Accurate and timely forecasting of traffic on local road networks is crucial for deploying effective dynamic traffic control, advanced route planning, and navigation services. This task is particularly challenging due to complex spatio-temporal dependencies arising from non-Euclidean spatial relations in road networks and non-linear temporal dynamics influenced by changing road conditions. This paper introduces the spatio-temporal network embedding (STNE) model, a novel deep learning framework tailored for learning and forecasting graph-structured traffic data over extended input sequences. Unlike traditional convolutional neural networks (CNNs), the model employs graph convolutional networks (GCNs) to capture the spatial characteristics of local road network topologies. Moreover, the segmentation of very long input traffic data into multiple sub-sequences, based on significant temporal properties such as closeness, periodicity, and trend, is performed. Multi-dimensional long short-term memory neural networks (MDLSTM) are utilized to flexibly access multi-dimensional context. Experimental results demonstrate that the STNE model surpasses state-of-the-art traffic forecasting benchmarks on two large-scale real-world traffic datasets.

Air transportation is a huge, complex system with emergence and self-organization, which makes it important for it to be modelled. In this paper, to model the air transportation system with more accuracy, from physical facilities to traffic applications, three-layer networks, including the air route network, the city-pair airline network, and flight operation network, are built, where the air route network is regarded as the physical layer, and city-pair airline network and flight operation network are the application layers. Furthermore, a powerful tool, complex network theory, is applied to discuss the topology characteristics of the three-layer networks. Moreover, considering the path diversity of city-pair airlines, a simulated annealing-based framework is proposed to optimize the routing paths on an air route network for each city-pair airline, such that the traffic congestion of the air route network can be alleviated, where an elaborated method for perturbing solutions, named Selection after Remove (SAR), is adopted. Experimental results show that, compared with the default routing paths, the shortest routing paths, and the random routing paths, the proposed routing-path optimization strategy can reduce the maximum traffic flows of the air route network by 2.4%, 4.6%, and 4.8%, respectively, which shows that the proposed optimization method performs well in alleviating the traffic congestion of the air route network.

Spectrum map fusion has emerged as an effective technique to enhance the accuracy of spectrum map construction. However, many existing fusion methods fail to address the strong correlation between spectrum data, resulting in sub-optimal performance. In this paper, we propose a new multi-unmanned aerial vehicle (UAV) spectrum map fusion method based on differential ridge regression. We first construct spectrum maps of UAVs by using differential features of spectrum data. Next, we present a spectrum map fusion model by leveraging the spatial distribution characteristic of spectrum data. To reduce the sensitivity of the fusion model to the strongly correlated data, a new map fusion regularization term is designed, which introduces $l_2$-norm to constrain the fusion regularization parameters and compress the ridge regression coefficient sizes. As a result, accurate spectrum maps can be constructed for the environments with highly correlated spectrum data. We then formulate a model-driven solution to the spectrum map fusion problem and derive its lower bound. By combining the propagation characteristics of the spectrum signal with the developed Lagrange duality, we can guarantee the convergence of map fusion processing while enhancing the convergence rate. Finally, we propose an accelerated maximally split alternating directions method of multipliers (AMS-ADMM) to reduce the computational complexity of spectrum map construction. Simulation results demonstrate that our proposed method can effectively eliminate external noise interference and outliers, and achieve an accuracy improvement of more than 27% compared to state-of-the-art fusion methods in spectrum map construction with low complexity.