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[2602.20643] TrajGPT-R: Generating Urban Mobility Trajectory with Reinforcement Learning-Enhanced Generative Pre-trained Transformer
Summary
The paper presents TrajGPT-R, a framework for generating urban mobility trajectories using a reinforcement learning-enhanced generative transformer model, addressing data privacy issues and improving urban planning insights.
Why It Matters
As urbanization increases, understanding mobility patterns is crucial for effective urban planning. This research provides a novel approach to generate synthetic mobility data, which can help in traffic management and urban development while respecting privacy concerns.
Key Takeaways
- Introduces TrajGPT-R, a model for generating urban mobility trajectories.
- Utilizes reinforcement learning and inverse reinforcement learning for improved data generation.
- Demonstrates superior performance over traditional models in reliability and diversity.
- Offers a publicly available implementation for further research and application.
- Addresses privacy concerns in urban mobility data collection.
Computer Science > Machine Learning arXiv:2602.20643 (cs) [Submitted on 24 Feb 2026] Title:TrajGPT-R: Generating Urban Mobility Trajectory with Reinforcement Learning-Enhanced Generative Pre-trained Transformer Authors:Jiawei Wang, Chuang Yang, Jiawei Yong, Xiaohang Xu, Hongjun Wang, Noboru Koshizuka, Shintaro Fukushima, Ryosuke Shibasaki, Renhe Jiang View a PDF of the paper titled TrajGPT-R: Generating Urban Mobility Trajectory with Reinforcement Learning-Enhanced Generative Pre-trained Transformer, by Jiawei Wang and 8 other authors View PDF HTML (experimental) Abstract:Mobility trajectories are essential for understanding urban dynamics and enhancing urban planning, yet access to such data is frequently hindered by privacy concerns. This research introduces a transformative framework for generating large-scale urban mobility trajectories, employing a novel application of a transformer-based model pre-trained and fine-tuned through a two-phase process. Initially, trajectory generation is conceptualized as an offline reinforcement learning (RL) problem, with a significant reduction in vocabulary space achieved during tokenization. The integration of Inverse Reinforcement Learning (IRL) allows for the capture of trajectory-wise reward signals, leveraging historical data to infer individual mobility preferences. Subsequently, the pre-trained model is fine-tuned using the constructed reward model, effectively addressing the challenges inherent in traditional RL-based autoreg...