urban infrastructure planners

Abstract: Deployment optimization of base stations (BSs) is critical for improving cellular network performance metrics such as coverage and capacity, impacting user experience, scalability, and infrastructure cost-effectiveness. Traditional methods that optimize precise BS locations do not generally provide broader insights into spatial deployment priorities or alternative viable deployments. Stochastic geometry approaches, on the other hand, offer a more interpretable intensity-based framework, but rarely utilize real-world user location data or investigate optimal BS deployment strategies. In this work, we propose a data-driven stochastic geometry-based approach that optimizes BS deployment intensity to maximize a network performance metric using samples from an unknown user-location distribution. We specialize the framework to an inhomogeneous Poisson point process (PPP) for cellular coverage analysis. The resulting infinite-dimensional problem is discretized over the deployment area and solved using sampled user locations. We characterize the discretization and sampling errors, apply stochastic mirror ascent to solve the empirical problem, and analyze the selection of concrete BS deployments using independent validation samples. Unlike traditional methods, our approach yields an optimized spatial intensity that reveals deployment priorities across regions and accommodates practical deployment constraints, multi-tier networks, and incremental deployment scenarios. Numerical experiments with synthetic and real-world spatial demand data demonstrate the proposed approach under different network configurations.

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.

Abstract: In the era of the Internet of Things (IoT), coordinating connected electric vehicle (EV) charging scheduling to balance EV charging satisfaction, station profitability, and smart grid stability presents a complex multi-objective challenge. Existing Multi-Agent Reinforcement Learning (MARL) approaches often struggle with high-dimensional state spaces generated by massive IoT sensing data and conflicting stakeholder interests. This paper proposes a novel LLM-enhanced MARL framework that, for the first time, simultaneously optimizes the Grid, EVs, and Stations within a unified loop. By integrating Large Language Model (LLM), we address two critical bottlenecks: interpretable feature selection and adaptive multi-objective balancing. The LLM analyzes real-time IoT-collected environmental states to extract physically significant features and dynamically assigns weights to conflicting objectives-including profit, user satisfaction, and grid load-using semantic reasoning instead of complex manual tuning. Extensive experiments demonstrate that our framework significantly outperforms state-of-the-art baselines, achieving superior market efficiency while reducing training time by over 70%. This approach offers a scalable, transparent solution for efficient and sustainable IoT-enabled urban charging infrastructure management.