# Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy

arXiv: https://arxiv.org/abs/2201.01139 · HTML: https://mlsanity.com/paper/2201.01139/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 66 references that are in the map.
- First posted on arXiv 4 Jan 2022.

## Builds on (most cited first, 12 of 12)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Data-driven generation of spatio-temporal routines in human mobility](https://mlsanity.com/paper/1607.05952/) (2016)
- [Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.1)](https://mlsanity.com/paper/2004.04145/) (2020)
- [Exploring universal patterns in human home-work commuting from mobile phone data](https://mlsanity.com/paper/1311.2911/) (2013)
- [The Effect of Recency to Human Mobility](https://mlsanity.com/paper/1504.01442/) (2015)
- [Privacy in trajectory micro-data publishing : a survey](https://mlsanity.com/paper/1903.12211/) (2019)
- [Extracting Trips from Multi-Sourced Data for Mobility Pattern Analysis: An App-Based Data Example](https://mlsanity.com/paper/1912.01835/) (2019)
- [The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective](https://mlsanity.com/paper/1603.04012/) (2016)
- [A New Insight into Land Use Classification Based on Aggregated Mobile Phone Data](https://mlsanity.com/paper/1310.6129/) (2013)
- [Spatio-Temporal Techniques for User Identification by means of GPS Mobility Data](https://mlsanity.com/paper/1501.06814/) (2015)
- [The tradeoff between the utility and risk of location data and implications for public good](https://mlsanity.com/paper/1905.09350/) (2019)

## Built on by (most cited first, 7 of 7)

- [Pretrained Mobility Transformer: A Foundation Model for Human Mobility](https://mlsanity.com/paper/2406.02578/) (2024)
- [Generating Synthetic Mobility Networks with Generative Adversarial Networks](https://mlsanity.com/paper/2202.11028/) (2022)
- [A deep learning framework to generate realistic population and mobility data](https://mlsanity.com/paper/2211.07369/) (2022)
- [From Narrative to Action: A Hierarchical LLM-Agent Framework for Human Mobility Generation](https://mlsanity.com/paper/2510.24802/) (2025)
- [Differentially Private Non Parametric Copulas: Generating synthetic data with non parametric copulas under privacy guarantees](https://mlsanity.com/paper/2409.18611/) (2024)
- [A Comprehensive Evaluation Framework for Synthetic Trip Data Generation in Public Transport](https://mlsanity.com/paper/2510.24375/) (2025)
- [Encounter-Driven City-Scale Human Mobility Generation with Large Language Models](https://mlsanity.com/paper/2610.17672/) (2026)

## Abstract (arXiv)

Location data collected from mobile devices represent mobility behaviors at individual and societal levels. These data have important applications ranging from transportation planning to epidemic modeling. However, issues must be overcome to best serve these use cases: The data often represent a limited sample of the population and use of the data jeopardizes privacy. To address these issues, we present and evaluate a system for generating synthetic mobility data using a deep recurrent neural network (RNN) which is trained on real location data. The system takes a population distribution as input and generates mobility traces for a corresponding synthetic population. Related generative approaches have not solved the challenges of capturing both the patterns and variability in individuals' mobility behaviors over longer time periods, while also balancing the generation of realistic data with privacy. Our system leverages RNNs' ability to generate complex and novel sequences while retaining patterns from training data. Also, the model introduces randomness used to calibrate the variation between the synthetic and real data at the individual level. This is to both capture variability in human mobility, and protect user privacy. Location based services (LBS) data from more than 22,700 mobile devices were used in an experimental evaluation across utility and privacy metrics. We show the generated mobility data retain the characteristics of the real data, while varying from the real data at the individual level, and where this amount of variation matches the variation within the real data.