arXiv：cs.LG· Federica Bragone, Matthieu Barreau·· 4 小时前AI 评分31
用随机物理信息神经细胞自动机学习交通流动力学
Learning Traffic Flow Dynamics with Stochastic Physics-Informed Neural Cellular Automata
AI 导读
研究者提出物理信息神经细胞自动机（PI-NCA），在标准 NCA 基础上设计出与道路拓扑一致、并保证车辆总数守恒的神经架构，将学习到的转移规则约束在物理可行范围内，并进一步参数化概率转移规则以扩展到随机动力学。在 Nagel-Schreckenberg 与 Kerner-Klenov-Wolf 细胞自动机生成的多个交通场景上，PI-NCA 成功学到两种交通模型的动力学并持续优于标准 NCA。
正文
Abstract:Traffic flow modeling is essential for understanding and predicting the collective dynamics of vehicles on road networks. Cellular automata provide a simple, interpretable yet powerful framework for representing these dynamics via local interaction rules, while retaining the ability to reproduce complex macroscopic traffic phenomena. However, learning local transition rules from data while preserving physically meaningful constraints remains challenging, particularly for stochastic models. In this work, we propose a physics-informed neural cellular automaton (PI-NCA) for data-driven traffic flow modeling. Building on the standard neural cellular automaton (NCA), we design a neural architecture that is physically consistent with the road topology and guarantees conservation of the total number of vehicles, thereby constraining the learned transition rules to physically admissible dynamics. We further extend this framework to stochastic dynamics by parameterizing probabilistic transition rules while preserving the same physics-informed constraints. We evaluate the proposed models on multiple traffic scenarios generated by the well-established Nagel-Schreckenberg and Kerner-Klenov-Wolf cellular automata. The results demonstrate that the PI-NCA successfully learns the dynamics of both traffic models and consistently outperforms a standard NCA, while the stochastic extension captures probabilistic transition rules without compromising the imposed physical constraints.
| Comments: | 45 pages, 16 figures |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.09946 [cs.LG] |
| (or arXiv:2610.09946v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09946 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Federica Bragone [view email]
[v1]
Wed, 7 Oct 2026 12:27:22 UTC (1,748 KB)
来源：arXiv：cs.LG · arxiv.org