As urban traffic networks grow more complex, seamless interaction between vehicles and infrastructure is critical, motivating Vehicle-to-Everything (V2X)-enabled intelligent transportation systems. Since vehicles are the main transport agents and traffic signals a key infrastructure component, jointly optimizing navigation and signal control is essential for sustainable V2X systems. However, existing methods often ignore long-range dependencies in road networks and lack effective large-scale vehicle coordination, limiting their ability to manage complex flows. To address this, we propose FCV2X-Net, a unified framework that enhances foresight and co-ordination in navigation and control. It consists of: (1) a Bayesian Graph Convolutional Network (BGCN)-based module with adaptive adjacency for modeling implicit long-range correlations; (2) a mean field-based intention propagation mechanism for scalable vehicle coordination; and (3) an intention-aware signal control module that adapts to aggregated vehicle intentions. Experiments on large-scale scenarios with 50 intersections show that FCV2X-Net increases vehicle throughput by 7.6% and reduces travel time by 7.2%, demonstrating its effectiveness for sustainable urban mobility. Codes and datasets are available at: https://github.com/JinweiZzz/FCV2X-Net.

Training reinforcement learning (RL) agents requires extensive trials and errors, which becomes prohibitively time-consuming in systems with costly reward evaluations. To address this challenge, we propose adaptive reward modeling (AdaReMo) which accelerates RL training by decomposing the complicated reward function into multiple localized fast reward models approximating direct reward evaluation with neural networks. These models dynamically adapt to the agent’s evolving policy by fitting the currently explored subspace with the latest trajectories, ensuring accurate reward estimation throughout the entire training process while significantly reducing computational overhead. We empirically show that AdaReMo not only achieves over 1,000 times speedup but also improves the performance by 14.6% over state-of-the-art approaches across three expensive-to-evaluate systems---molecular generation, epidemic control, and spatial planning. Code and data for the project are provided at https://github.com/tsinghua-fib-lab/AdaReMo.

The facility location problem (FLP) is a classical combinatorial optimization challenge aimed at strategically laying out facilities to maximize their accessibility. In this paper, we propose a reinforcement learning method tailored to solve large-scale urban FLP, capable of producing near-optimal solutions at superfast inference speed. We distill the essential swap operation from local search, and simulate it by intelligently selecting edges on a graph of urban regions, guided by a knowledge-informed graph neural network, thus sidestepping the need for heavy computation of local search. Extensive experiments on four US cities with different geospatial conditions demonstrate that our approach can achieve comparable performance to commercial solvers with less than 5% accessibility loss, while displaying up to 1000 times speedup. We deploy our model as an online geospatial application at https://huggingface.co/spaces/tsinghua-fib-lab/MFLP.

Millions of slum dwellers suffer from poor accessibility to urban services due to inadequate road infrastructure within slums, and road planning for slums is critical to the sustainable development of cities. Existing re-blocking or heuristic methods are either time-consuming which cannot generalize to different slums, or yield sub-optimal road plans in terms of accessibility and construction costs. In this paper, we present a deep reinforcement learning based approach to automatically layout roads for slums. We propose a generic graph model to capture the topological structure of a slum, and devise a novel graph neural network to select locations for the planned roads. Through masked policy optimization, our model can generate road plans that connect places in a slum at minimal construction costs. Extensive experiments on real-world slums in different countries verify the effectiveness of our model, which can significantly improve accessibility by 14.3% against existing baseline methods. Further investigations on transferring across different tasks demonstrate that our model can master road planning skills in simple scenarios and adapt them to much more complicated ones, indicating the potential of applying our model in real-world slum upgrading. The code and data are available at https://github.com/tsinghua-fib-lab/road-planning-for-slums.