In air traffic and airport management,experience gained from past operations is crucial in designing appropriate strategies when facing a new scenario.Therefore,this paper uses massive spatiotemporal flight data to id...In air traffic and airport management,experience gained from past operations is crucial in designing appropriate strategies when facing a new scenario.Therefore,this paper uses massive spatiotemporal flight data to identify similar traffic and delay patterns,which become critical for gaining a better understanding of the aviation system and relevant decision-making.However,as the datasets imply complex dependence and higher-order interactions between space and time,retrieving significant features and patterns can be very challenging.In this paper,we propose a probabilistic framework for highdimensional historical flight data.We apply a latent class model and demonstrate the effectiveness of this framework using air traffic data from 224 airports in China during 2014–2017.We find that profiles of each dimension can be clearly divided into various patterns representing different regular operations.To prove the effectiveness of these patterns,we then create an estimation model that provides preliminary judgment on the airport delay level.The outcomes of this study can help airport operators and air traffic managers better understand air traffic and delay patterns according to the experience gained from historical scenarios.展开更多
Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning,flight scheduling,airport operation,and passenger service.Flight delay is affected by nu...Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning,flight scheduling,airport operation,and passenger service.Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity,which brings critical challenges to accurate flight delay prediction.In recent years,Graph Convolutional Networks(GCNs)have become popular in flight delay prediction due to the advantage in extracting complicated relationships.However,most of the existing GCN-based methods have failed to effectively capture the spatial-temporal information in flight delay prediction.In this paper,a Geographical and Operational Graph Convolutional Network(GOGCN)is proposed for multi-airport flight delay prediction.The GOGCN is a GCN-based spatial-temporal model that improves node feature representation ability with geographical and operational spatial-temporal interactions in a graph.Specifically,an operational aggregator is designed to extract global operational information based on the graph structure,while a geographical aggregator is developed to capture the similar nature among spatially close airports.Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.展开更多
Purpose-Flights are often delayed owing to emergencies.This paper proposes a cooperative slot secondary assignment(CSSA)model based on a collaborative decision-making(CDM)mechanism,and the operation mode of flight wav...Purpose-Flights are often delayed owing to emergencies.This paper proposes a cooperative slot secondary assignment(CSSA)model based on a collaborative decision-making(CDM)mechanism,and the operation mode of flight waves designs an improved intelligent algorithm to solve the optimal flight plan and minimize the total delay of passenger time.Design/methodology/approach-Taking passenger delays,transfer delays and flight cancellation delays into account comprehensively,the total delay time is minimized as the objective function.The model is verified by a linear solver and compared with the first come first service(FCFS)method to prove the effectiveness of the method.An improved adaptive partheno-genetic algorithm(IAPGA)using hierarchical serial number coding was designed,combining elite and roulette strategies to find pareto solutions.Findings-Comparing and analyzing the experimental results of various scale examples,the optimization model in this paper is greatly optimized compared to the FCFS method in terms of total delay time,and the IAPGA algorithm is better than the algorithm before in terms of solution performance and solution set quality.Originality/value-Based on the actual situation,this paper considers the operation mode of flight waves.In addition,the flight plan solved by the model can be guaranteed in terms of feasibility and effectiveness,which can provide airlines with reasonable decision-making opinions when reassigning slot resources.展开更多
Reinforcement Learning(RL)techniques are being studied to solve the Demand and Capacity Balancing(DCB)problems to fully exploit their computational performance.A locally gen-eralised Multi-Agent Reinforcement Learning...Reinforcement Learning(RL)techniques are being studied to solve the Demand and Capacity Balancing(DCB)problems to fully exploit their computational performance.A locally gen-eralised Multi-Agent Reinforcement Learning(MARL)for real-world DCB problems is proposed.The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management(ATFM)region to quickly obtain a satisfactory solution.In this method,agents of all flights in a scenario form a multi-agent decision-making system based on partial observation.The trained agent with the customised neural network can be deployed directly on the corresponding flight,allowing it to solve the DCB problem jointly.A cooperation coefficient is introduced in the reward function,which is used to adjust the agent’s cooperation preference in a multi-agent system,thereby controlling the distribution of flight delay time allocation.A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated.Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method.From a statis-tical point of view,it is proven that the proposed method is generalised within the scope of the flights and sectors of interest,and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods.The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.展开更多
基金This paper is supported by the National Key Research and Development Program of China(2019YFF0301400)the National Natural Science Foundation of China(61671031,61722102,and 61961146005).
文摘In air traffic and airport management,experience gained from past operations is crucial in designing appropriate strategies when facing a new scenario.Therefore,this paper uses massive spatiotemporal flight data to identify similar traffic and delay patterns,which become critical for gaining a better understanding of the aviation system and relevant decision-making.However,as the datasets imply complex dependence and higher-order interactions between space and time,retrieving significant features and patterns can be very challenging.In this paper,we propose a probabilistic framework for highdimensional historical flight data.We apply a latent class model and demonstrate the effectiveness of this framework using air traffic data from 224 airports in China during 2014–2017.We find that profiles of each dimension can be clearly divided into various patterns representing different regular operations.To prove the effectiveness of these patterns,we then create an estimation model that provides preliminary judgment on the airport delay level.The outcomes of this study can help airport operators and air traffic managers better understand air traffic and delay patterns according to the experience gained from historical scenarios.
基金supported by the National Natural Science Foundation of China(Nos.71731001,U2133210,and U2033215,61822102)。
文摘Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning,flight scheduling,airport operation,and passenger service.Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity,which brings critical challenges to accurate flight delay prediction.In recent years,Graph Convolutional Networks(GCNs)have become popular in flight delay prediction due to the advantage in extracting complicated relationships.However,most of the existing GCN-based methods have failed to effectively capture the spatial-temporal information in flight delay prediction.In this paper,a Geographical and Operational Graph Convolutional Network(GOGCN)is proposed for multi-airport flight delay prediction.The GOGCN is a GCN-based spatial-temporal model that improves node feature representation ability with geographical and operational spatial-temporal interactions in a graph.Specifically,an operational aggregator is designed to extract global operational information based on the graph structure,while a geographical aggregator is developed to capture the similar nature among spatially close airports.Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.
基金The presented research work was supported by the National Social Science Foundation of China(Grant no.18BGL003)。
文摘Purpose-Flights are often delayed owing to emergencies.This paper proposes a cooperative slot secondary assignment(CSSA)model based on a collaborative decision-making(CDM)mechanism,and the operation mode of flight waves designs an improved intelligent algorithm to solve the optimal flight plan and minimize the total delay of passenger time.Design/methodology/approach-Taking passenger delays,transfer delays and flight cancellation delays into account comprehensively,the total delay time is minimized as the objective function.The model is verified by a linear solver and compared with the first come first service(FCFS)method to prove the effectiveness of the method.An improved adaptive partheno-genetic algorithm(IAPGA)using hierarchical serial number coding was designed,combining elite and roulette strategies to find pareto solutions.Findings-Comparing and analyzing the experimental results of various scale examples,the optimization model in this paper is greatly optimized compared to the FCFS method in terms of total delay time,and the IAPGA algorithm is better than the algorithm before in terms of solution performance and solution set quality.Originality/value-Based on the actual situation,this paper considers the operation mode of flight waves.In addition,the flight plan solved by the model can be guaranteed in terms of feasibility and effectiveness,which can provide airlines with reasonable decision-making opinions when reassigning slot resources.
基金co-funded by the National Natural Science Foundation of China(No.61903187)the National Key R&D Program of China(No.2021YFB1600500)+2 种基金the China Scholarship Council(No.202006830095)the Natural Science Foundation of Jiangsu Province(No.BK20190414)the Jiangsu Province Postgraduate Innovation Fund(No.KYCX20_0213).
文摘Reinforcement Learning(RL)techniques are being studied to solve the Demand and Capacity Balancing(DCB)problems to fully exploit their computational performance.A locally gen-eralised Multi-Agent Reinforcement Learning(MARL)for real-world DCB problems is proposed.The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management(ATFM)region to quickly obtain a satisfactory solution.In this method,agents of all flights in a scenario form a multi-agent decision-making system based on partial observation.The trained agent with the customised neural network can be deployed directly on the corresponding flight,allowing it to solve the DCB problem jointly.A cooperation coefficient is introduced in the reward function,which is used to adjust the agent’s cooperation preference in a multi-agent system,thereby controlling the distribution of flight delay time allocation.A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated.Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method.From a statis-tical point of view,it is proven that the proposed method is generalised within the scope of the flights and sectors of interest,and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods.The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.
