Intelligent Optimizations for UAV, Digital Twin, and ISCC Enabled Intelligent Transportation Systems

The evolution of intelligent transportation systems necessitate the integration of advanced technologies to address challenges in data processing, real-time decision-making, and network coverage. In this paper, we present a novel intelligent transportation system architecture that synergizes Unmanned Aerial Vehicles (UAVs), Digital Twins (DTs), and an Integrated Sensing, Communication, and Computation (ISCC) framework. In this system, UAVs equipped with edge computing capabilities collaborate with the ground-based cloud center to process data collected by perception devices (PDs). Each UAV and PD is mirrored by a Digital Twin (DT) at the base station, enabling real-time monitoring and predictive analytics. We intend to maximize the network lifetime and minimize the economic overhead, which is achieved through the joint optimization of the association policies of UEs, input data caching decisions, UAVs’ flight trajectory and speed, task processing mode selection and data unload proportion allocation under joint processing. To tackle the inherent challenges of nonlinearity, dynamic network conditions, and heterogeneous data sources, the problem is modeled as a Markov Decision Process (MDP) and solved using an enhanced Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, adapted to handle both continuous and discrete action spaces. The experimental results show that compared to the baseline algorithm, this approach not only exhibits faster convergence but also achieves outstanding performance in maximizing system utility for intelligent transportation systems.