Departure Sequencing Research Articles

Arrival and Departure Sequencing, Considering Runway Assignment Preferences and Crossings

Aircraft sequencing has the potential to decrease flight delays and improve operational efficiency at airports. This paper presents the aircraft sequencing problem (ASP) on multiple runways with complex interactions by allocating flights on runways and optimizing landing times, take-off times, and crossing times simultaneously in a uniform framework. The problem was formulated as a mixed-integer program considering realistic operational constraints, including runway assignment preferences based on the entry/exit fixes of the terminal maneuvering area (TMA), minimum runway separation, time window, and arrival crossing rules. Variable-fixing strategies were applied, to strengthen the formulation. A first-come-first-served (FCFS) heuristic was proposed for comparison. Various instances from the literature and from realistic data sets were tested. Our computational study showed that the solution approach optimizes runway schedules, to achieve significantly fewer flight delays, taking runway assignment preferences and arrival crossings into account.

Integrated optimization of scheduling for unmanned follow-me cars on airport surface

To promote the application of automated vehicles in large airports, in this study, we present an integrated optimization method for scheduling Unmanned follow-me cars. The scheduling process is divided into three phases: Dispatch, Guidance, and Recycle. For the Dispatch phase, we establish a vehicle assignment model, to allocate the vehicle resource equitably. For the Guidance phase, we offer an quantitative way, to measure the spacing between Unmanned follow-me car and aircraft. To optimize the efficiency of airport operation in the three phases and ensure safety, the collaborative planning model, and the conflict prediction model are established. An improved grey wolf optimization algorithm is adopted to enhance the convergence speed and generalization performance. A case study at Ezhou Huahu Airport in China demonstrates the effectiveness of the methods. The results show that the model of collaborative planning can make the balance of path selection, Unmanned follow-me car’s working time, and departure sequence. The convergence speed of the improved algorithm has been increased by 18.75%. The inequity index of vehicle assignment is only 0.015731, and the spatiotemporal distribution of conflicts is influenced by the airport’s surface layout.

Joint optimisation of vehicle trajectory and signal control at intersections mixed with connected automated vehicles: a departure sequence estimation-based approach

With advanced connected automated vehicles (CAVs) technologies, joint optimisation of trajectories and signal control can significantly improve traffic mobility and safety. This study presents a joint optimisation method of trajectory and signal control at intersections mixed with human-driven vehicles (HVs) and CAVs based on vehicle departure sequence estimation. First, a signal control optimisation method based on the trajectories detected by CAVs was proposed. By estimating the virtual arrival time of HVs and CAVs, the optimal departure sequence with minimal delay was determined. Then, a joint optimisation of trajectories and signal control was developed depending on the position of CAVs in the mixed vehicles platoon, i.e. leader and follower. Simulation experiments under different scenarios were conducted to analyse the performance of the proposed method. Comparison results show that the method decreases the average delay by 19.66% when the CAV’s penetration rate ranges from 10% to 20%.

An Optimized Air Traffic Departure Sequence According to the Standard Instrument Departures

An Optimized Air Traffic Departure Sequence According to the Standard Instrument Departures

An efficient evolutionary algorithm for high-speed train rescheduling under a partial station blockage

This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. Firstly, a HSTR model subject to train operation constraints is established to minimize the total train delay. A permutation-based encoding method is developed to define an efficient search space based on the train departure sequence. A heuristic decoding method is employed to eliminate all train operation constraints and output the rescheduled timetable. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of PS-SEGA. Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced evolutionary algorithms and their improved variants also using the improvements of PS-SEGA, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX cannot obtain optimal solutions within 10 min, PS-SEGA can provide quasi-optimal solutions in real time. Furthermore, compared with the other two heuristics algorithms (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can give the train departure sequence with a smaller total train delay.

Stability analysis for scheduled air traffic flow

PurposeModeling and system performance analysis play an important role in the planning of air traffic system. In particular, this paper aims to concentrate on the modeling and the performance evaluation of air transportation system.Design/methodology/approachIt has shown that system matrices inherent in the airspace network can be acquired based on the service times of flights. This paper deals with a logical modeling which can avoid temporal synchronization conflict of resources. As a result, a class of queuing system is investigated to obtain a representation of the airspace network dynamics using dioid model.FindingsThe analysis of the air traffic system is conducted by solving the system state model. This indicates that it is feasible to use eigenvalues of system model for acquiring characteristics of the considered air transport systems.Practical implicationsThe departure scheduling prototype presented in this paper can offer air traffic controller a decision support tool to build optimal departure sequences for aircraft.Originality/valueThe properties of max-plus algebra described allow us to apply linear algebra concepts like eigenvalue and eigenvectors to obtain a solution to the air traffic system being modeled. By solving and analyzing the dioid model, this paper evaluated some performance measures of airspace network.

How to Improve the Efficiency of Check-In Counter: A Counter-Sharing Method

Improving the efficiency of check-in counters is a significant concern for airport operation management departments and airline companies. However, the existing studies mainly optimize the counter allocation based on the number of counters and airport demands, which needs more exploration on improving the utilization rate of check-in counters under the condition of unchanged resource allocation. In this paper, we propose a counter-sharing method to improve check-in efficiency by sharing the idle check-in counters in the adjacent check-in area. In the proposed counter-sharing method, we first define the passenger’s total walking distance and waiting time for queues as the metrics, then reassign the check-in areas and adjust the internal departure sequence of flights to maximize the counter-sharing rate. The numerical results indicate that the proposed counter-sharing method can improve the efficiency of check-in counters and reduce the passenger’s total walking distance and waiting time in the queue during the check-in process, which can enhance the airport’s operation efficiency and competitiveness.

Evaluation of the Trade-Off between Ground Delays and Intersecting Departures under Various Pilot Acceptance Rate Scenarios

The increasing demand for air traffic at airports necessitates the efficient utilization of ground facilities such as runways and taxiways. Intersecting departures, in which one or more aircraft take off from intersecting points on the runway, is a commonly used approach to increase runway capacity and reduce ground delays and taxi times, as well as noise and air pollution. However, the procedure carries potential risks such as runway incursion and excursion. This creates a trade-off between minimizing the number of intersecting departures and minimizing ground delays. In practice, the decision to perform an intersecting departure is ultimately up to the pilot, resulting in uncertainty in the acceptance rate of these types of takeoffs. In this study, a departure sequencing model was developed for a single-runway airport that considers intersecting departures and various pilot acceptance rate scenarios. The primary objective of the model is to minimize total ground delay, including taxi delays, runway holds, and conflict holds. The secondary objective is to minimize the number of intersecting departures by directing the most operationally critical aircraft to the intersection takeoff. The epsilon constraint method—a multi-objective scalarization method—was used to reveal the trade-offs between the objective functions. The results of the model were compared with a traditional scenario that only allows take offs from the beginning of the runway. As a result, average delay savings ranged from 17.1% to 31.5% in various acceptance rate scenarios, as well as average taxi time savings ranging from 4.9% to 8.4% compared with the traditional scenario.

Efficient Aircraft Arrival Sequencing Given Airport Gate Assignment

The aircraft sequencing problem has been well discussed in many variants over the years; and during this time, the problem has changed from a standalone optimization problem to an embedded problem. At hub airports, aircraft sequencing influences passengers’ available connecting times. Short connections could benefit from a modification of the arrival or departure sequence. In our work, we have developed a bilevel optimization problem that combines the aircraft sequencing problem and the airport gate assignment problem at hub airports to make connections more efficient. The decision on the gate assignments is made at the upper level, whereas the efficient aircraft arrival sequencing is addressed at the lower level. Methods for solving this kind of large-scale bilevel optimization problem by considering a full day of operation and dynamically changing traffic situations have yet to be researched. In this paper, we focus on efficient aircraft sequencing by taking into consideration gate assignments, and we propose a multiobjective mixed integer linear program that is solved by an interactive method and considers the (contradicting) objectives of competing airlines and the air traffic control. The capabilities of our approach are demonstrated by modeling and solving a real-case scenario for Munich Airport.

Aircraft Trajectory Prediction for Terminal Airspace Employing Social Spatiotemporal Graph Convolutional Network

Four-dimensional (4-D) trajectory prediction is the core of air traffic management technologies such as flow management, conflict detection and resolution, arrival and departure sequencing, and aircraft abnormal behavior monitoring. The airport terminal airspace has a complex airspace structure, and the flight status of aircraft is changeable, which poses a challenge to trajectory prediction. To this end, aiming at the problem of track prediction in airport terminal area, we propose a 4-D trajectory prediction model of social spatiotemporal graph convolutional neural network (S-STGCNN) based on pattern matching. For each type of flight pattern, an S-STGCNN is trained to improve the robustness. Taking each aircraft as a node of a graph, the spatiotemporal graph convolution is used to extract features of the graph so as to simultaneously characterize the time dependence of trajectories and the interaction between aircraft. The time extrapolation convolutional neural network is used to generate the predicted trajectory. Experimental results show that the trajectory prediction method proposed in this paper has a higher prediction accuracy and a generalization ability than other models.

Structured Urban Airspace Capacity Analysis: Four Drone Delivery Cases

A route network-based urban airspace is one of the initial operational concepts of managing the high-density very low-level (VLL) urban airspace for unmanned aircraft system (UAS) traffic management (UTM). For the conceptual urban airspace, it is necessary to perform a quantitative analysis of urban airspace to stakeholders for designing rules and regulations. This study aims to discuss the urban airspace capacity for four different operation types by applying different sequencing algorithms and comparing its results to provide insight and suggestions for different operation cases to assist airspace designers, regulators, and policymakers. Four drone delivery operation types that can be applied in the high-density VLL urban airspace are analysed using the suggested four metrics: total flight time; total flight distance; mission completion time; the number of conflicts. The metrics can be calculated from a flight planning algorithm that we proposed in our previous studies. The algorithm for multiple agents flight planning problems consists of an inner loop algorithm, which calculates each agent’s flight plan, and an outer loop algorithm, which determines the arrival and departure sequences. For each operation type, we apply two different outer loops with the same inner loop to suggest an appropriate sequencing algorithm. Numerical simulation results show tendencies for each type of operation with regard to the outer loop algorithms and the number of agents, and we analyse the results in terms of airspace capacity, which could be utilised for designing structures depending on urban airspace situations and environments. We expect that this study could give some intuition and support to policymakers, urban airspace designers, and regulators.

Mitigation of ADS-B Spoofing Attack Impact on Departure Sequencing Through Modulated Synchronous Taxiing Approach

Mitigation of ADS-B Spoofing Attack Impact on Departure Sequencing Through Modulated Synchronous Taxiing Approach

Real-time rescheduling approach of train operation for high-speed railways using problem-specific knowledge under a station blockage

This study considers the train timetable as the problem object to investigate the station blockage caused by emergencies. Based on the evolutionary algorithm framework, a real-time rescheduling approach using problem-specific knowledge is proposed. This approach ensures the safety and efficiency of high-speed rail operations and the satisfaction and comfort of passengers by minimizing the total train delay. First, a permutation encoding method is developed to reduce invalid searches in the search space based on the rescheduling strategy of reordering train departure sequences. Next, according to the train operation tracking mode, called tracking interval control, a heuristic decoding method is designed to eliminate all constraints of train operation to improve efficiency. Finally, the dispatcher's experience in adjusting the train timetable is used as the problem-specific knowledge to initialize the initial population of evolutionary computing. A heuristic population initialization method based on problem-specific knowledge is employed to speed up the algorithm convergence in the early stage and improve solution equality. Furthermore, the Beijing–Tianjin high-speed railway line is used as an example. Nine typical scenarios with different blockage durations of 20–150 min under the station blockage are installed at the Beijing South station. The strengthened elitist genetic algorithm (SEGA) and differential evolution (DE) are selected to perform the simulation using different combinations of the real-integer or permutation encoding and random or heuristic population initialization, respectively. The simulation results indicate that, compared with the real-integer encoding that cannot obtain feasible solutions, the two evolutionary algorithms can provide the rescheduling solution with a smaller total train delay in an average time of 9 s after using the permutation encoding. Besides, the results of the two evolutionary algorithms improved by the heuristic population initialization can quickly converge to a quasi-optimal solution. Finally, the SEGA in the permutation encoding with the heuristic population initialization is chosen as the optimal improved evolutionary algorithm. This improved evolutionary algorithm can provide quasi-optimal solutions in 20 s in the seven scenarios where the CPLEX cannot provide optimal solutions in 10 min.

An Improved Genetic Algorithm-Based Traffic Scheduling Model for Airport Terminal Areas

This paper takes the airport terminal area as the main research content and combines genetic algorithm with airport terminal area analysis theory to analyze and study the traffic scheduling in the airport terminal area. Based on the study of traditional traffic scheduling techniques and key techniques of genetic algorithms, this paper participates in the actual project of genetic algorithm-based traffic scheduling, analyzes the requirements of the project, focuses on the design and implementation of the traffic scheduling algorithm module in the genetic algorithm-based traffic scheduling system, and conducts further research on the pathfinding by constraints submodule. In this paper, the flight approach and departure sequencing problem and runway allocation problem are the main research objects. The dynamic optimal scheduling model of flight approach and departure is established by considering the interests and demands of airlines and airports, and a new scheduling algorithm is proposed. In this paper, a brief introduction to the airport terminal area is given, and the feasibility of the approach/departure optimal scheduling is introduced from the perspective of airlines with a long-range parallel two-runway airport as the research background. Secondly, through the analysis of the flight approach and departure process and the study of the approach and departure cooperative optimization strategy, a single-runway flight approach and departure traffic scheduling model under the joint sequencing strategy is established with the optimization objective of minimizing the total flight delay time, and the model is solved by using the sliding time window algorithm. Then, based on the single-runway scheduling model, a multirunway multiobjective flight optimal scheduling model is established with the objectives of minimizing total delay time, increasing runway throughput per unit time and fairness of flight delay time allocation, and a dynamic algorithm (STW-GA) combining sliding time window algorithm and dual-structured chromosome genetic algorithm is proposed to solve the model.

A Heuristic Charging Cost Optimization Algorithm for Residential Charging of Electric Vehicles

The charging loads of electric vehicles (EVs) at residential premises are controlled through a tariff system based on fixed timing. The conventional tariff system presents the herding issue, such as with many connected EVs, all of them are directed to charge during the same off-peak period, which results in overloading the power grid and high charging costs. Besides, the random nature of EV users restricts them from following fixed charging times. Consequently, the real-time pricing scenarios are natural and can support optimizing the charging load and cost for EV users. This paper aims to develop charging cost optimization algorithm (CCOA) for residential charging of EVs. The proposed CCOA coordinates the charging of EVs by heuristically learning the real-time price pattern and the EV’s information, such as the battery size, current state-of-charge, and arrival & departure times. In contrast to the holistic price, the CCOA determines a threshold price value for each arrival and departure sequence of EVs and accordingly coordinates the charging process with optimizing the cost at each scheduling period. The charging cost is captured at the end of each charging activity and the cumulative cost is calculated until the battery’s desired capacity. Various charging scenarios for individual and aggregated EVs with random arrival sequences of EVs against the real-time price pattern are simulated through MATLAB. The simulation results show that the proposed algorithm outperforms with a low charging cost while avoiding the overloading of the grid compared to the conventional uncoordinated, flat-rate, and time-of-use systems.

Aircraft Sequencing and Scheduling in Parallel-Point Merge Systems for Multiple Parallel Runways

The Point Merge System (PMS) is one of the approaches that offer systematic sequencing in terminal maneuver area (TMA) which was developed at the EUROCONTROL Experimental Centre in 2006. The Parallel-Point Merge System (P-PMS) is used in TMAs with high air traffic density as a combination of two PMSs with opposite directions. However, it is difficult to optimize arrival sequences in such a system because of the additional separation requirements at common points as well as at merge points. Additionally, it is important to operate at maximum efficiency with parallel runway systems when using the runway reassignment process. Reassigning arriving/departing aircraft to a different runway, however, can significantly affect delays and fuel consumption depending on the difference in taxi-in/out times. This effect is overlooked in most aircraft sequencing and scheduling optimization studies with PMS. This study proposes single and multi-objective programming models for a TMA with P-PMS to minimize total fuel consumption, total flight time, and total delay including taxi-in/out times. The models were implemented on the current layout of Istanbul Airport having two PMS with two merge points, three common points, and five parallel runways. The considered traffic scenarios included both dependent and independent runway operations for mixed arrival–departure sequences. The results revealed that arrival–departure sequencing considering taxi-in/out times resulted in shorter delays up to 77.5% and low level of fuel consumption up to 8.7%.

Timing Matters

This article studies the equilibrium states that can be reached in a network design game via natural game dynamics. First, we show that an arbitrarily interleaved sequence of arrivals and departures of players can lead to a polynomially inefficient solution at equilibrium. This implies that the central controller must have some control over the timing of agent arrivals and departures to ensure efficiency of the system at equilibrium. Indeed, we give a complementary result showing that if the central controller is allowed to restore equilibrium after every set of arrivals/departures via improving moves , then the eventual equilibrium states reached have exponentially better efficiency.

Multi-Aircraft Trajectory Collaborative Prediction Based on Social Long Short-Term Memory Network

Aircraft trajectory prediction is the basis of approach and departure sequencing, conflict detection and resolution and other air traffic management technologies. Accurate trajectory prediction can help increase the airspace capacity and ensure the safe and orderly operation of aircraft. Current research focuses on single aircraft trajectory prediction without considering the interaction between aircraft. Therefore, this paper proposes a model based on the Social Long Short-Term Memory (S-LSTM) network to realize the multi-aircraft trajectory collaborative prediction. This model establishes an LSTM network for each aircraft and a pooling layer to integrate the hidden states of the associated aircraft, which can effectively capture the interaction between them. This paper takes the aircraft trajectories in the Northern California terminal area as the experimental data. The results show that, compared with the mainstream trajectory prediction models, the S-LSTM model in this paper has smaller prediction errors, which proves the superiority of the model’s performance. Additionally, another comparative experiment is conducted on airspace scenes with aircraft interactions, and it is found that S-LSTM has a better prediction effect than LSTM, which proves the effectiveness of the former considering aircraft interaction.

Joint Optimization of Intersection Control and Trajectory Planning Accounting for Pedestrians in a Connected and Automated Vehicle Environment

Connected and automated vehicle (CAV) technology makes it possible to track and control the movement of vehicles, thus providing enormous potential to improve intersection operations. In this paper, we study the traffic signal control problem at an isolated intersection in a CAV environment, considering mixed traffic including various types of vehicles and pedestrians. Both the vehicle delay and the pedestrian delay are incorporated into the model formulation. This introduces some additional complexity, as any benefits to pedestrians will come at the expense of higher delays for the vehicles. Thus, some valid questions we answer in this paper are as follows: Under which circumstances could we provide priority to pedestrians without over penalizing the vehicles at the intersection? How important are the connectivity and autonomy associated with CAV technology in this context? What type of signal control algorithm could be used to minimize person delay accounting for both vehicles and pedestrians? How could it be solved efficiently? To address these questions, we present a model that optimizes signal control (i.e., vehicle departure sequence), automated vehicle trajectories, and the treatment of pedestrian crossing. In each decision step, the weighted sum of the vehicle delay and the pedestrian delay (e.g., the total person delay) is minimized by the joint optimization on the basis of the predicted departure sequences of vehicles and pedestrians. Moreover, a near-optimal solution of the integrated problem is obtained with an ant colony system algorithm, which is computationally very efficient. Simulations are conducted for different demand scenarios and different CAV penetration rates. The performance of the proposed algorithm in terms of the average person delay is investigated. The simulation results show that the proposed algorithm has potential to reduce the delay compared to an actuated signal control method. Moreover, in comparison to a CAV-based signal control that does not account for the pedestrian delay, the joint optimization proposed here can achieve improvement in the low- and moderate-vehicle-demand scenarios.

Grasping behavior of the human hand during tomato picking

To comprehensively investigate the grasping behavior of the human hand during tomato picking, a new 16-channel data acquisition e-glove was developed and subsequently worn to randomly pick 60 tomatoes in a greenhouse. A 2-channel surface electromyography was used to record the electrical activity of the flexor pollicis longus and flexor digitorum superficialis in real time. Each fruit-picking process was divided into fruit-stem separation, fruit moving and release stages based on two peak pressures applied by the thumb distal region. The total grasping pressure at the moment of fruit-stem separation primarily depended on the intersection angle between the first and second stems of a fruit. Conversely, at the moment of fruit release, the total grasping pressure primarily depended on the fruit size (p < 0.05). The duration of the tomato-picking process ranged from 2.28 to 4.54 s. The duration of the separation between the first and second stems of a fruit ranged from 0.26 to 1.56 s, and the duration of the fruit release ranged from 0.34 to 2.02 s. The entry and departure sequences of 16 hand regions varied during picking. The frequency analysis results showed that the thumb distal region always entered first to contact the fruit surface for fruit-stem separation, while the ring finger distal region always departed first from the fruit surface for fruit release. At the moment of fruit-stem separation, the number of contact regions between the hand and the fruit significantly increased with the fruit size (p < 0.05), which was slightly different from that at the moment of fruit release. The distal regions of the thumb, middle finger and index finger always made the greatest pressure contribution to the picking of each fruit. The fruit surface pressure applied by the thumb and the other four fingers were moderately related to the mean absolute values of the electrical activity level of the flexor pollicis longus and the flexor digitorum superficialis, respectively.