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.

References

Alahi A, Goel K, Ramanathan V, Robicquet A, Fei-Fei L, Savarese S (2016). Social LSTM: Human trajectory prediction in crowded spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 961–971.

Andreini P, Bonechi S, Bianchini M, Mecocci A, Scarselli F, Sodi A (2019). A two stage gan for high resolution retinal image generation and segmentation. arXiv preprint: https://arxiv.org/abs/1907.12296.

Baratchi M, Meratnia N, Havinga PJM, Skidmore AK, Toxopeus BAKG (2014). A hierarchical hidden semi-Markov model for modeling mobility data. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, UbiComp ’14, (AJ Brush, A Friday, JA Kientz, J Scott, J Song, eds.), 401–412. Association for Computing Machinery, New York, NY, USA.

Barth A, Franke U (2009). Estimating the driving state of oncoming vehicles from a moving platform using stereo vision. IEEE Transactions on Intelligent Transportation Systems, 10(4): 560–571.

Bengio S, Vinyals O, Jaitly N, Shazeer N (2015). Scheduled sampling for sequence prediction with recurrent neural networks. In: Advances in Neural Information Processing Systems, (CC Cortes, ND Lawrence, DD Lee, M Sugiyama, R Garnett, eds.), 1171–1179.

Chen C, Mu S, Xiao W, Ye Z, Wu L, Ju Q (2019). Improving image captioning with conditional generative adversarial nets. In: Proceedings of the AAAI Conference on Artificial Intelligence, (A Cohn, Emeritus, K Ford, eds.), volume 33, 8142–8150.

Cho K, van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, et al. (2014). Learning phrase representations using rnn encoder–decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), (A Moschitti, B Pang, W Daelemans, eds.), 1724–1734. Association for Computational Linguistics, Doha, Qatar.

Dai J, Yang B, Guo C, Ding Z (2015). Personalized route recommendation using big trajectory data. In: 2015 IEEE 31st International Conference on Data Engineering, (JG Gehrke, WL Lehner, KS Shim, SK Cha, GM Lohman, eds.), 543–554.

Duan J (2017). Two stage GAN. https://davidsonic.github.io/Project/GAN.html (Accessed on 12/05/2020).

Ferguson D, Darms M, Urmson C, Kolski S (2008). Detection, prediction, and avoidance of dynamic obstacles in urban environments. In: 2008 IEEE Intelligent Vehicles Symposium, 1149–1154.

Gao Q, Zhou F, Zhang K, Trajcevski G, Luo X, Zhang F (2017). Identifying human mobility via trajectory embeddings. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, (C Sierra, eds.), 1689–1695.

Garg S, Peitz S, Nallasamy U, Paulik M (2019). Jointly learning to align and translate with transformer models. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP), (K Inui, J Jiang, V Ng, X Wan, eds.), 4453–4462. Hong Kong, China. November 3–7.

Gehring J, Auli M, Grangier D, Yarats D, Dauphin YN (2017). Convolutional sequence to sequence learning. In: Proceedings of International Conference on Machine Learning, (D Precup, Y Whye, eds.), 1243–1252.

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. (2014). Generative adversarial nets. In: Advances in Neural Information Processing Systems, (Z Ghahramani, M Welling, C Cortes, N Lawrence, KQ Weinberger, eds.), volume 27, 2672–2680. Curran Associates, Inc.

Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017). Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems, (I Guyon, UV Luxburg, S Bengio, H Wallach, R Fergus, S Vishwanathan, R Garnett, eds.), volume 30, 5767–5777. Curran Associates, Inc.

Hochreiter S, Schmidhuber J (1997). Long short-term memory. Neural Computation, 9(8): 1735–1780.

Huang Z, Xu W, Yu K (2015). Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint: https://arxiv.org/abs/1508.01991.

Kulkarni V, Garbinato B (2017). Generating synthetic mobility traffic using RNNs. In: Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery, (H Mao, Y Hu, B Kar, S Gao, G McKenzie, eds.), 1–4.

Kulkarni V, Tagasovska N, Vatter T, Garbinato B (2018). Generative models for simulating mobility trajectories. In: Workshop on Modeling and Decision-Making in the Spatiotemporal Domain, 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada.

Li J, Chen W, Liu A, Li Z, Zhao L (2018). FTS: A feature-preserving trajectory synthesis model. Geoinformatica, 22(1): 49–70.

Liu Y, Kang C, Gao S, Xiao Y, Tian Y (2012). Understanding intra-urban trip patterns from taxi trajectory data. Journal of Geographical Systems, 14(4): 463–483.

Long J, Shelhamer E, Darrell T (2015). Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3431–3440.

Lu J, Xiong C, Parikh D, Socher R (2017). Knowing when to look: Adaptive attention via a visual sentinel for image captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 375–383.

Mohajerin N, Rohani M (2019). Multi-step prediction of occupancy grid maps with recurrent neural networks. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 10592–10600.

Ouyang K, Shokri R, Rosenblum DS, Yang W (2018). A non-parametric generative model for human trajectories. In: International Joint Conferences on Artificial Intelligence, (J Lang, eds.), 3812–3817.

Shokri R, Theodorakopoulos G, Le Boudec JY, Hubaux JP (2011). Quantifying location privacy. In: IEEE Symposium on Security and Privacy, 247–262.

Srikanth S, Ansari JA, Ram RK, Sharma S, Murthy JK, Krishna KM (2019). INFER: INtermediate Representations for FuturE pRediction. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 942–949.

Vinyals O, Toshev A, Bengio S, Erhan D (2015). Show and tell: A neural image caption generator. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3156–3164.

Wang Z, Lu M, Yuan X, Zhang J, Van De Wetering H (2013). Visual traffic jam analysis based on trajectory data. IEEE Transactions on Visualization and Computer Graphics, 19(12): 2159–2168.

Weikum G (2002). Foundations of statistical natural language processing. SIGMOD Record, 31(3): 37–38.

Wu Q, Shen C, Liu L, Dick A, Van Den Hengel A (2016a). What value do explicit high level concepts have in vision to language problems? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 203–212.

Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, et al. (2016b). Google’s Neural Machine Translation system: Bridging the gap between human and machine translation. arXiv preprint: https://arxiv.org/abs/1609.08144.

Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, et al. (2015). Show, attend and tell: Neural image caption generation with visual attention. In: International Conference on Machine Learning, (FR Bach, DM Blei, eds.), 2048–2057.

Yue Y, Zhuang Y, Li Q, Mao Q (2009). Mining time-dependent attractive areas and movement patterns from taxi trajectory data. In: 2009 17th International Conference on Geoinformatics, 1–6.

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Keywords

generative adversarial network GPS trajectory spatial-temporal sequence

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