arXiv Machine Learning
MotiveMob: Motivation as Semantic Action for Closed-Loop Human Mobility Generation
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arXiv AI
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TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation
arXiv:2606. 12657v1 Announce Type: new Abstract: Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating realistic synthetic trajectory generation.
arXiv Machine Learning
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Relative Transitions, Not Absolute Destinations: A Transfer-and-Ground Framework for Target-Trajectory-Free Human Mobility Generation
The paper introduces Nomad, a transfer-and-ground framework for generating human mobility trajectories without target-city trajectory data. It learns relative transitions from source cities using POI attributes and then grounds these transitions onto a target city’s POI map via a behavior graph and exploration–return walk. Experiments across ten cities show Nomad improves trajectory fidelity and downstream utility by roughly 15% and 3% respectively over adaptation baselines.
arXiv Machine Learning
Sep 22
CALM: A Calibrated LLM Choice Network Framework for Activity-Based Traveler Simulation
CALM is a reproducible hybrid framework that combines an optional large language model (LLM) activity planner with calibrated stochastic choice, shared network feedback, memory and habit, typed feasibility checks, and deterministic offline replay. It executes a closed traveler‑day loop and evaluates each generative module against an empirical, reproducible baseline, using the 2024 New York City Citywide Mobility Survey data. The framework demonstrates significant improvements in mode‑choice accuracy, quantifies trade‑offs through live‑LLM ablation, and supports controlled stress testing and deterministic replay of downstream simulations.
arXiv AI
Jun 9
Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction
arXiv:2606. 08122v1 Announce Type: new Abstract: Predicting a user's next Point-of-Interest (POI) based on their historical check-in records is a fundamental task in location-based services.
arXiv AI
Jul 10
MobiDiff: Semantic-Aware Multi-Channel Discrete Diffusion for Human Mobility Data Generation
arXiv:2607. 08357v1 Announce Type: new Abstract: Human mobility data are essential for transportation optimization, urban planning, and resource allocation, yet real-world mobility data are costly to collect and difficult to share due to privacy concerns.
arXiv AI
Jun 2
MobEvolve: An Agentic Self-Evolving Heuristic System for Interpretable Human Mobility Generation
arXiv:2606. 01640v1 Announce Type: new Abstract: Human mobility generation aims to synthesize realistic trip chains for target populations based on individual features.