The terminal airspace that surrounds an airport is the area with high flight density and complex structure. Aircraft are asked to follow the standard arrival and departure routes in terminal airspace, yet the actual trajectories may deviate due to air traffic control (ATC) instructions, pilots' decisions, surveillance and flying performance variations, etc. Predicting aircraft trajectories considering such uncertainties plays a crucial role in evaluating a redesign of the standard routes. Traditional simulation approaches for generating aircraft trajectories in a terminal airspace are cumbersome to use as it requires a detailed setup for each new scenario, while most existing data-driven methods can only be used in an airspace with historical trajectories, not applicable to new structure designs or other terminal areas. To fill in gap, in this paper, we develop a new model based on Multilayer Perceptron Neural Network (MLPNN) to predict aircraft trajectories with uncertainties for terminal airspace design evaluations. A key feature of the proposed model is that it is trained on existing standard routes yet it can be applied to new standard routes to generate trajectories. The enabler of the model's transferability is a novel input-and-output construction method for feature representations of raw trajectory data based on domain knowledge, including trajectory reconstruction, feature engineering, and output designing. After the input-and-output construction, a supervised learning model based on MLPNN is built to predict the standard deviations from the extracted features using historical trajectory data of existing standard routes. Once the model is built, trajectories with uncertainty can be simulated, through applying Gaussian distribution and exponential moving average algorithms, even on newly designed standard routes, where no aircraft have flown yet. Subsequently, new terminal airspace designs could be evaluated for their safety, efficiency, and environmental implications based on the simulated trajectories. The proposed model was tested on real-world operational data. Results showed that the model can quantify the characteristics of aircraft trajectories that are transferable across standard routes, and generate trajectories for new standard routes. We also demonstrated the proposed model on evaluating deficiencies on fuel consumption of actual arrival trajectories compared with the designed arrival routes. The generated trajectories showed 23%–37% more fuel consumption on average than the standard arrival routes in the terminal airspace of Hong Kong International Airport, which was validated with actual flight data.

High-tech giants and start-ups are investing in drone technologies to provide urban air delivery service, which is expected to solve the last-mile problem and mitigate road traffic congestion. However, air delivery service will not scale up without proper traffic management for drones in dense urban environment. Currently, a range of Concepts of Operations (ConOps) for unmanned aircraft system traffic management (UTM) are being proposed and evaluated by researchers, operators, and regulators. Among these, the tube-based (or corridor-based) ConOps has emerged in operations in some regions of the world for drone deliveries and is expected to continue serving certain scenarios that with dense and complex airspace and requires centralized control in the future. Towards the tube-based ConOps, we develop a route network planning method to design routes (tubes) in a complex urban environment in this paper. In this method, we propose a priority structure to decouple the network planning problem, which is NP-hard, into single-path planning problems. We also introduce a novel space cost function to enable the design of dense and aligned routes in a network. The proposed method is tested on various scenarios and compared with other state-of-the-art methods. Results show that our method can generate near-optimal route networks with significant computational time-savings.

In China, while large-scale industrial land redevelopment has played a significant role in promoting economic growth, it has also triggered a series of unsustainable problems. To date, few studies have explored the expectations of stakeholders in industrial redevelopment projects in China. Gaining an in-depth understanding of the diverse expectations among core stakeholders is an essential step towards realizing social sustainability. This study aims to analyze these diverse expectations in industrial land redevelopment projects. For this purpose, 19 factors were identified and compared across the following core stakeholders: local governments, consulting experts, the general public, and original land users of state-led redevelopment and land user-led redevelopment projects, using questionnaires and interviews conducted in Shanghai, China. The findings show there to be tremendous differences between the expectations of different stakeholder groups in terms of economic, social and environmental aspects. Major differences were also found between the expectations of original land users across different project types. Moreover, the negative externalities, the balance between industrial and residential space, the diverse needs of original land users in different project types, and the barriers to stakeholders' participation, in industrial land redevelopment in China were discussed. The findings of the current paper are conducive to optimizing stakeholder participation in industrial land redevelopment so as to enhance social sustainability.

Safety is a top priority for civil aviation. To help airlines further improve safety, various clustering-based methods were developed to better understand their current flight operations and detect unknown risks from onboard flight data. However, existing methods can only be carried on historical data in batches, resulting in its inability to update and adjust as new data come in. New onboard flight data related to anomaly detection are generated at airlines every day. The addition of new data will inevitably cause changes in the clustering results. Yet it would be computational costly to run clustering on all data as they accumulate. Therefore, anomaly detection methods that allow real-time update of cluster models as new data come in are more practical for airlines. This paper presents a reinforcement learning method to identify common patterns in flight data via cluster analysis and update its clusters as new data come in. This method is based on Gaussian Mixture Model (GMM) and uses online (recursive) expectation-maximization (EM) algorithm to update clustering results over time. An initial result of clusters can be obtained by performing GMM-based clustering on historical flight data. Then, as new data come in, the parameters of GMM are updated via an online EM algorithm. By recording the GMM parameters, the method can also track changes in clusters over time. We demonstrated the proposed method using Flight Data Recorder (FDR) data from real operations of an airline. The evolution of clusters was observed as new batches of flight data are fed into the proposed method.

Aircraft mass is the most important parameter airliners use to determine how much cargo and fuel they should take for the fight to maximize their proft while keep the total weight under the safety limit. For many practical reasons, airliners can not get the accurate aircraft mass data by weighting every cargo and passenger for each fight. Several studies have proposed methods to estimate aircraft mass based on radar data or ADS-B data. But due to the measurement errors in the data, and also due to uncertainties in aircraft confguration, it is hard to calculate accurate aircraft mass data for every fight. In the paper, the quick access recorder data are used for analysis, parameters that are not available in radar data or ADS-B data can now be used to eliminate parameters with large errors in the level fight point-mass dynamics model. Equations are reformulated as a set of overdetermined linear equations with nonlinearly structured errors in system matrix. The set of equations does not depend on inaccurate parameters like thrust and it does not require accurate knowledge of the aircraft like geometry, aerodynamic coefcients, etc. The set of equations is solved by an improved structured nonlinear total least squares method using Monte Carlo method. The method is applied to 120 real fights of Boeing 777300ER aircraft, the result shows a good accuracy for some type of fights.