# Learning to Simulate Daily Activities via Modeling Dynamic Human Needs

arXiv: https://arxiv.org/abs/2302.10897 · HTML: https://mlsanity.com/paper/2302.10897/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 58 references that are in the map.
- First posted on arXiv 9 Feb 2023.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [Transformer Hawkes Process](https://mlsanity.com/paper/2002.09291/) (2020)
- [Neural Jump Stochastic Differential Equations](https://mlsanity.com/paper/1905.10403/) (2019)
- [Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning](https://mlsanity.com/paper/1805.10000/) (2018)
- [ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences](https://mlsanity.com/paper/2206.05291/) (2022)

## Built on by (most cited first, 20 of 20)

- [Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation](https://mlsanity.com/paper/2402.14744/) (2024)
- [Spatio-temporal Diffusion Point Processes](https://mlsanity.com/paper/2305.12403/) (2023)
- [Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment](https://mlsanity.com/paper/2312.11813/) (2023)
- [Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook](https://mlsanity.com/paper/2402.19348/) (2024)
- [Simulating Human-like Daily Activities with Desire-driven Autonomy](https://mlsanity.com/paper/2412.06435/) (2024)
- [COLA: Cross-city Mobility Transformer for Human Trajectory Simulation](https://mlsanity.com/paper/2403.01801/) (2024)
- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)
- [Chain-of-Planned-Behaviour Workflow Elicits Few-Shot Mobility Generation in LLMs](https://mlsanity.com/paper/2402.09836/) (2024)
- [Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation](https://mlsanity.com/paper/2306.09381/) (2023)
- [A Universal Model for Human Mobility Prediction](https://mlsanity.com/paper/2412.15294/) (2024)
- [GATSim: Urban Mobility Simulation with Generative Agents](https://mlsanity.com/paper/2506.23306/) (2025)
- [Pretrained Mobility Transformer: A Foundation Model for Human Mobility](https://mlsanity.com/paper/2406.02578/) (2024)
- [Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priors](https://mlsanity.com/paper/2412.05000/) (2024)
- [A Population-to-individual Tuning Framework for Adapting Pretrained LM to On-device User Intent Prediction](https://mlsanity.com/paper/2408.09815/) (2024)
- [Inferring the Unspoken: Aligning Embodied Agents with Implicit Preferences](https://mlsanity.com/paper/2502.00858/) (2025)
- [Next-Generation Simulation Illuminates Scientific Problems of Organised Complexity](https://mlsanity.com/paper/2401.09851/) (2024)
- [A Survey of Physics-Informed AI for Complex Urban Systems](https://mlsanity.com/paper/2506.13777/) (2025)
- [UniMove: A Unified Model for Multi-city Human Mobility Prediction](https://mlsanity.com/paper/2508.06986/) (2025)
- [MoveFM-R: Advancing Mobility Foundation Models via Language-driven Semantic Reasoning](https://mlsanity.com/paper/2509.22403/) (2025)
- [LLMs Reading the Rhythms of Daily Life: Aligned Understanding for Behavior Prediction and Generation](https://mlsanity.com/paper/2604.23578/) (2026)

## Abstract (arXiv)

Daily activity data that records individuals' various types of 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 to benefit practical applications. However, existing solutions, including rule-based methods with simplified assumptions of human behavior and data-driven methods directly fitting real-world data, both cannot fully qualify for matching reality. In this paper, 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. To enhance the fidelity and utility of the generated activity data, our core idea is to model the evolution of human needs as the underlying mechanism that drives activity generation in the simulation model. Specifically, this is achieved by a hierarchical model structure that disentangles different need levels, and the use of neural stochastic differential equations that successfully captures piecewise-continuous characteristics of need dynamics. Extensive experiments demonstrate that our framework outperforms the state-of-the-art baselines in terms of data fidelity and utility. Besides, we present the insightful interpretability of the need modeling. The code is available at https://github.com/tsinghua-fib-lab/SAND.