Wenwei Yue

Aoyu Pang, Maonan Wang, Zifan Sha et al.

Urban Air Mobility (UAM) has emerged as a transformative solution to alleviate urban congestion by utilizing low-altitude airspace, thereby reducing pressure on ground transportation networks. To enable truly efficient and seamless door-to-door travel experiences, UAM requires close integration with existing ground transportation infrastructure. However, current research on optimal integrated routing strategies for passengers in air-ground mobility systems remains limited, with a lack of systematic exploration.To address this gap, we first propose a unified optimization model that integrates strategy selection for both air and ground transportation. This model captures the dynamic characteristics of multimodal transport networks and incorporates real-time traffic conditions alongside passenger decision-making behavior. Building on this model, we propose a Unified Air-Ground Mobility Coordination (UAGMC) framework, which leverages deep reinforcement learning (RL) and Vehicle-to-Everything (V2X) communication to optimize vertiport selection and dynamically plan air taxi routes. Experimental results demonstrate that UAGMC achieves a 34\% reduction in average travel time compared to conventional proportional allocation methods, enhancing overall travel efficiency and providing novel insights into the integration and optimization of multimodal transportation systems. This work lays a solid foundation for advancing intelligent urban mobility solutions through the coordination of air and ground transportation modes. The related code can be found at https://github.com/Traffic-Alpha/UAGMC.

Jingli Li, Yiyan Ma, Bo Ai et al.

Low-altitude wireless networks (LAWN) require drones to follow specific trajectories controlled by ground base stations (GBSs). However, given complex low-altitude channel conditions and limited spectrum and power resources, sensing errors and wireless link unreliability cannot be ignored, leading to trajectory deviations that threaten flight safety. To address this issue, this paper proposes an integrated sensing-communication-control (ISCC) closed-loop trajectory tracking approach, aiming to reveal the coupling mechanisms among communication, sensing, and control during drone flight. In detail, we incorporate sensing errors in trajectory state estimation, packet losses in control command transmission, and finite blocklength transmission effects into the closed-loop dynamics. First, through theoretical analysis, we identify the dominant role of the time-frequency resources allocated to control in ensuring system stability and derive a lower bound on the resources required to guarantee stable operation. Second, to minimize tracking error, we formulate a time-frequency resource allocation optimization problem for the sensing, communication, and control components, subject to constraints on communication rate and closed-loop stability. Accordingly, a solution algorithm based on successive convex approximation is proposed. Third, simulation results indicate that once stability is ensured, system performance is primarily determined by sensing accuracy, with the trajectory tracking error exhibiting an approximately linear dependence on the position error bound. Finally, it is shown that the proposed ISCC scheme avoids trajectory divergence under FBL transmission compared with ISCC designs ignoring control packet loss, and could achieve decimeter-level average tracking accuracy, reducing the error to only 17.37% of that observed in the baseline global navigation satellite system scheme.