# Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis

arXiv: https://arxiv.org/abs/2405.17468 · HTML: https://mlsanity.com/paper/2405.17468/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 77 references that are in the map.
- First posted on arXiv 24 May 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Data-driven generation of spatio-temporal routines in human mobility](https://mlsanity.com/paper/1607.05952/) (2016)
- [Semantic Trajectory Data Mining with LLM-Informed POI Classification](https://mlsanity.com/paper/2405.11715/) (2024)
- [Fairness-enhancing deep learning for ride-hailing demand prediction](https://mlsanity.com/paper/2303.05698/) (2023)
- [An analytical framework to nowcast well-being using mobile phone data](https://mlsanity.com/paper/1606.06279/) (2016)
- [A Bayesian Optimization approach for calibrating large-scale activity-based transport models](https://mlsanity.com/paper/2302.03480/) (2023)
- [Impact of COVID-19 behavioral inertia on reopening strategies for New York City Transit](https://mlsanity.com/paper/2006.13368/) (2020)

## Built on by (most cited first, 10 of 10)

- [NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks](https://mlsanity.com/paper/2409.03024/) (2024)
- [Human Mobility Modeling with Household Coordination Activities under Limited Information via Retrieval-Augmented LLMs](https://mlsanity.com/paper/2409.17495/) (2024)
- [Learning Universal Human Mobility Patterns with a Foundation Model for Cross-domain Data Fusion](https://mlsanity.com/paper/2503.15779/) (2025)
- [Synthesising Activity Participations and Scheduling with Deep Generative Machine Learning](https://mlsanity.com/paper/2501.10221/) (2025)
- [Reconstructing Human Mobility Pattern: A Semi-Supervised Approach for Cross-Dataset Transfer Learning](https://mlsanity.com/paper/2410.03788/) (2024)
- [MobiVerse: Scaling Urban Mobility Simulation with Hybrid Lightweight Domain-Specific Generator and Large Language Models](https://mlsanity.com/paper/2506.21784/) (2025)
- [Next-Generation Travel Demand Modeling with a Generative Framework for Household Activity Coordination](https://mlsanity.com/paper/2507.08871/) (2025)
- [Beyond 9-to-5: A Generative Model for Augmenting Mobility Data of Underrepresented Shift Workers](https://mlsanity.com/paper/2507.19510/) (2025)
- [From Narrative to Action: A Hierarchical LLM-Agent Framework for Human Mobility Generation](https://mlsanity.com/paper/2510.24802/) (2025)
- [Markovian Reeb Graphs for Simulating Spatiotemporal Patterns of Life](https://mlsanity.com/paper/2510.03152/) (2025)

## Abstract (arXiv)

Human mobility plays a crucial role in transportation, urban planning, and public health, but current approaches face important limitations. Existing deep learning models tend to overlook the semantic interdependencies among activities and households and rely on restricted GPS data, while activity-based models depend on rigid assumptions and extensive data, making them costly and difficult to adapt to new regions, especially those with limited conventional travel data. To address these limitations, we propose a generative Transformer model that uses socio-demographic and household attributes to synthesize daily activity chains, with a location module assigning spatial zones to produce complete daily trajectories. Trained on open-source and widely available household travel survey data and then fine-tuned with local data, the model captures national activity patterns and transfers effectively to California, Washington, and Mexico City. This approach offers potential for advancing synthetic human mobility modeling and provides urban planners and policymakers with improved tools for simulating transportation systems and supporting decisions in urban development and public health. Its practical utility is demonstrated through large-scale traffic simulations in Los Angeles County. Compared to the SCAG Activity-Based Model (ABM), the proposed method produces consistent spatial demand patterns, achieving an activity location cosine similarity of 0.997 and a network-level vehicle-miles-traveled Mean Absolute Percentage Error (MAPE) of 4.97%. Compared to real-world observations from Caltrans PeMS, the simulated traffic achieves MAPE of 5.85% for traffic volume and 4.36% for speed on California's I-405 corridor, presenting the practicality of learning-based synthetic mobility for regional simulation and planning.