arXiv:2611.14536v1 Announce Type: cross
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 Mat\'ern 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.
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 Mat\'ern 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.
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Gaussian processes for real-time urban traffic estimation using a fleet of drones