Flight Trajectory Prediction (FTP) is an essential task in Air Traffic Control (ATC), which can assist air traffic controllers in managing airspace more safely and efficiently. Existing approaches generally perform multi-horizon FTP tasks in an autoregressive manner, thereby suffering from error accumulation and low-efficiency problems. In this paper, a novel framework, called FlightBERT++, is proposed to i) forecast multi-horizon flight trajectories directly in a non-autoregressive way, and ii) improve the limitation of the binary encoding (BE) representation in the FlightBERT. Specifically, the FlightBERT++ is implemented by a generalized encoder-decoder architecture, in which the encoder learns the temporal-spatial patterns from historical observations and the decoder predicts the flight status for the future horizons. Compared with conventional architecture, an innovative horizon-aware contexts generator is dedicatedly designed to consider the prior horizon information, which further enables non-autoregressive multi-horizon prediction. Moreover, a differential prompted decoder is proposed to enhance the capability of the differential predictions by leveraging the stationarity of the differential sequence. The experimental results on a real-world dataset demonstrated that the FlightBERT++ outperformed the competitive baselines in both FTP performance and computational efficiency.

Airport traffic flow prediction is a fundamental research topic in the field of air traffic flow management. Most existing works focus on the single airport traffic flow prediction with temporal dynamics but fail to consider the influence of the topological airport network. In this paper, a novel deep learning-based framework, called airport traffic flow prediction network (ATFPNet), is proposed to capture spatial-temporal dependencies of the historical airport traffic flow (departure and arrival) for the multiple-step situational (network-level) arrival flow prediction. Firstly, considering the nature of the airport distribution and the context of air transportation, a special semantic graph built on the flight schedule is applied to represent the airport network, which is the key to encoding the situational airport traffic flow into a single representation. Then, the graph convolution operator and the gated recurrent unit are combined to extract high-level transition patterns of airport traffic flow in the spatial and temporal dimensions. Finally, a real-world airport traffic flow dataset is applied to validate the effectiveness of the proposed model, and the experimental results demonstrate that the ATFPNet outperforms other baselines on different prediction horizons. Specifically, the proposed method achieves up to 17% MAE improvement compared to baselines. Based on the proposed approach, efficient traffic planning is expected to be achieved for airport management.

Flight Trajectory Prediction (FTP) is an essential task in Air Traffic Control (ATC), which can assist air traffic controllers in managing airspace more safely and efficiently. Existing methods generally perform multi-horizon FTP tasks in an autoregressive manner, thereby suffering from error accumulation and low-efficiency problems. In this paper, a novel framework, called FlightBERT++, is proposed to i) forecast multi-horizon flight trajectories directly in a non-autoregressive way, and ii) improve the limitation of the binary encoding (BE) representation in the FlightBERT framework. Specifically, the proposed framework is implemented by a generalized encoder-decoder architecture, in which the encoder learns the temporal-spatial patterns from historical observations and the decoder predicts the flight status for the future horizons. Compared to conventional architecture, an innovative horizon-aware context generator is dedicatedly designed to consider the prior horizon information, which further enables non-autoregressive multi-horizon prediction. Additionally, the Gray code representation and the differential prediction paradigm are designed to cope with the high-bit misclassifications of the BE representation, which significantly reduces the outliers in the predictions. Moreover, a differential prompted decoder is proposed to enhance the capability of the differential predictions by leveraging the stationarity of the differential sequence. Extensive experiments are conducted to validate the proposed framework on a real-world flight trajectory dataset. The experimental results demonstrated that the proposed framework outperformed the competitive baselines in both FTP performance and computational efficiency. The code is publicly available at: https://github.com/gdy-scu/FlightBERT_PP_V2

Object tracing, especially continuous object tracing, is an important application in wireless sensor networks (WSNs). Due to the loose infrastructure, unfixed topology and limited resource, it is a big challenge to trace objects in WSN by sensors' cooperation. In this paper, we propose an energy-efficient scheme to detect the continuous objects based on ring-architecture. Different from previous works, our solution optimizes report range by computing the location of the phenomena in each boundary face but not the boundary nodes, which reduces the energy consumption efficiently. Simulation results show that total average report message size as well as the number of nodes transmitted the report messages to the base station are greatly reduced.

In air traffic systems, aircraft trajectories between airports are monitored by the radar networking system forming dynamic air traffic flow. Accurate airport arrival flow prediction is significant in implementing large-scale intelligent air traffic flow management. Despite years of studies to improve prediction precision, most existing methods only focus on a single airport or simplify the traffic network as a static and simple graph. To mitigate this shortage, we propose a hybrid neural network method, called Dynamic Multi-graph Convolutional Spatial-Temporal Network (DMCSTN), to predict network-level airport arrival flow considering the multiple operation constraints and flight interactions among airport nodes. Specifically, in the spatial dimension, a novel dynamic multi-graph convolutional network is designed to adaptively model the heterogeneous and dynamic airport networks. It enables the proposed model to dynamically capture informative spatial correlations according to the input traffic features. In the temporal dimension, an enhanced self-attention mechanism is utilized to mine the arrival flow evolution patterns. Experiments on a real-world dataset from an ATFM system validate the effectiveness of DMCSTN for arrival flow forecasting tasks.

Research on airport delay forecasting is essential for quickly identifying key bottleneck points within the airport network, which enables controllers to promptly assess traffic conditions and take appropriate actions. However, there are two major challenges that should be sincerely considered: (1) modeling the cascading effects of airport delay is often incomplete in existing prediction methods; (2) there is an underlying relationship between traffic flow and delays, but it is difficult to measure how flow changes impact airport delays. To address these problems, we propose a dual‐path deep learning framework called the dynamic spatial‐temporal propagation neural network (DSTPNN) to forecast airport delays. Specifically, the spatial‐temporal convolutional embedding (STCE) module is designed to capture delay propagation patterns by considering dynamic and static spatial dependencies, together with multi‐scale temporal features. To model the evolutionary correlations between traffic flow and delays, the auxiliary temporal embedding (ATE) module is first constructed based on causal and dilated convolution to learn high‐level feature representations of traffic flow. Based on the output of both components, we design a traffic situation awareness attention (TSAA) mechanism that incorporates both convolution and cross‐attention techniques to mine the potential causal relationships between traffic features (flow and delays) at the airport level. Experiments on a real‐world dataset from the Bureau of Transportation Statistics (BTS) indicate that the proposed DSTPNN outperforms the baselines, obtaining relative improvements of at least 1.5% in MAE, 2.3% in RMSE, and 9.7% in . Furthermore, ablation and analysis experiments demonstrate that each proposed technical block is instrumental in achieving the desired performance enhancements.