In this study, we introduce the R package shp2graph, which provides tools to convert a spatial network into an ‘igraph’ graph of the igraph R package. This conversion greatly empowers a spatial network study, as the vast array of graph analytical tools provided in igraph are then readily available to the network analysis, together with the inherent advantages of being within the R statistical computing environment and its vast array of statistical functions. Through three urban road network case studies, the calculation of road network distances with shp2graph and with igraph is demonstrated through four key stages: (i) confirming the connectivity of a spatial network; (ii) integrating points/locations with a network; (iii) converting a network into a graph; and (iv) calculating network distances (and travel times). Throughout, the required R commands are given to provide a useful tutorial on the use of shp2graph.

Safety is a primary concern for the civil aviation industry. Airlines record high-frequency but potentially low-severity unsafe events, i.e., incidents, in their reports. Over the past few decades, civil aviation security practitioners have made efforts to analyze these issues. The information in incident reports is valuable for risk analysis. However, incident reports were inefficiently utilized due to incoherence, large volume, and poor structure. In this study, we proposed a technical scheme to intelligently classify and extract risk factors from Chinese civil aviation incident reports. Firstly, we adopted machine learning classifiers and vectorization strategies to classify incident reports into 11 categories. Grid search was used to adjust the parameters of the classifier. In the preliminary experiment, the combination of the extreme gradient boosting (XGBoost) classifier and the occurrence position (OC-POS) vectorization strategy outperformed with an 0.85 weighted F1-score. In addition, we designed a rule-based system to identify the factors related to the occurrence of incidents from 25 empirical causes, which included equipment, human, environment, and organizational causes. For cause identification, we used rules obtained through manual analysis with keywords and discourse. F1-score above 0.90 was obtained on the test set using the causes identification model derived from the training set. The proposed system permits insights into unsafe factors in aviation incidents and prevents reoccurrence. Future works can proceed on this study, such as exploring the causal relationship between causes and incidents.

In the civil aviation industry, security risk management has shifted from post-accident investigations and analyses to pre-accident warnings in an attempt to reduce flight risks by identifying currently untracked flight events and their trends and effectively preventing risks before they occur. The use of flight monitoring data for flight anomaly detection is effective in discovering unknown and potential flight incidents. In this paper, we propose a time-feature attention mechanism and construct a deep hybrid model for flight anomaly detection. The hybrid model combines a time-feature attention-based convolutional autoencoder with the HDBSCAN clustering algorithm, where the autoencoder is constructed and trained to extract flight features while the HDBSCAN works as an anomaly detector. Quick access record (QAR) flight data containing information of aircraft landing at Kunming Changshui International and Chengdu Shuangliu International airports are used as the experimental data, and the results show that (1) the time-feature-based convolutional autoencoder proposed in this paper can better extract the flight features and further discover the different landing patterns; (2) in the representation space of the flights, anomalous flight objects are better separated from normal objects to provide a quality database for subsequent anomaly detection; and (3) the discovered flight patterns are consistent with those at the airports, resulting in anomalies that could be interpreted with the corresponding pattern. Moreover, several examples of anomalous flights at each airport are presented to analyze the characteristics of anomalies.

With the rapid development of the aviation industry, it is particularly important to ensure the safe flight of aircraft. How to find potential hazards in the process of aircraft flight has always been one of the important topics of civil aviation research. At present, the Quick Access Recorder (QAR) is the most widely used equipment to store the data recorded on aircraft. QAR data contain a lot of valuable and unexplored information, which records the true status of the aircraft in detail. Therefore, finding abnormal data from QAR data lays an important foundation for obtaining the cause of abnormality and providing a guarantee for flight. In this paper, in order to discover the abnormal information in the QAR data, we applied a VAE-LSTM model with a multihead self-attention mechanism. Compared to the VAE and LSTM models alone, our model performs much better in anomaly detection and prediction, detecting all types of anomalies. We conducted extensive experiments on real-world QAR data sets to prove the efficiency and accuracy of our proposed neural network model. The experimental results proved that our proposed model can outperform state-of-the-art models under different experimental settings.

Low-altitude unstable approach (UA) is one of the crucial risks that threaten flight safety. In this study, we proposed a technical program for detecting low-altitude UA events. The detection logic was to optimize the step-wise regression model with iterative surveys with more than 20 experienced pilots. Accordingly, the frequencies of UA events occurring around each airport in January 2018 were calculated for all the airports within mainland China. Finally, the spatial distribution characteristics of UA events were analyzed via exploratory spatial data analysis. In addition, Pearson’s correlation coefficient and the geographically weighted correlation coefficient were used to explore the correlations between UA frequency and the altitude elevation, wind level, and bad weather. The experimental results revealed that the proposed method can accurately detect the occurrence of low-altitude UA and quantitatively characterize risks. It was found that UA exhibits obvious differences in spatial distribution. Moreover, significantly strong correlations were found between UA and altitude elevation, wind level, and bad weather, and correlation differences were also reflected in different regions in China.

Keywords: Extended Kalman Filter(EKF), Unscented Kalman Filter(UKF), Particle Filter(PF), Integrated Positioning In order to realize fast and accurate BDS/GPS integrated positioning, it is necessary to overcome the adverse effects of signal attenuation, multipath effect and echo interference to ensure the result of continuous and accurate navigation and positioning. In this paper, pseudo-range positioning is used as the mathematical model. In the stage of data preprocessing, using precise and smooth carrier phase measurement value to promote the rough pseudo-range measurement value without ambiguity. At last, the Extended Kalman Filter(EKF), the Unscented Kalman Filter(UKF) and the Particle Filter(PF) algorithm are applied in the integrated positioning method for higher positioning accuracy. The experimental results show that the positioning accuracy of PF is the highest, and UKF is better than EKF.