracy.

Supplementary material

Supplementary Material

The trajectories data of Porto is available on Kaggle (http://www.kaggle.com/c/pkdd-15-predict-taxi-service-trajectory-i). Our Python code in experiment section can be found at https://github.com/XingruiWang/Two-Stage-Gan-in-trajectory-generation.

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Keywords

generative adversarial network GPS trajectory spatial-temporal sequence

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