A5101916207- Air Traffic Management and Optimization
- Aviation Industry Analysis and Trends
- Transportation Planning and Optimization
- Traffic control and management
- Traffic and Road Safety
- Vehicle Routing Optimization Methods
- Aerospace and Aviation Technology
- Human-Automation Interaction and Safety
- Autonomous Vehicle Technology and Safety
- Traffic Prediction and Management Techniques
- Opinion Dynamics and Social Influence
- Complex Network Analysis Techniques
- Scheduling and Timetabling Solutions
- Advanced Aircraft Design and Technologies
- Evacuation and Crowd Dynamics
- Simulation and Modeling Applications
- Crystallization and Solubility Studies
- Safety and Risk Management
- X-ray Diffraction in Crystallography
- Advanced Decision-Making Techniques
- Catalytic Processes in Materials Science
- Military Defense Systems Analysis
- Robotic Path Planning Algorithms
- Advanced Manufacturing and Logistics Optimization
- Welding Techniques and Residual Stresses
Nanjing University of Aeronautics and Astronautics
2016-2025
Traffic Management Research Institute
2015-2024
Software Engineering Institute of Guangzhou
2022-2023
Jianghan University
2013-2022
Shanghai Academy of Spaceflight Technology
2021
Chinese Academy of Sciences
2019-2020
Technical Institute of Physics and Chemistry
2019-2020
University of Chinese Academy of Sciences
2019-2020
Shanghai Jiao Tong University
2015-2016
Southwest Jiaotong University
2013
We propose an on-demand airport slot management (ODASM) approach to guide fire-break capacity setup and allocation. The ODASM consists of a tree-structured profile coadapted setting allocation model. that is constructed using decision tree aims capture various settings their corresponding delays. It provides diversified schemes delay references in the process. model are able generate good coadaptation. That is, fire-breaks set adapt preferences airlines' requests minimise total...
The interaction between supply and demand of civil aviation passenger transport serves as an important reference for airport planning, transportation structure optimization dynamic matching demand. Based on the panel data 31 province-level administrative divisions (excluding Hong Kong, Macao, Taiwan) in China spanning from 2004 to 2019, this study employs entropy-weighted TOPSIS method evaluate level transport. On foundation, uses modified coupling coordination degree model measure demand,...
Abstract Air transport network, or airport is a complex network involving numerous airports. Effective management of the air system requires an in-depth understanding roles airports in network. Whereas knowledge on properties has been improved greatly, methods to find critical are still lacking. In this paper, we present investigate and identify A novel model proposed with as nodes correlations between traffic flow edges. Spectral clustering algorithm developed classify Spatial distribution...
Reinforcement Learning (RL) techniques are being studied to solve the Demand and Capacity Balancing (DCB) problems fully exploit their computational performance. A locally generalised Multi-Agent (MARL) for real-world DCB is proposed. The proposed method can deploy trained agents directly unseen scenarios in a specific Air Traffic Flow Management (ATFM) region quickly obtain satisfactory solution. In this method, of all flights scenario form multi-agent decision-making system based on...
Abstract Departure delays are a major cause of economic loss and inefficiency in the growing industry passenger flights. A departure delay current flight is inevitably affected by late arrival immediately preceding it with same aircraft. We seek to understand mechanisms such propagated delays, obtain universal metrics which evaluate an airline’s operational effectiveness alleviation. Here we use big data collected American Bureau Transportation Statistics design models delays. Offering two...
Reinforcement learning (RL) techniques have been studied for solving the conflict resolution (CR) problem in air traffic management, leveraging their potential computation and ability to handle uncertainty. However, challenges remain that impede application of RL methods CR practice, including three-dimensional manoeuvres, generalisation, trajectory recovery, success rate. This paper proposes a general multi-agent reinforcement approach real-time multi-aircraft resolution, which agents share...
Reinforcement learning (RL) techniques have been studied for solving the demand and capacity balancing (DCB) problem in air traffic management to exploit their full computational potential. Due lack of generalisation seemingly reduced optimisation performance affected by training scenarios, it is challenging existing RL-based DCB methods be effectively applied practice. This paper proposes a general multi-agent reinforcement (MARL) method that integrates heuristic-based delay priority...
We propose an agent-based model for predicting individual flight delays in entire air traffic network. In contrast to previous work, more detailed parameter estimation methods were incorporated into the model, acting on state transitions of agents. Specifically, a conditional probability was proposed modifying expected departure time, which used indicate whether had experienced necessary waiting due Ground Delay Programs (GDPs) or carrier-related reasons. Additionally, two random forest...
A specific 2D porous nanostructure and multi-binding ligands endow SNPG with outstanding selectivity ability to separate Hg(<sc>ii</sc>) over Pb(<sc>ii</sc>) Cu(<sc>ii</sc>).