# Data-driven generation of spatio-temporal routines in human mobility

arXiv: https://arxiv.org/abs/1607.05952 · HTML: https://mlsanity.com/paper/1607.05952/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2016.

## Built on by (most cited first, 19 of 19)

- [Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation](https://mlsanity.com/paper/2402.14744/) (2024)
- [Be More Real: Travel Diary Generation Using LLM Agents and Individual Profiles](https://mlsanity.com/paper/2407.18932/) (2024)
- [Spatiotemporal-Augmented Graph Neural Networks for Human Mobility Simulation](https://mlsanity.com/paper/2306.09381/) (2023)
- [Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis](https://mlsanity.com/paper/2405.17468/) (2024)
- [PateGail: A Privacy-Preserving Mobility Trajectory Generator with Imitation Learning](https://mlsanity.com/paper/2407.16729/) (2024)
- [Revisiting Synthetic Human Trajectories: Imitative Generation and Benchmarks Beyond Datasaurus](https://mlsanity.com/paper/2409.13790/) (2024)
- [Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy](https://mlsanity.com/paper/2201.01139/) (2022)
- [CATS: Conditional Adversarial Trajectory Synthesis for Privacy-Preserving Trajectory Data Publication Using Deep Learning Approaches](https://mlsanity.com/paper/2309.11587/) (2023)
- [Generating Synthetic Mobility Networks with Generative Adversarial Networks](https://mlsanity.com/paper/2202.11028/) (2022)
- [Human Mobility Prediction with Causal and Spatial-constrained Multi-task Network](https://mlsanity.com/paper/2206.05731/) (2022)
- [Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing](https://mlsanity.com/paper/2307.09494/) (2023)
- [CityBehavEx: A Scalable and Empirically Validated LLM-Assisted Urban Simulation Platform](https://mlsanity.com/paper/2607.12086/) (2026)
- [The Urban Impact of AI: Modeling Feedback Loops in Next-Venue Recommendation](https://mlsanity.com/paper/2504.07911/) (2025)
- [Deep Generative Model for Human Mobility Behavior](https://mlsanity.com/paper/2510.06473/) (2025)
- [M-STAR: Multi-Scale Spatiotemporal Autoregression for Human Mobility Modeling](https://mlsanity.com/paper/2512.07314/) (2025)
- [MobEvolve: An Agentic Self-Evolving Heuristic System for Interpretable Human Mobility Generation](https://mlsanity.com/paper/2606.01640/) (2026)
- [GenWorld: Empirically Grounded Urban Simulation Infrastructure for Scalable LLM-Agent Studies](https://mlsanity.com/paper/2606.27650/) (2026)
- [Towards Inclusive Mobility Modeling: Characterizing and Evaluating Elderly Trajectory Patterns in Urban Systems](https://mlsanity.com/paper/2606.31207/) (2026)
- [Relative Transitions, Not Absolute Destinations: A Transfer-and-Ground Framework for Target-Trajectory-Free Human Mobility Generation](https://mlsanity.com/paper/2610.02033/) (2026)

## Abstract (arXiv)

The generation of realistic spatio-temporal trajectories of human mobility is of fundamental importance in a wide range of applications, such as the developing of protocols for mobile ad-hoc networks or what-if analysis in urban ecosystems. Current generative algorithms fail in accurately reproducing the individuals' recurrent schedules and at the same time in accounting for the possibility that individuals may break the routine during periods of variable duration. In this article we present DITRAS (DIary-based TRAjectory Simulator), a framework to simulate the spatio-temporal patterns of human mobility. DITRAS operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory. We propose a data-driven algorithm which constructs a diary generator from real data, capturing the tendency of individuals to follow or break their routine. We also propose a trajectory generator based on the concept of preferential exploration and preferential return. We instantiate DITRAS with the proposed diary and trajectory generators and compare the resulting algorithm with real data and synthetic data produced by other generative algorithms, built by instantiating DITRAS with several combinations of diary and trajectory generators. We show that the proposed algorithm reproduces the statistical properties of real trajectories in the most accurate way, making a step forward the understanding of the origin of the spatio-temporal patterns of human mobility.