Kaiquan Cai

Evolution of Chinese airport network

Abstract With the rapid development of the economy and the accelerated globalization process, the aviation industry plays a more and more critical role in today’s world, in both developed and developing countries. As the infrastructure of aviation industry, the airport network is one of the most important indicators of economic growth. In this paper, we investigate the evolution of the Chinese airport network (CAN) via complex network theory. It is found that although the topology of CAN has remained steady during the past few years, there are many dynamic switchings inside the network, which have changed the relative importance of airports and airlines. Moreover, we investigate the evolution of traffic flow (passengers and cargoes) on CAN. It is found that the traffic continues to grow in an exponential form and has evident seasonal fluctuations. We also found that cargo traffic and passenger traffic are positively related but the correlations are quite different for different kinds of cities.

Using computational intelligence for large scale air route networks design

Due to the rapid development of air transportation, Air Route Networks (ARNs) need to be carefully designed to improve both efficiency and safety of air traffic service. The Crossing Waypoints Location Problem (CWLP) plays a crucial role in the design of an ARN. This paper investigates this problem in the context of designing the national ARN of China. Instead of adopting the single-objective formulation established in previous research, we propose to formulate CWLP as a bi-objective optimization problem. An algorithm named Memetic Algorithm with Pull-Push operator (MAPP) is proposed to tackle it. MAPP employs the Pull-Push operator, which is specifically designed for CWLP, for local search and the Comprehensive Learning Particle Swarm Optimizer for global search. Empirical studies using real data of the current national ARN of China showed that MAPP outperformed an existing approach to CWLP as well as three well-known Multi-Objective Evolutionary Algorithms (MOEAs). Moreover, MAPP not only managed to reduce the cost of the current ARN, but also improved the airspace safety. Hence, it has been implemented as a module in the software that is currently used for ARN planning in China. The data used in our experimental studies have been made available online and can be used as a benchmark problem for research on both ARN design and evolutionary multi-objective optimization.

A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem

Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.

Optimization of the crossing Waypoints in air route network

The optimization of national air route network (ARN) has become an effective method to improve the safety and efficiency of air transportation. The Crossing Waypoints Optimization (CWO) problem is the crucial problem in the design of the ARN. This paper presents a multi-objective optimization model for the CWO problem to minimize both airlines cost and flight conflicts. Meanwhile, the corresponding algorithm based on Particle Swarm Optimization (PSO) is adopted to solve the optimization model. The application to redesign trunk route network of China shows the proposed methodology validity and effectiveness by comparison with the existing ARN.

Simultaneous Optimization of Airspace Congestion and Flight Delay in Air Traffic Network Flow Management

Air traffic flow management (ATFM) aims to facilitate the utilization of airspace and airport resources and is critical in air transportation systems. During the past decades, several challenging problems have arisen from this domain and attracted intensive studies. This paper addresses the problem of alleviating the airspace congestion and reducing the flight delays in ATFM simultaneously. We formulate this problem as a multi-objective air traffic network flow optimization (MATNFO) problem. In this MATNFO model, comprehensive ATFM actions, for instance, ground-holding, airborne-holding, rerouting, and speed control, are considered. Meanwhile, a systematic approach, namely route and time-slot assignment (RTA) algorithm, is developed to solve the MATNFO problem. The idea of divide-and-conquer is embedded in the algorithm by sequentially applying both route searching module and time refinement module. Furthermore, for the sake of efficiency, a pre-selection operator is proposed as one heuristic strategy to identify promising solutions and reduce the search space by defining a sector equilibrium metric. Experiments on real data of the Chinese airspace show that the RTA algorithm outperforms an existing competitor and three related multi-objective evolutionary algorithms. In addition, RTA is competent for high-quality real-time air traffic network flow assignment.

An evolutionary multi-objective approach for Network-wide Conflict-free Flight Trajectories Planning

Under the demand of strategic Air Traffic Flow Management (ATFM) and the concept of Trajectory Based Operations (TBO), studies on the Network-wide Conflict-free Flight Trajectories Planning (NCFTP) has been motivated with the purpose of allocating all the 4D trajectories to realize a conflict-free airspace. However, the introduction of accurate 4D trajectories increases the problem complexity, and the collaborative decision-making philosophy calls for economical and fair plans for airlines. In this paper, therefore, we formulate a multi-objective 4D trajectories planning problem to minimize flight cost and maximize fairness on the premise of a conflict-free trajectory planning. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to solve this large-scale constrained optimization problem. Moreover, a specific heuristic operator is hybridized in the algorithm to rapidly de-conflict 4D trajectories and accelerate the optimization process. Empirical studies using the real air traffic data in China show that the proposed evolutionary multi-objective approach is effective to solve the NCFTP problem. And the solutions achieved provide elaborate decision support under TBO environment for decision-makers.

Decentralized Multi-aircraft Conflict Resolution in the presence of uncertainty

Multi-aircraft Conflict Resolution (CR) in the presence of uncertainty is the core task for air traffic controller to maintain flight safety. In order to avoid the conflicts among all involved aircraft while address the flight flexibility of individual aircraft, a decentralized multi-aircraft CR scheme is proposed. The CR problem is divided into a sequential of sub-problems based on a novel conflict resolution order, in which the flexibility of aircraft is described as the velocity reach set modelled through the concept of Velocity Obstacle (VO). Then, the sub-problem is formulated into a Mixed Integer Linear Program (MILP) solved via CPLEX. Some numerical results are given to show the efficacy of the proposed decentralized approach.

Terminal-area aircraft intent inference approach based on online trajectory clustering

Terminal-area aircraft intent inference (T-AII) is a prerequisite to detect and avoid potential aircraft conflict in the terminal airspace. T-AII challenges the state-of-the-art AII approaches due to the uncertainties of air traffic situation, in particular due to the undefined flight routes and frequent maneuvers. In this paper, a novel T-AII approach is introduced to address the limitations by solving the problem with two steps that are intent modeling and intent inference. In the modeling step, an online trajectory clustering procedure is designed for recognizing the real-time available routes in replacing of the missed plan routes. In the inference step, we then present a probabilistic T-AII approach based on the multiple flight attributes to improve the inference performance in maneuvering scenarios. The proposed approach is validated with real radar trajectory and flight attributes data of 34 days collected from Chengdu terminal area in China. Preliminary results show the efficacy of the presented approach.

Repeated thinking promotes cooperation in spatial prisoner's dilemma game

Inspired by the realistic process of taking decisions in social life, we have proposed a repeated thinking mechanism in the evolutionary spatial prisoners dilemma game where players are denoted by the vertices and play games with their direct neighbors. Under our mechanism, a player i will randomly select a neighbor j and then deliberate for M times before strategy updating. It will remain unchanged if not all M considerations suggest it to learn the strategy of j. We mainly focus on the evolution of cooperation in the systems. Interestingly, we find that the cooperation level fC is remarkably promoted and fC has a monotonic dependence on the caution parameter M, indicating that being cautious facilitates the emergence and persistence of cooperation. We give a simple but clear explanation for this cooperation promotion via detecting the cooperator–defector transition process. Moreover, the robustness of this mechanism is also examined on different noise levels and game models.

Large scale aircraft conflict resolution based on location network

This paper proposes the method for solving large scale flight conflicts among multi-aircraft during the pre-tactical phase. Under the concept of the Trajectory-Based Operations (TBO), each aircraft is assigned a 4D trajectory to track. Therefore, with the position information obtained from the predicted 4D trajectory, potential conflicts are required to be resolved during the pre-tactical phase instead of the current tactical process. This long-term flight deconfliction induces a scale enlargement of flight conflicts, which sets a higher demand on the resolution method efficiency. Therefore, to reduce the conflict scale and enhance the deconfliction efficiency, we select critical aircraft with high potential conflict risks and only these key aircraft perform the deconfliction maneuver. The key aircraft are selected from the global perspective based on the location network which can characterize the structural characteristics of air traffic scenario. In this paper, the velocity adjustment is considered as the only admissible maneuver and the Aircraft Conflict Resolution (ACR) problem is formulated as a Mixed Integer Linear Program (MILP) problem to minimize the total flight time thus resolve all conflicts efficiently. Finally, simulation results illustrate the advantage of our approach in operational efficiency improvement and problem complexity reduction compared with the traditional method.