用无人机编队与高斯过程实现城市交通实时估计
Gaussian processes for real-time urban traffic estimation using a fleet of drones
AI 导读
一项研究提出时空高斯过程框架，从无人机编队的部分航拍观测重建全路网交通状态：用图 Matérn 核嵌入路网拓扑，用指数时间核刻画时间依赖，并以历史环形检测器数据作为先验均值实时更新。在巴塞罗那微观仿真和雅典真实无人机数据两个案例中，该方法在估计和预测车辆行驶小时数（VHT）上优于历史基线，可显著减少所需机队规模；利用预测不确定性引导无人机移动还能带来额外性能提升。
正文 · 原文
Abstract:Real-time urban traffic monitoring is essential for modern city management, yet conventional static sensors provide only limited spatial coverage. Autonomous drone fleets can complement this infrastructure by offering flexible, high-resolution mobile sensing across different parts of the network. This paper introduces a spatio-temporal Gaussian process (GP) framework for reconstructing network-wide traffic states from partial aerial observations. The model accounts for the physical structure of the road network through a graph Matérn kernel, which embeds network topology into the spatial covariance structure, and captures temporal dependencies through an exponential temporal kernel. Within this framework, historical loop detector data provide an informative prior mean that is updated in real time using incoming drone observations, yielding network-wide point estimates together with predictive uncertainty. We validate our framework in two distinct case studies: a microscopic simulation of Barcelona and real-world drone data from Athens. The results demonstrate that our approach outperforms historical baselines in estimating and predicting Vehicle Hours Traveled (VHT), notably reducing the required fleet size. Furthermore, leveraging the model's predictive uncertainty to guide drone movements yields additional performance gains, highlighting the potential for future uncertainty-aware fleet routing algorithms.
| Subjects: | Applications (stat.AP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY) |
| Cite as: | arXiv:2611.14536 [stat.AP] |
| (or arXiv:2611.14536v1 [stat.AP] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14536 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Batuhan Avci [view email]
[v1]
Tue, 13 Oct 2026 15:09:36 UTC (591 KB)
来源：arXiv cs.AI · arxiv.org