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Generating Daily Activities with Need Dynamics

Published: 22 February 2024 Publication History

Abstract

Daily activity data recording individuals’ various activities in daily life are widely used in many applications such as activity scheduling, activity recommendation, and policymaking. Though with high value, its accessibility is limited due to high collection costs and potential privacy issues. Therefore, simulating human activities to produce massive high-quality data is of great importance. However, existing solutions, including rule-based methods with simplified behavior assumptions and data-driven methods directly fitting real-world data, both cannot fully qualify for matching reality. In this article, motivated by the classic psychological theory, Maslow’s need theory describing human motivation, we propose a knowledge-driven simulation framework based on generative adversarial imitation learning. Our core idea is to model the evolution of human needs as the underlying mechanism that drives activity generation in the simulation model. Specifically, a hierarchical model structure that disentangles different need levels and the use of neural stochastic differential equations successfully capture the piecewise-continuous characteristics of need dynamics. Extensive experiments demonstrate that our framework outperforms the state-of-the-art baselines regarding data fidelity and utility. We also present the insightful interpretability of the need modeling. Moreover, privacy preservation evaluations validate that the generated data does not leak individual privacy. The code is available at https://github.com/tsinghua-fib-lab/Activity-Simulation-SAND.

References

[1]
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep learning with differential privacy. In CCS. 308–318.
[2]
Clayton P. Alderfer. 1969. An empirical test of a new theory of human needs. Organizational Behavior and Human Performance 4, 2 (1969), 142–175.
[3]
Galen Andrew, Steve Chien, and Nicolas Papernot. 2019. TensorFlow privacy. https://blog.tensorflow.org/2019/03/introducing-tensorflow-privacy-learning.html
[4]
Theo Arentze, Frank Hofman, Henk van Mourik, and Harry Timmermans. 2000. ALBATROSS: Multiagent, rule-based model of activity pattern decisions. Transportation Research Record 1706, 1 (2000), 136–144.
[5]
Joshua Auld and Abolfazl Kouros Mohammadian. 2012. Activity planning processes in the agent-based dynamic activity planning and travel scheduling (ADAPTS) model. Transp. Res. Part A Policy Pract. 46, 8 (2012), 1386–1403.
[6]
John J. Bartko. 1966. The intraclass correlation coefficient as a measure of reliability. Psychological Reports 19, 1 (1966), 3–11.
[7]
John L. Bowman and Moshe E. Ben-Akiva. 2001. Activity-based disaggregate travel demand model system with activity schedules. Transportation Research Part A: Policy and Practice 35, 1 (2001), 1–28.
[8]
Wei-Lun Chang and Soe-Tsyr Yuan. 2008. A synthesized model of Markov chain and ERG theory for behavior forecast in collaborative prototyping. JITTA 9, 2 (2008), 5.
[9]
Gang Chen, Sai Wu, Jingbo Zhou, and Anthony K. H. Tung. 2013. Automatic itinerary planning for traveling services. IEEE Transactions on Knowledge and Data Engineering 26, 3 (2013), 514–527.
[10]
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. 2018. Neural ordinary differential equations. arXiv preprint arXiv:1806.07366 (2018).
[11]
Kae H. Chung. 1969. A Markov chain model of human needs: An extension of Maslow’s need theory. Academy of Management Journal 12, 2 (1969), 223–234.
[12]
Edward De Brouwer, Jaak Simm, Adam Arany, and Yves Moreau. 2019. GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series. arXiv preprint arXiv:1905.12374 (2019).
[13]
Yves-Alexandre De Montjoye, César A. Hidalgo, Michel Verleysen, and Vincent D. Blondel. 2013. Unique in the crowd: The privacy bounds of human mobility. Scientific Reports 3, 1 (2013), 1–5.
[14]
Dick Ettema, Aloys Borgers, and Harry Timmermans. 1993. Simulation model of activity scheduling behavior. Transportation Research Record (1993), 1–1.
[15]
Jie Feng, Zeyu Yang, Fengli Xu, Haisu Yu, Mudan Wang, and Yong Li. 2020. Learning to simulate human mobility. In KDD. 3426–3433.
[16]
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. NIPS 27 (2014).
[17]
Alex Graves. 2013. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013).
[18]
Aditya Grover, Manik Dhar, and Stefano Ermon. 2018. Flow-GAN: Combining maximum likelihood and adversarial learning in generative models. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.
[19]
Vinayak Gupta and Srikanta Bedathur. 2022. ProActive: Self-attentive temporal point process flows for activity sequences. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 496–504.
[20]
Jonathan Ho and Stefano Ermon. 2016. Generative adversarial imitation learning. Advances in Neural Information Processing Systems 29 (2016), 4565–4573.
[21]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735–1780.
[22]
Yuchen Wu Depeng Jin Lina Yao Huandong Wang, Changzheng Gao and Yong Li. 2023. PateGail: A privacy-preserving mobility trajectory generator with imitation learning. In Proceedings of the AAAI Conference on Artificial Intelligence.
[23]
Martin Jacobsen and Joseph Gani. 2006. Point Process Theory and Applications: Marked Point and Piecewise Deterministic Processes. Springer.
[24]
Vikramaditya Jakkula and Diane J. Cook. 2007. Mining sensor data in smart environment for temporal activity prediction. KDD (2007).
[25]
Neziha Jaouedi, Francisco J. Perales, José Maria Buades, Noureddine Boujnah, and Med Salim Bouhlel. 2020. Prediction of human activities based on a new structure of skeleton features and deep learning model. Sensors 20, 17 (2020), 4944.
[26]
Junteng Jia and Austin R. Benson. 2019. Neural jump stochastic differential equations. arXiv preprint arXiv:1905.10403 (2019).
[27]
Linlang Jiang, Jingbo Zhou, Tong Xu, Yanyan Li, Hao Chen, and Dejing Dou. 2022. Time-aware neural trip planning reinforced by human mobility. In Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 1–8.
[28]
Linlang Jiang, Jingbo Zhou, Tong Xu, Yanyan Li, Hao Chen, Jizhou Huang, and Hui Xiong. 2021. Adversarial neural trip recommendation. arXiv preprint arXiv:2109.11731 (2021).
[29]
Sidney Katz. 1983. Assessing self-maintenance: Activities of daily living, mobility, and instrumental activities of daily living. Journal of the American Geriatrics Society 31, 12 (1983), 721–727.
[30]
Hamdi Kavak, Joon-Seok Kim, Andrew Crooks, Dieter Pfoser, Carola Wenk, and Andreas Züfle. 2019. Location-based social simulation. In SSTD. 218–221.
[31]
Joon-Seok Kim, Hyunjee Jin, Hamdi Kavak, Ovi Chris Rouly, Andrew Crooks, Dieter Pfoser, Carola Wenk, and Andreas Züfle. 2020. Location-based social network data generation based on patterns of life. In MDM. IEEE, 158–167.
[32]
Joon-Seok Kim, Hamdi Kavak, Umar Manzoor, Andrew Crooks, Dieter Pfoser, Carola Wenk, and Andreas Züfle. 2019. Simulating urban patterns of life: A geo-social data generation framework. In SIGSPATIAL. 576–579.
[33]
Diederik P. Kingma and Max Welling. 2019. An introduction to variational autoencoders. Found. Trends Mach. Learn. 12, 4 (2019), 307–392.
[34]
Ryuichi Kitamura, Eric I. Pas, Clarisse V. Lula, T. Keith Lawton, and Paul E. Benson. 1996. The sequenced activity mobility simulator (SAMS): An integrated approach to modeling transportation, land use and air quality. Transportation 23, 3 (1996), 267–291.
[35]
Patrick J. Laub, Thomas Taimre, and Philip K. Pollett. 2015. Hawkes processes. arXiv preprint arXiv:1507.02822 (2015).
[36]
Lincan Li, Kaixiang Yang, Fengji Luo, and Jichao Bi. 2023. STS-CCL: Spatial-temporal synchronous contextual contrastive learning for urban traffic forecasting. arXiv preprint arXiv:2307.02507 (2023).
[37]
Yuxuan Liang, Kun Ouyang, Hanshu Yan, Yiwei Wang, Zekun Tong, and Roger Zimmermann. 2021. Modeling trajectories with neural ordinary differential equations. In IJCAI. 1498–1504.
[38]
Nikolaos Limnios and Gheorghe Oprisan. 2012. Semi-Markov Processes and Reliability. Springer.
[39]
Jianhua Lin. 1991. Divergence measures based on the Shannon entropy. IEEE Transactions on Information theory 37, 1 (1991), 145–151.
[40]
Zinan Lin, Alankar Jain, Chen Wang, Giulia Fanti, and Vyas Sekar. 2020. Using GANs for sharing networked time series data: Challenges, initial promise, and open questions. In IMC. 464–483.
[41]
Yihong Ma, Patrick Gerard, Yijun Tian, Zhichun Guo, and Nitesh V. Chawla. 2022. Hierarchical spatio-temporal graph neural networks for pandemic forecasting. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 1481–1490.
[42]
Charles M. Macal and Michael J. North. 2005. Tutorial on agent-based modeling and simulation. In WSC. IEEE, 14–pp.
[43]
Abraham Harold Maslow. 1943. A theory of human motivation. Psychological Review 50, 4 (1943), 370.
[44]
Hongyuan Mei and Jason Eisner. 2016. The neural Hawkes process: A neurally self-modulating multivariate point process. arXiv preprint arXiv:1612.09328 (2016).
[45]
Bryan Minor, Janardhan Rao Doppa, and Diane J. Cook. 2015. Data-driven activity prediction: Algorithms, evaluation methodology, and applications. In KDD. 805–814.
[46]
Shakir Mohamed and Balaji Lakshminarayanan. 2016. Learning in implicit generative models. arXiv preprint arXiv:1610.03483 (2016).
[47]
Goran Murić, Alexey Tregubov, Jim Blythe, Andrés Abeliuk, Divya Choudhary, Kristina Lerman, and Emilio Ferrara. 2020. Massive cross-platform simulations of online social networks. In AAMAS. 895–903.
[48]
Michael J. North, Charles M. Macal, James St. Aubin, Prakash Thimmapuram, Mark Bragen, June Hahn, James Karr, Nancy Brigham, Mark E. Lacy, and Delaine Hampton. 2010. Multiscale agent-based consumer market modeling. Complexity 15, 5 (2010), 37–47.
[49]
Henry Friday Nweke, Ying Wah Teh, Mohammed Ali Al-Garadi, and Uzoma Rita Alo. 2018. Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. Expert Systems with Applications 105 (2018), 233–261.
[50]
Kun Ouyang, Reza Shokri, David S. Rosenblum, and Wenzhuo Yang. 2018. A non-parametric generative model for human trajectories. In IJCAI. 3812–3817.
[51]
Menghai Pan, Weixiao Huang, Yanhua Li, Xun Zhou, and Jun Luo. 2020. xGAIL: Explainable generative adversarial imitation learning for explainable human decision analysis. In KDD. 1334–1343.
[52]
Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. 2023. Generative agents: Interactive simulacra of human behavior. arXiv preprint arXiv:2304.03442 (2023).
[53]
Andreas Züfle, Hamdi Kavak, Joon-Seok Kim, Dieter Pfoser, Carola Wenk, Umar Manzoor, and Andrew Crooks. 2021. Towards Large-Scale Agent-Based Geospatial Simulation.
[54]
Jan-Hendrik Prinz, Hao Wu, Marco Sarich, Bettina Keller, Martin Senne, Martin Held, John D. Chodera, Christof Schütte, and Frank Noé. 2011. Markov models of molecular kinetics: Generation and validation. The Journal of Chemical Physics 134, 17 (2011), 174105.
[55]
Lawrence Rabiner and Biinghwang Juang. 1986. An introduction to hidden Markov models. IEEE ASSP Magazine 3, 1 (1986), 4–16.
[56]
John Rauschenberger, Neal Schmitt, and John E. Hunter. 1980. A test of the need hierarchy concept by a Markov model of change in need strength. Administrative Science Quarterly (1980), 654–670.
[57]
W. W. Recker and G. S. Root. 1981. Toward a dynamic model of individual activity pattern formulation. (1981).
[58]
Yulia Rubanova, Ricky T. Q. Chen, and David Duvenaud. 2019. Latent ODEs for irregularly-sampled time series. arXiv preprint arXiv:1907.03907 (2019).
[59]
Ilan Salomon. 1985. Telecommunications and travel: Substitution or modified mobility? Journal of Transport Economics and Policy (1985), 219–235.
[60]
Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, and Peter Battaglia. 2019. Hamiltonian graph networks with ode integrators. arXiv preprint arXiv:1909.12790 (2019).
[61]
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017).
[62]
Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, and An-Xiang Zeng. 2019. Virtual-taobao: Virtualizing real-world online retail environment for reinforcement learning. In AAAI, Vol. 33. 4902–4909.
[63]
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017. Membership inference attacks against machine learning models. In Proceedings of the 2017 IEEE Symposium on Security and Privacy (SP). IEEE, 3–18.
[64]
Christian Stab and Iryna Gurevych. 2014. Annotating argument components and relations in persuasive essays. In ACL. 1501–1510.
[65]
Richard S. Sutton and Andrew G. Barto. 1998. Introduction to Reinforcement Learning. Vol. 135.
[66]
Xuxiang Ta, Zihan Liu, Xiao Hu, Le Yu, Leilei Sun, and Bowen Du. 2022. Adaptive spatio-temporal graph neural network for traffic forecasting. Knowledge-Based Systems 242 (2022), 108199.
[67]
Chen-Ya Wang, Yueh-Hsun Wu, and Seng-Cho T. Chou. 2010. Toward a ubiquitous personalized daily-life activity recommendation service with contextual information: A services science perspective. Inf. Syst. E-Bus. Manag. 8, 1 (2010), 13–32.
[68]
Hua Wei, Dongkuan Xu, Junjie Liang, and Zhenhui Li. 2021. How do we move: Modeling human movement with system dynamics. In AAAI.
[69]
Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, and Wei Wang. 2017. Modeling trajectories with recurrent neural networks. In IJCAI.
[70]
Chugui Xu, Ju Ren, Deyu Zhang, Yaoxue Zhang, Zhan Qin, and Kui Ren. 2019. GANobfuscator: Mitigating information leakage under GAN via differential privacy. IEEE TIFS 14, 9 (2019), 2358–2371.
[71]
Fengli Xu, Zhen Tu, Yong Li, Pengyu Zhang, Xiaoming Fu, and Depeng Jin. 2017. Trajectory recovery from ash: User privacy is not preserved in aggregated mobility data. In WWW. 1241–1250.
[72]
Dingqi Yang, Daqing Zhang, Vincent W. Zheng, and Zhiyong Yu. 2014. Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. TSMC 45, 1 (2014), 129–142.
[73]
Jihang Ye, Zhe Zhu, and Hong Cheng. 2013. What’s your next move: User activity prediction in location-based social networks. In Proceedings of the 2013 SIAM International Conference on Data Mining. SIAM, 171–179.
[74]
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2017. Seqgan: Sequence generative adversarial nets with policy gradient. In AAAI, Vol. 31.
[75]
Yuan Yuan, Jingtao Ding, Chenyang Shao, Depeng Jin, and Yong Li. 2023. Spatio-temporal diffusion point processes. arXiv preprint arXiv:2305.12403 (2023).
[76]
Yuan Yuan, Jingtao Ding, Huandong Wang, Depeng Jin, and Yong Li. 2022. Activity trajectory generation via modeling spatiotemporal dynamics. In KDD. 4752–4762.
[77]
Yuan Yuan, Huandong Wang, Jingtao Ding, Depeng Jin, and Yong Li. 2023. Learning to simulate daily activities via modeling dynamic human needs. In Proceedings of the ACM Web Conference 2023. 906–916.
[78]
Qianru Zhang, Chao Huang, Lianghao Xia, Zheng Wang, Zhonghang Li, and Siuming Yiu. 2023. Automated spatio-temporal graph contrastive learning. In Proceedings of the ACM Web Conference 2023. 295–305.
[79]
Xin Zhang, Yanhua Li, Xun Zhou, and Jun Luo. 2019. Unveiling taxi drivers’ strategies via cgail: Conditional generative adversarial imitation learning. In ICDM. IEEE, 1480–1485.
[80]
Jingbo Zhou and Anthony K. H. Tung. 2015. Smiler: A semi-lazy time series prediction system for sensors. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. 1871–1886.
[81]
Jingbo Zhou, Anthony K. H. Tung, Wei Wu, and Wee Siong Ng. 2013. A “semi-lazy” approach to probabilistic path prediction in dynamic environments. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 748–756.
[82]
Simiao Zuo, Haoming Jiang, Zichong Li, Tuo Zhao, and Hongyuan Zha. 2020. Transformer hawkes process. In ICML. PMLR, 11692–11702.

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  • (2024)A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent PredictionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671984(896-907)Online publication date: 25-Aug-2024
  • (2024)STAGE: a spatiotemporal-knowledge enhanced multi-task generative adversarial network (GAN) for trajectory generationInternational Journal of Geographical Information Science10.1080/13658816.2024.2381146(1-28)Online publication date: 29-Jul-2024
  • (2024)Generative Action Procedure Manzai Scenario Based on Maslow’s Stages of Need TheoryAdvances in Network-Based Information Systems10.1007/978-3-031-72325-4_31(319-327)Online publication date: 20-Sep-2024

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cover image ACM Transactions on Intelligent Systems and Technology
ACM Transactions on Intelligent Systems and Technology  Volume 15, Issue 2
April 2024
481 pages
EISSN:2157-6912
DOI:10.1145/3613561
  • Editor:
  • Huan Liu
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 February 2024
Online AM: 14 December 2023
Accepted: 20 November 2023
Revised: 23 October 2023
Received: 14 June 2023
Published in TIST Volume 15, Issue 2

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  1. Daily activities
  2. generation
  3. need dynamics
  4. GAIL

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  • National Key Research and Development Program of China
  • National Nature Science Foundation of China
  • Young Elite Scientists Sponsorship Program by CIC

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  • (2024)A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent PredictionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671984(896-907)Online publication date: 25-Aug-2024
  • (2024)STAGE: a spatiotemporal-knowledge enhanced multi-task generative adversarial network (GAN) for trajectory generationInternational Journal of Geographical Information Science10.1080/13658816.2024.2381146(1-28)Online publication date: 29-Jul-2024
  • (2024)Generative Action Procedure Manzai Scenario Based on Maslow’s Stages of Need TheoryAdvances in Network-Based Information Systems10.1007/978-3-031-72325-4_31(319-327)Online publication date: 20-Sep-2024

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