UniMove: A Unified Model for Multi-city Human Mobility Prediction

• URL: http://arxiv.org/abs/2508.06986v2
• Date: Sat, 08 Nov 2025 14:44:15 GMT
• Title: UniMove: A Unified Model for Multi-city Human Mobility Prediction
• Authors: Chonghua Han, Yuan Yuan, Yukun Liu, Jingtao Ding, Jie Feng, Yong Li,
• Abstract summary: Human mobility prediction is vital for urban planning, transportation optimization, and personalized services.<n>Existing solutions often require training separate models for each city due to distinct spatial representations and geographic coverage.<n>We propose UniMove, a unified model for multi-city human mobility prediction.
• Score: 18.826615430413373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human mobility prediction is vital for urban planning, transportation optimization, and personalized services. However, the inherent randomness, non-uniform time intervals, and complex patterns of human mobility, compounded by the heterogeneity introduced by varying city structures, infrastructure, and population densities, present significant challenges in modeling. Existing solutions often require training separate models for each city due to distinct spatial representations and geographic coverage. In this paper, we propose UniMove, a unified model for multi-city human mobility prediction, addressing two challenges: (1) constructing universal spatial representations for effective token sharing across cities, and (2) modeling heterogeneous mobility patterns from varying city characteristics. We propose a trajectory-location dual-tower architecture, with a location tower for universal spatial encoding and a trajectory tower for sequential mobility modeling. We also design MoE Transformer blocks to adaptively select experts to handle diverse movement patterns. Extensive experiments across multiple datasets from diverse cities demonstrate that UniMove truly embodies the essence of a unified model. By enabling joint training on multi-city data with mutual data enhancement, it significantly improves mobility prediction accuracy by over 10.2\%. UniMove represents a key advancement toward realizing a true foundational model with a unified architecture for human mobility. We release the implementation at https://github.com/tsinghua-fib-lab/UniMove/.

Related papers

• CAMS: A CityGPT-Powered Agentic Framework for Urban Human Mobility Simulation [9.907406552578607]
  textbfCAMS is an agentic framework that leverages the language based urban foundation model to simulate human mobility in urban space.<n>textbfCAMS achieves superior performance without relying on externally provided geospatial information.
  arXiv  Detail & Related papers  (2025-06-16T15:24:07Z)
• TrajMoE: Spatially-Aware Mixture of Experts for Unified Human Mobility Modeling [10.338272381612112]
  We propose TrajMoE, a unified and scalable model for cross-city human mobility modeling.<n>TrajMoE addresses two key challenges: (1) inconsistent spatial semantics across cities, and (2) diverse urban mobility patterns.<n>Extensive experiments demonstrate that TrajMoE achieves up to 27% relative improvement over competitive mobility foundation models.
  arXiv  Detail & Related papers  (2025-05-24T12:17:47Z)
• DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers [61.92571851411509]
  We introduce a multimodal driving language based on interleaved image and action tokens, and develop DrivingGPT to learn joint world modeling and planning.<n>Our DrivingGPT demonstrates strong performance in both action-conditioned video generation and end-to-end planning, outperforming strong baselines on large-scale nuPlan and NAVSIM benchmarks.
  arXiv  Detail & Related papers  (2024-12-24T18:59:37Z)
• A Universal Model for Human Mobility Prediction [7.542075639525391]
  We propose a universal human mobility prediction model (named UniMob), which can be applied to both individual trajectory and crowd flow.<n>Our model achieves the highest performance improvement of more than 14% and 25% in MAPE and Accuracy@5.
  arXiv  Detail & Related papers  (2024-12-19T07:38:13Z)
• UniTraj: Learning a Universal Trajectory Foundation Model from Billion-Scale Worldwide Traces [64.24594320103066]
  Building a universal trajectory foundation model is a promising solution to address the limitations of existing trajectory modeling approaches.<n>We introduce UniTraj, a Universal Trajectory foundation model that aims to address these limitations through three key innovations.<n>First, we construct WorldTrace, an unprecedented dataset of 2.45 million trajectories with billions of GPS points spanning 70 countries.
  arXiv  Detail & Related papers  (2024-11-06T12:06:43Z)
• Multi-Transmotion: Pre-trained Model for Human Motion Prediction [68.87010221355223]
  Multi-Transmotion is an innovative transformer-based model designed for cross-modality pre-training. Our methodology demonstrates competitive performance across various datasets on several downstream tasks.
  arXiv  Detail & Related papers  (2024-11-04T23:15:21Z)
• ST-MoE-BERT: A Spatial-Temporal Mixture-of-Experts Framework for Long-Term Cross-City Mobility Prediction [6.0588503913405045]
  We propose a robust approach to predict human mobility patterns called ST-MoE-BERT. Our methodology integrates the Mixture-of-Experts architecture with BERT model to capture complex mobility dynamics. We demonstrate the effectiveness of the proposed model on GEO-BLEU and DTW, comparing it to several state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-18T00:32:18Z)
• MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility [52.0930915607703]
  Recent advances in Robotics and Embodied AI make public urban spaces no longer exclusive to humans. Micromobility enabled by AI for short-distance travel in public urban spaces plays a crucial component in the future transportation system. We present MetaUrban, a compositional simulation platform for the AI-driven urban micromobility research.
  arXiv  Detail & Related papers  (2024-07-11T17:56:49Z)
• Sports-Traj: A Unified Trajectory Generation Model for Multi-Agent Movement in Sports [53.637837706712794]
  We propose a Unified Trajectory Generation model, UniTraj, that processes arbitrary trajectories as masked inputs.<n>Specifically, we introduce a Ghost Spatial Masking (GSM) module, embedded within a Transformer encoder, for spatial feature extraction.<n>We benchmark three practical sports datasets, Basketball-U, Football-U, and Soccer-U, for evaluation.
  arXiv  Detail & Related papers  (2024-05-27T22:15:23Z)
• Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis [11.90100976089832]
  We develop a novel generative deep learning approach for human mobility modeling and synthesis. It incorporates both activity patterns and location trajectories using open-source data. The model can be fine-tuned with local data, allowing it to adapt to accurately represent mobility patterns across diverse regions.
  arXiv  Detail & Related papers  (2024-05-24T02:04:10Z)
• COLA: Cross-city Mobility Transformer for Human Trajectory Simulation [44.157114416533915]
  We develop a Cross-city mObiLity trAnsformer (COLA) with a dedicated model-agnostic transfer framework. COLA divides the Transformer into the private modules for city-specific characteristics and the shared modules for city-universal mobility patterns. Our implemented cross-city baselines have demonstrated its superiority and effectiveness.
  arXiv  Detail & Related papers  (2024-03-04T07:45:29Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.