Impact of continuous climb operations on airport capacity

The full benefits of Continuous Climb Operations (CCO) are realised when CCO are performed without interruption. However, CCO require safe departures that necessarily implies a reduction in capacity at high density traffic airports. This paper quantifies the capacity impact due to the integration of CCO (conflict-free with other departures and arrivals) in a high density traffic airport. The methodology develops a scheduling algorithm, a conflict-detection and resolution algorithm, and Monte Carlo simulations. The scheduling algorithm calculates two schedules, one for departures and another for arrivals, considering the CCO Rate. The conflict-detection and resolution algorithm compares CCO and arrival trajectories to detect which aircraft pairs are in conflict. The Air Traffic Control (ATC) intervention required to solve the conflict is modelled by delaying the CCO take-off. Numerical simulations based on Monte Carlo techniques are used to analyse scheduling combinations that are statistically significant in terms of conflict, ATC interventions, total delay and capacity. The results show a 32% reduction in the maximum theoretical capacity with a CCO Rate of 100%. Despite the reduction, the number of CCO departures is above the maximum operational capacity (50% of the maximum theoretical capacity). This implies that with optimised scheduling it is possible for all departures to be CCO.     

Predictability impacts of airport surface automation

Past evaluations of airport surface operations automation technologies have focused on capacity utilization, delay mitigation and fuel efficiency impacts. Predictability, while recognized as an important operational performance goal, has received little attention. One reason could be that applicable predictability metrics have not been developed in the context of airport surface operations management. This research fills the gap by proposing metrics for predictability performance evaluation. Using results from a SARDA human-in-the-loop simulation conducted at NASA Ames’ Future Flight Central, we present a comprehensive assessment of the predictability impacts of airport surface automation. A wide range of the impacts is considered, which includes variability in taxi-out time, predictability of take-off time and take-off sequence, entropy of the airfield state, and perceived predictability from users.     

An efficient algorithm for smoothing airspace congestion by fine-tuning take-off times

Current technological advances in communications and navigation have improved air traffic management (ATM) with new decision support tools to balance airspace capacity with user demands. Despite agreements achieved in flying reference business trajectories (RBTs) among different stakeholders, tight spatio-temporal connectivity between trajectories in dense sectors can cause perturbations that might introduce time or space deviations into the original RBTs, thus potentially affecting other 4D trajectories. In this paper, several challenging results are presented by properly tuning the Calculated Take-Off Times (CTOTs) as a tool for mitigating the propagation of perturbations between trajectories that can readily appear in dense sectors. Based on the identification of “collective microregions”, a tool for predicting potential spatio-temporal concurrence events between trajectories over the European airspace was developed, together with a CTOT algorithm to sequence the departures that preserve the scheduled slots while relaxing tight trajectory interactions. The algorithm was tested by considering a realistic scenario (designed and analyzed in the STREAM project (Stream, 2013)) to evaluate relevant ATM KPIs that provide aggregated information about the sensitivity of the system to trajectory interactions, taking into account the system dynamics at a network level. The proposed approach contributes to enhancing the ATM capacity of airports to mitigate network perturbations.     

Investigation of environmental justice analysis in airport planning practice from 2000 to 2010

Municipal airport owners and the Federal Aviation Administration (FAA) regularly evaluate capacity and demand to decide if and when airports need more infrastructure. New infrastructure can alter the profile of noise, emissions, and land use, which may affect the quality of life for airport-adjacent communities. When the FAA and airport owners initiate infrastructure expansion, they must conduct environmental justice analysis to measure the distribution of negative externalities on nearby communities. This research investigates the environmental justice methodologies and narratives reported in planning documents for nineteen airport capacity expansions planned or deployed from 2000 to 2010 in the United States. The mixed-methods approach analyzes airport operations data, spatial demographic data, and planning artifacts to determine whether the environmental justice analyses were robust. This research proposes alternative metrics, the ‘Risk of disproportionate impact’ and ‘Capacity strain’, to further contextualize the presence of protected population groups alongside capacity needs. The main finding of the study is that the planning documents did not consistently detect environmental justice impacts, nor did they consistently confer importance to those impacts when high proportions of protected populations were detected. As a result, the social costs of collective airport expansion are unclear and likely underestimated. This study identifies two limitations that undermined the environmental justice analysis throughout the airport sample: (1) inconsistent methodological choices impeded the detection of impacts and, (2) narrative interpretations tended to ‘null’ the finding even when impacts were detected.     

Assessing the impact of tactical airport surface operations on airline schedule block time setting

With the growth of air traffic, airport surfaces are congested and air traffic operations are disrupted by the formation of bottlenecks on the surface. Hence, improving the efficiency and predictability of airport surface operations is not only a key goal of NASA’s initiatives in Integrated Arrival/Departure/Surface (IADS) operations, but also has been recognized as a critical aspect of the FAA NextGEN implementation plan. While a number of tactical initiatives have been shown to be effective in improving airport surface operations from a service provider’s perspective, their impacts on airlines’ scheduled block time (SBT) setting, which has been found to have direct impact on airlines’ on-time performance and operating cost, have received little attention. In this paper, we assess this impact using an econometric model of airline SBT combined with a before/after analysis of the implementation of surface congestion management (SCM) at John F. Kennedy International Airport (JFK) in 2010. Since airlines do not consider gate delay in setting SBT, we find that reduction in taxi-out time variability resulting from SCM leads to more predictable taxi-out times and thus decreases in SBT. The JFK SCM implementation is used as a case study to validate model prediction performance. The observed SBT decrease between 2009 and 2011 at JFK is 4.8 min and our model predicts a 4.2 min decrease. In addition, Charlotte Douglas International Airport (CLT) is used as an example to demonstrate how different surface operations improvements scenarios can be evaluated in terms of SBT reduction.     

Comparison of emerging ground propulsion systems for electrified aircraft taxi operations

Aviation is a mode with high fuel consumption per passenger mile and has significant environmental impacts. It is important to seek ways to reduce fuel consumption by the aviation sector, but it is difficult to improve fuel efficiency during the en-route cruise phase of flight because of technology barriers, safety requirements, and the mode of operations of air transportation. Recent efforts have emphasized the development of innovative Aircraft Ground Propulsion Systems (AGPS) for electrified aircraft taxi operations. These new technologies are expected to significantly reduce aircraft ground-movement-related fuel burn and emissions. This study compares various emerging AGPS systems and presents a comprehensive review on the merits and demerits of each system, followed with the local environmental impacts assessment of these systems. Using operational data for the 10 busiest U.S. airports, a comparison of environmental impacts is performed for four kinds of AGPS: conventional, single engine-on, external, and on-board systems. The results show that there are tradeoffs in fuel and emissions among these emerging technologies. On-board system shows the best performance in the emission reduction, while external system shows the least fuel burn. Compared to single-engine scenario, external AGPS shows the reduction of HC and CO emissions but the increase of NO_x emission. When a general indicator is considered, on-board AGPS shows the best potential of reducing local environmental impacts. The benefit-cost analysis shows that both external and on-board systems are worth being implemented and the on-board system appeals to be more beneficial.     

Operating cost sensitivity to required time of arrival commands to ensure separation in optimal aircraft 4D trajectories

Trajectory optimisation has shown good potential to reduce environmental impact in aviation. However, a recurring problem is the loss in airspace capacity that fuel optimal procedures pose, usually overcome with speed, altitude or heading advisories that lead to more costly trajectories. This paper aims at the quantification in terms of fuel and time consumption of implementing suboptimal trajectories in a 4D trajectory context that use required times of arrival at specific navigation fixes. A case study is presented by simulating conflicting Airbus A320 departures from two major airports in Catalonia. It is shown how requiring an aircraft to arrive at a waypoint early or late leads to increased fuel burn. In addition, the efficiency of such methods to resolve air traffic conflicts is studied in terms of both fuel burn and resulting aircraft separations. Finally, various scenarios are studied reflecting various airline preferences with regards to cost and fuel burn, as well as different route and conflict geometries for a broader scope of study.     

Airport capacity increase via the use of braking profiles

Many airports are encountering the problem of insufficient capacity, which is particularly severe in periods of increased traffic. A large number of elements influence airport capacity, but one of the most important is runway occupancy time. This time depends on many factors, including how the landing roll procedure is performed. The procedure usually does not include the objective to minimize the runway occupancy time. This paper presents an analysis which shows that the way of braking during landing roll has an essential impact on runway throughput and thus on airport capacity. For this purpose, the landing roll simulator (named ACPENSIM) was created. It uses Petri nets and is a convenient tool for dynamic analysis of aircraft movement on the runway with given input parameters and a predetermined runway exit. Simulation experiments allowed to create a set of nominal braking profiles that have different objective functions: minimizing the runway occupancy time, minimizing noise, minimizing tire wear, maximizing passenger comfort and maximizing airport capacity as a whole. The experiments show that there is great potential to increase airport capacity by optimizing the braking procedure. It has been shown that by using the proposed braking profiles it is possible to reduce the runway occupancy time even by 50%.     

Analysis of air traffic control operational impact on aircraft vertical profiles supported by machine learning

The Air Traffic Management system is under a paradigm shift led by NextGen and SESAR. The new trajectory-based Concept of Operations is supported by performance-based trajectory predictors as major enablers. Currently, the performance of ground-based trajectory predictors is affected by diverse factors such as weather, lack of integration of operational information or aircraft performance uncertainty.Trajectory predictors could be enhanced by learning from historical data. Nowadays, data from the Air Traffic Management system may be exploited to understand to what extent Air Traffic Control actions impact on the vertical profile of flight trajectories.This paper analyses the impact of diverse operational factors on the vertical profile of flight trajectories. Firstly, Multilevel Linear Models are adopted to conduct a prior identification of these factors. Then, the information is exploited by trajectory predictors, where two types are used: point-mass trajectory predictors enhanced by learning the thrust law depending on those factors; and trajectory predictors based on Artificial Neural Networks.Air Traffic Control vertical operational procedures do not constitute a main factor impacting on the vertical profile of flight trajectories, once the top of descent is established. Additionally, airspace flows and the flight level at the trajectory top of descent are relevant features to be considered when learning from historical data, enhancing the overall performance of the trajectory predictors for the descent phase.     

Maximizing airborne delay at no extra fuel cost by means of linear holding

This paper introduces a linear holding strategy based on prior works on cruise speed reduction, aimed at performing airborne delay at no extra fuel cost, as a complementary strategy to current ground and airborne holding strategies. Firstly, the equivalent speed concept is extended to climb and descent phases through an analysis of fuel consumption and speed from aircraft performance data. This gives an insight of the feasibility to implement the concept, differentiating the case where the cruise flight level initially requested is kept and the case where it can be changed before departure in order to maximize the linear holding time. Illustrative examples are given, where typical flights are simulated using an optimal trajectory generation tool where linear holding is maximized while keeping constant the initially planned fuel. Finally, the effects of linear holding are thoroughly assessed in terms of the vertical trajectory profiles, range of feasible speed intervals and trade-offs between fuel and time. Results show that the airborne delay increases significantly with nearly 3-fold time for short-haul flights and 2-fold for mid-hauls to the cases in prior works.     

Controlled time of arrival windows for already initiated energy-neutral continuous descent operations

Continuous descent operations with controlled times of arrival at one or several metering fixes could enable environmentally friendly procedures without compromising terminal airspace capacity. This paper focuses on controlled time of arrival updates once the descent has been already initiated, assessing the feasible time window (and associated fuel consumption) of continuous descent operations requiring neither thrust nor speed-brake usage along the whole descent (i.e. only elevator control is used to achieve different metering times). Based on previous works, an optimal control problem is formulated and numerically solved. The earliest and latest times of arrival at the initial approach fix have been computed for the descent of an Airbus A320 under different scenarios, considering the potential altitudes and distances to go when receiving the controlled time of arrival update. The effects of the aircraft mass, initial speed, longitudinal wind and position of the initial approach fix on the time window have been also investigated. Results show that time windows about three minutes could be achieved for certain conditions, and that there is a trade-off between robustness facing controlled time of arrival updates during the descent and fuel consumption. Interestingly, minimum fuel trajectories almost correspond to those of minimum time.     

Ensemble based traffic light control for city zones using a reduced number of sensors

Rapid advances in computing, sensing and telecommunication technology offer unprecedented opportunities for artificial intelligence concepts to expand their applications in the field of traffic management and control. Our methodology gravitates around a powerful decision-making method: ensemble-based systems. This technique is used to accurately classify the near future traffic conditions and to make efficient decisions for adapting the traffic lights sequences within an urban area to optimize the traffic flows. The proposed approach requires only measurements provided by traffic sensors located along the principal roads entering the zone. This reduced number of sensors are considered to be enough relevant for classifying the near future state of the traffic and moreover, their measurements can be validated through analytical/hardware redundancy. Our methodology is meant to be implemented within the framework of a wireless sensor and actuator network and is confirmed by computer simulation, including normal or abnormal traffic conditions, for the central part of the city of Timisoara-Romania.     

Development and evaluation of a knowledge-based system for traffic congestion management and control

This paper describes a real-time knowledge-based system (KBS) for decision support to Traffic Operation Center personnel in the selection of integrated traffic control plans after the occurrence of non-recurring congestion, on freeway and arterial networks. The uniqueness of the system, called TCM, lies in its ability to cooperate with the operator, by handling different sources of input data and inferred knowledge, and providing an explanation of its reasoning process. A data fusion algorithm for the analysis of congestion allows to represent and interpret different types of data, with various levels of reliability and uncertainty, to provide a clear assessment of traffic conditions. An efficient algorithm for the selection of control plans determines alternative traffic control responses. These are proposed to an operator, along with an explanation of the reasoning process that led to their development and an estimation of their expected effect on traffic. The validation of the system, which is one of only few examples of validation of a KBS in transportation, demonstrates the validity of the approach. The evaluation results, in a simulated environment demonstrate the ability of TCM to reduce congestion, through the formulation of traffic diversion and control schemes.     

Metaheuristics for efficient aircraft scheduling and re-routing at busy terminal control areas

Intelligent decision support systems for the real-time management of landing and take-off operations can be very effective in helping air traffic controllers to limit airport congestion at busy terminal control areas. The key optimization problem to be solved regards the assignment of airport resources to take-off and landing aircraft and the aircraft sequencing on them. The problem can be formulated as a mixed integer linear program. However, since this problem is strongly NP-hard, heuristic algorithms are typically adopted in practice to compute good quality solutions in a short computation time. This paper presents a number of algorithmic improvements implemented in the AGLIBRARY solver (a state-of-the-art optimization solver to deal with complex routing and scheduling problems) in order to improve the possibility of finding good quality solutions quickly. The proposed framework starts from a good initial solution for the aircraft scheduling problem with fixed routes (given the resources to be traversed by each aircraft), computed via a truncated branch-and-bound algorithm. A metaheuristic is then applied to improve the solution by re-routing some aircraft in the terminal control area. New metaheuristics, based on variable neighbourhood search, tabu search and hybrid schemes, are introduced. Computational experiments are performed on an Italian terminal control area under various types of disturbances, including multiple aircraft delays and a temporarily disrupted runway. The metaheuristics achieve solutions of remarkable quality, within a small computation time, compared with a commercial solver and with the previous versions of AGLIBRARY.     

Exploring the effects of cooperative adaptive cruise control on highway traffic flow using microscopic traffic simulation

This paper examines the impact of having cooperative adaptive cruise control (CACC) embedded vehicles on traffic flow characteristics of a multilane highway system. The study identifies how CACC vehicles affect the dynamics of traffic flow on a complex network and reduce traffic congestion resulting from the acceleration/deceleration of the operating vehicles. An agent-based microscopic traffic simulation model (Flexible Agent-based Simulator of Traffic) is designed specifically to examine the impact of these intelligent vehicles on traffic flow. The flow rate of cars, the travel time spent, and other metrics indicating the evolution of traffic congestion throughout the lifecycle of the model are analyzed. Different CACC penetration levels are studied. The results indicate a better traffic flow performance and higher capacity in the case of CACC penetration compared to the scenario without CACC-embedded vehicles.     

Exploring the issue of integrating logistics and traffic control in urban areas

This paper proposes an optimization framework for urban transportation networks’ (re-)design which explicitly takes into account the specific decision-making processes of ordinary users and logistic operators. Ordinary users are typically commuters whose travels consist of well-defined pairs of origin and destination points, while logistic operators make deliveries at multiple locations. Obviously, these two user classes have different objectives and scopes of action. These differences are seldom considered in traffic research since most models aggregate the flow demand in OD matrices and use assignment models to predict the response of all users as if the dynamics of their optimization processes were of the same nature. This work demonstrates that better results can be achieved if the particular features of each user class are included in the models. It potentially improves the estimation of the responses and allows managers to shape their control measures to address specific user needs.