# TS-Mob: Social and Geographical-Aware Time Series Foundation-Model Framework for Human Mobility Prediction

arXiv: https://arxiv.org/abs/2507.00945 · HTML: https://mlsanity.com/paper/2507.00945/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 40 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 19 of 19)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Graph Neural Network for Traffic Forecasting: A Survey](https://mlsanity.com/paper/2101.11174/) (2021)
- [PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting](https://mlsanity.com/paper/2210.08964/) (2022)
- [Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction](https://mlsanity.com/paper/1802.08714/) (2018)
- [Where Would I Go Next? Large Language Models as Human Mobility Predictors](https://mlsanity.com/paper/2308.15197/) (2023)
- [A Survey on Deep Learning for Human Mobility](https://mlsanity.com/paper/2012.02825/) (2020)
- [Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information](https://mlsanity.com/paper/2012.00489/) (2020)
- [AgentMove: A Large Language Model based Agentic Framework for Zero-shot Next Location Prediction](https://mlsanity.com/paper/2408.13986/) (2024)
- [Attentive Crowd Flow Machines](https://mlsanity.com/paper/1809.00101/) (2018)
- [Large Language Models are Zero-Shot Next Location Predictors](https://mlsanity.com/paper/2405.20962/) (2024)
- [Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Models](https://mlsanity.com/paper/2411.00823/) (2024)
- [Vision-based Multi-future Trajectory Prediction: A Survey](https://mlsanity.com/paper/2302.10463/) (2023)
- [Enhancing Large Language Models for Mobility Analytics with Semantic Location Tokenization](https://mlsanity.com/paper/2506.11109/) (2025)
- [Enhancing crowd flow prediction in various spatial and temporal granularities](https://mlsanity.com/paper/2203.07372/) (2022)

## Abstract (arXiv)

Short-term forecasting of aggregated human mobility flows supports urban planning, intelligent transportation systems, and emergency response, yet existing models often require substantial mobility history and learn spatial structure implicitly through grids or graphs. Time series foundation models provide strong temporal priors but typically lack explicit geographic and social conditioning for origin-destination interactions. We introduce TS-Mob, a framework that conditions a fine-tuned time series foundation model (TimesFM) forecaster on a gravity-inspired destination-attractiveness index that encodes geographic and social signals computed from open data (living population, centroid distances, and Overture POI counts), together with weather covariates. Evaluated on commonly used benchmarks like Bike New York City, Taxi Beijing, and a nation-scale Spain origin-destination matrix estimated through mobile phone data, TS-Mob outperforms classical, deep spatio-temporal, and a set of foundation model-based baselines across RMSE, MAE, and CPC, with gains up to 78.71% lower MAE and 137.93% higher CPC over the best classical baseline, and up to about 4.27% lower RMSE over the strongest foundation baseline. Stratified analyses further show robustness across different temporal regimes, like weekdays/weekends.