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Trajectory data is essential for various applications as it records the movement of vehicles.
T. M. Cover, Elements of information theory . John Wiley & Sons, 1999
1999
Earlier work this paper cites.
Y. Theodoridis and M. A. Nascimento, “Generating spatiotemporal datasets on the www,” ACM SIGMOD Record , vol. 29, no. 3, pp. 39–43, 2000
2000
Earlier work this paper cites.
R. R. Joshi, “A new approach to map matching for in-vehicle navigation systems: the rotational variation metric,” in ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No. 01TH8585) . IEEE, 2001, pp. 33–38
2001
Earlier work this paper cites.
D. Pfoser and Y. Theodoridis, “Generating semantics-based trajectories of moving objects,” Computers, Environment and Urban Systems , vol. 27, no. 3, pp. 243–263, 2003
2003
Earlier work this paper cites.
P. Newson and J. Krumm, “Hidden markov map matching through noise and sparseness,” in Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems , 2009, pp. 336–343
2009
Earlier work this paper cites.
S. Isaacman, R. Becker, R. Cáceres, M. Martonosi, J. Rowland, A. Varshavsky, and W. Willinger, “Human mobility modeling at metropolitan scales,” in Proceedings of the 10th international conference on Mobile systems, applications, and services , 2012, pp. 239–252
2012
Earlier work this paper cites.
S. Gambs, M.-O. Killijian, and M. N. del Prado Cortez, “Next place prediction using mobility markov chains,” in Proceedings of the first workshop on measurement, privacy, and mobility , 2012, pp. 1–6
2012
Earlier work this paper cites.
N. Pelekis, C. Ntrigkogias, P. Tampakis, S. Sideridis, and Y. Theodoridis, “Hermoupolis: a trajectory generator for simulating generalized mobility patterns,” in Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III 13 . Springer, 2013, pp. 659–662
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
J. Dai, B. Yang, C. Guo, and Z. Ding, “Personalized route recommendation using big trajectory data,” in 2015 IEEE 31st international conference on data engineering . IEEE, 2015, pp. 543–554
2015
Earlier work this paper cites.
R. Choe, J. Puig, V. Cichella, E. Xargay, and N. Hovakimyan, “Trajectory generation using spatial pythagorean hodograph bézier curves,” in AIAA Guidance, Navigation, and Control Conference , 2015, p. 0597
2015
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International conference on machine learning . PMLR, 2015, pp. 2256–2265
2015
Earlier work this paper cites.
J. Chung, K. Kastner, L. Dinh, K. Goel, A. C. Courville, and Y. Bengio, “A recurrent latent variable model for sequential data,” Advances in neural information processing systems , vol. 28, 2015
2015
Earlier work this paper cites.
J. Ho and S. Ermon, “Generative adversarial imitation learning,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining , 2016, pp. 855–864
2016
Earlier work this paper cites.
L. Yu, W. Zhang, J. Wang, and Y. Yu, “Seqgan: Sequence generative adversarial nets with policy gradient,” in Proceedings of the AAAI conference on artificial intelligence , vol. 31, no. 1, 2017
2017
Earlier work this paper cites.
K. Ouyang, R. Shokri, D. S. Rosenblum, and W. Yang, “A non-parametric generative model for human trajectories.” in IJCAI , vol. 18, 2018, pp. 3812–3817
2018
Earlier work this paper cites.
K. Zhao, D. Khryashchev, and H. Vo, “Predicting taxi and uber demand in cities: Approaching the limit of predictability,” IEEE Transactions on Knowledge and Data Engineering , vol. 33, no. 6, pp. 2723–2736, 2019
2019
Earlier work this paper cites.
Y. Ge, H. Li, and A. Tuzhilin, “Route recommendations for intelligent transportation services,” IEEE Transactions on Knowledge and Data Engineering , vol. 33, no. 3, pp. 1169–1182, 2019
2019
Earlier work this paper cites.
Q. Gao, G. Trajcevski, F. Zhou, K. Zhang, T. Zhong, and F. Zhang, “Deeptrip: Adversarially understanding human mobility for trip recommendation,” in Proceedings of the 27th ACM SIGSPATIAL international conference on advances in geographic information systems , 2019, pp. 444–447
2019
Earlier work this paper cites.
P. Zhao, H. Jiang, J. Li, F. Zeng, X. Zhu, K. Xie, and G. Zhang, “Synthesizing privacy preserving traces: Enhancing plausibility with social networks,” IEEE/ACM Transactions on Networking , vol. 27, no. 6, pp. 2391–2404, 2019
2019
Cited alongside, same era.
D. Huang, X. Song, Z. Fan, R. Jiang, R. Shibasaki, Y. Zhang, H. Wang, and Y. Kato, “A variational autoencoder based generative model of urban human mobility,” in 2019 IEEE conference on multimedia information processing and retrieval (MIPR) . IEEE, 2019, pp. 425–430
2019
Cited alongside, same era.
2019
Cited alongside, same era.
D. Smolyak, K. Gray, S. Badirli, and G. Mohler, “Coupled igmm-gans with applications to anomaly detection in human mobility data,” ACM Transactions on Spatial Algorithms and Systems (TSAS) , vol. 6, no. 4, pp. 1–14, 2020
2020
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
Later among the works it cites.
2022
Later among the works it cites.
L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, W. Zhang, B. Cui, and M.-H. Yang, “Diffusion models: A comprehensive survey of methods and applications,” ACM Computing Surveys , 2022
2022
Later among the works it cites.
M. Hu, Y. Wang, T.-J. Cham, J. Yang, and P. N. Suganthan, “Global context with discrete diffusion in vector quantised modelling for image generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 11 502–11 511
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Cited alongside, same era.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro, “Diffwave: A versatile diffusion model for audio synthesis,” in International Conference on Learning Representations , 2020
2020
Cited alongside, same era.
J. Feng, Z. Yang, F. Xu, H. Yu, M. Wang, and Y. Li, “Learning to simulate human mobility,” in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining , 2020, pp. 3426–3433
2020
Cited alongside, same era.
2020
Cited alongside, same era.
J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” in International Conference on Learning Representations , 2020
2020
Cited alongside, same era.
J. Kim, S. Kim, J. Kong, and S. Yoon, “Glow-tts: A generative flow for text-to-speech via monotonic alignment search,” Advances in Neural Information Processing Systems , vol. 33, pp. 8067–8077, 2020
2020
Cited alongside, same era.
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2021
Cited alongside, same era.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Yuan, J. Ding, H. Wang, D. Jin, and Y. Li, “Activity trajectory generation via modeling spatiotemporal dynamics,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2022, pp. 4752–4762
2022
Later among the works it cites.
Y. Wang, G. Li, K. Li, and H. Yuan, “A deep generative model for trajectory modeling and utilization,” Proceedings of the VLDB Endowment , vol. 16, no. 4, pp. 973–985, 2022
2022
Later among the works it cites.
V. Voleti, A. Jolicoeur-Martineau, and C. Pal, “Mcvd-masked conditional video diffusion for prediction, generation, and interpolation,” Advances in Neural Information Processing Systems , vol. 35, pp. 23 371–23 385, 2022
2022
Later among the works it cites.
X. Li, J. Thickstun, I. Gulrajani, P. S. Liang, and T. B. Hashimoto, “Diffusion-lm improves controllable text generation,” Advances in Neural Information Processing Systems , vol. 35, pp. 4328–4343, 2022
2022
Later among the works it cites.
J. Liu, C. Li, Y. Ren, F. Chen, and Z. Zhao, “Diffsinger: Singing voice synthesis via shallow diffusion mechanism,” in Proceedings of the AAAI conference on artificial intelligence , vol. 36, no. 10, 2022, pp. 11 020–11 028
2022
Later among the works it cites.
T. Chen, R. ZHANG, and G. Hinton, “Analog bits: Generating discrete data using diffusion models with self-conditioning,” in The Eleventh International Conference on Learning Representations , 2022
2022
Later among the works it cites.
N. Zhang, L. Qin, P. Yu, W. Gao, and Y. Li, “Grey-markov model of user demands prediction based on online reviews,” Journal of Engineering Design , vol. 34, no. 7, pp. 487–521, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Q. Long, H. Wang, T. Li, L. Huang, K. Wang, Q. Wu, G. Li, Y. Liang, L. Yu, and Y. Li, “Practical synthetic human trajectories generation based on variational point processes,” in Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2023, pp. 4561–4571
2023
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Later among the works it cites.
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2023
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Y. Zhu, Y. Ye, S. Zhang, X. Zhao, and J. Yu, “Difftraj: Generating gps trajectory with diffusion probabilistic model,” in Thirty-seventh Conference on Neural Information Processing Systems , 2023
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J. Li and W. Zhao, “Trajectory generation of ultra-low-frequency travel routes in large-scale complex road networks,” Systems , vol. 11, no. 2, p. 61, 2023
2023
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2024
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