Uncertainty management at the airport transit view

Air traffic networks, where airports are the nodes that interconnect the entire system, have a time-varying and stochastic nature. An incident in the airport environment may easily propagate through the network and generate system-level effects. This paper analyses the aircraft flow through the Airport Transit View framework, focusing on the airspace/airside integrated operations. In this analysis, we use a dynamic spatial boundary associated with the Extended Terminal Manoeuvring Area concept. Aircraft operations are characterised by different temporal milestones, which arise from the combination of a Business Process Model for the aircraft flow and the Airport Collaborative Decision-Making methodology. Relationships between factors influencing aircraft processes are evaluated to create a probabilistic graphical model, using a Bayesian network approach. This model manages uncertainty and increases predictability, hence improving the system's robustness. The methodology is validated through a case study at the Adolfo Suárez Madrid-Barajas Airport, through the collection of nearly 34,000 turnaround operations. We present several lessons learned regarding delay propagation, time saturation, uncertainty precursors and system recovery. The contribution of the paper is two-fold: it presents a novel methodological approach for tackling uncertainty when linking inbound and outbound flights and it also provides insight on the interdependencies among factors driving performance

Airport Ground Access Reliability and Resilience of Transit Networks: a Case Study

Abstract Airport ground access is one of the key determinants influencing air travellers' airport choice. The continuous growth of air travel demand and the consequent induced road congestion have encouraged the development of efficient transit systems approaching the airport, thus promoting a modal shift from individual cars to greener transport alternatives. In addition, transit systems must be resilient and reliable to air travellers, since the cost of missing a flight is high. In this paper, resilience aspects of transit systems accessing airport areas are discussed and some indexes have been set up to estimate the transit network resilience. Three different transit systems to get to a large regional Italian airport (Automated People Mover, Airport Shuttle Bus, Bus Line) are modelled and the system resilience has been estimated for each scenario by using the proposed indexes

Collaborative rescheduling of flights in a single mega-hub network

Traditionally, airlines have configured flight operations into a Hub and Spoke network design. Using connecting arrival departure waves at multiple hubs these networks achieve efficient passenger flows. Recently, there has been much growth in the development of global single mega-hub (SMH) flight networks that have a significantly different operating cost structure and schedule design. These are located primarily in the Middle East and are commonly referred to as the ME3. The traditionalist view is that SMH networks are money losers and subsidized by sovereign funds. This research studies and analyzes SMH networks in an attempt to better understand their flight efficiency drivers. Key characteristics of SMH airports are identified as: (i) There are no peak periods, and flight activity is balanced with coordinated waves (ii) No priority is assigned to arrival/departure times at destinations (selfish strategy) only hub connectivity is considered (iii) There is less than 5% OD traffic at SMH (iv) The airline operates only non-stop flights (v) Passengers accept longer travel times in exchange for economic benefits (vi) Airline and airport owners work together to achieve collaborative flight schedules. This research focuses on the network structure of SMH airports to identify and optimize the operational characteristics that are the source of their advantages. A key feature of SMH airports is that the airline and airport are closely aligned in a partnership. To model this relationship, the Mega-Hub Collaborative Flight Rescheduling (MCFR). Problem is introduced. The MCFR starts with an initial flight schedule developed by the airline, then formulates a cooperative objective which is optimized iteratively by a series of reschedules. Specifically, in a network of iEM cities, the decision variables are i* the flight to be rescheduled, Di* the new departure time of flight to city i* and Hi* the new hold time at the destinatioin city i*. The daily passenger traffic is given by Ni,j and normally distributed with parameters µNi,j and sNi,j. A three-term MCFR objective function is developed to represent the intersecting scheduling decision space between airlines and airports: (i) Passenger Waiting Time (ii) Passenger Volume in Terminal, and (iii) Ground Activity Wave Imbalance. The function is non-linear in nature and the associated constraints and definitions are also non-linear. An EXCEL/VBA based simulator is developed to simulate the passenger traffic flows and generate the expected cost objective for a given flight network. This simulator is able to handle up to an M=250 flight network tracking 6250 passenger arcs. A simulation optimization approach is used to solve the MCFR. A Wave Gain Loss (WGL) strategy estimates the impact Zi of flight shift Δi on the objective. The WGL iteratively reschedules flights and is formulated as a non-linear program. It includes functions to capture the traffic affinity driven solution dependency between flights, the relationship between passengers in terminal gradients and flight shifts, and the relationship between ground traffic activity gradients and flight shifts. Each iteration generates a Zi ranked list of flights. The WGL is integrated with the EXCEL/VBA simulator and shown to generate significant costs reduction in an efficient time. Extensive testing is done on a set of 5 flight network problems, each with 3 different passengers flow networks characterized by low, medium and high traffic concentrations

Maximising runway capacity by mid-term prediction of runway configuration and aircraft sequencing using machine learning

Maximising runway capacity is one of the essential measures to meet the growing traffic demand. Runway capacity maximisation is an open challenge in the literature due to a complex and non-linear interplay of many factors which are stochastic in nature such as wind, weather, and arrival and departure sequence of aircraft. However, an effective sequencing of arrivals and departures may condense the service time on runway, thereby generating opportunities for new landing or take-off slots, which may maximise the runway capacity. In addition, sequencing of arrivals and departures optimised for a predicted runway configuration, given the weather and wind conditions, may lead to maximising the runway capacity. First, I develop an optimisation method, using aircraft position shifting and path-planning, for aircraft sequencing for a single runway airport. The proposed method can provide an optimal aircraft sequence, for both arrivals and departures, such that it minimises the total inter-arrival and departure times and, consequently, maximises the runway throughput. The proposed method implements several arrival/departure sequencing strategies, i.e., constraint position-shifting with one, two and N-positions, and First Come First Serve (FCFS) in order to obtain an optimal sequence (i.e., a sequence with the lowest process time). The novelty of the sequencing model is to incorporate the Standard Terminal Arrival Routes (STAR) for path planning and sequencing of arriving aircraft at Final Approach Fix (FAF) and departing aircraft sequence at the runway threshold. The simulation result demonstrates that the model can increase up to 15% of the runway capacity compared with the commonly used aircraft sequencing technique (i.e., FCFS). Second, a runway configuration (i.e., a set of runways active in a specific period in a multi-runaway system) plays a vital role in determining runway capacity. Thus, I developed an evolutionary computation (Cooperative Co-evolutionary with Genetic Algorithm) algorithm for determining which runway configuration is most suitable for processing a given optimal aircraft sequence (arrival-departure), such that the runway capacity is maximised in a multi-runway system. The proposed algorithm models and uses Runway Configuration Capacity Envelopes (RCCEs) which defines arrival and departure throughputs. RCCE helps in identifying the unique capacity constraints based on which runways are used, for example, arrivals/departures or both. In the proposed evolutionary algorithm (CCoGA), the runway configuration and aircraft sequence are modelled as two species which interacts and evolve co-operatively to yield the best populations (combination) for maximising runway capacity. The fitness function for the optimal sequence species is to reduce the total process time for a given runway configuration, while fitness function for the runway configuration species is to maximises the total capacity for the given optimal sequence. The simulation results show that CCoGA can provide trade-off solutions with multiple runway configurations, for a given arrival-departure sequence, which can lead to capacity maximisation. Third, the weather conditions at an airport play a major role in determining the runway configuration which then has a significant impact on its runway capacity. Typically, aircraft take-off and landing operations use the runways which are most closely aligned with the wind direction, speed and other factors (e.g., cloud ceiling, visibility). However, selecting a runway configuration is a challenging task because it requires not only wind/weather (current and predicted) conditions but also the arrival/departure sequence (active and anticipated) at an airport. Predicting a suitable runway configuration under the operating conditions and a given traffic distribution may be useful for maximising the runway capacity. To achieve that, I first propose a Machine Learning (ML) model for predicting a suitable runway configuration given wind/weather and arrival/departure sequence. The simulation results demonstrate the accuracy of the prediction (98%) and usefulness of the ML techniques for assisting Air Traffic Controllers (ATCs) to choose certain runway configuration based on real-world weather data. In the final part of this thesis, I extend the ML model for forecasting runway configuration and develop a capacity estimation model for estimating associated capacity in a medium-term horizon (6 hours). The proposed model incorporates influencing factors (i.e., wind, visibility, cloud ceiling, and operation time) as well as all possible runway layouts for predicting the most suitable configuration. As a case study for Amsterdam Schiphol Airport, one month of airport weather data and associated runway configurations are processed to train and test the developed ML model. The results, on two days of sample traffic data, demonstrate the prediction accuracy of the ML model of up to 98%. Also, it is demonstrated that the ML predicted configuration can accommodate an additional number of flights (i.e., up to 20 flights) within one hour. This shows the viability and benefits of using ML approach for maximising runway capacity

3D-in-2D Displays for ATC.

This paper reports on the efforts and accomplishments of the 3D-in-2D Displays for ATC project at the end of Year 1. We describe the invention of 10 novel 3D/2D visualisations that were mostly implemented in the Augmented Reality ARToolkit. These prototype implementations of visualisation and interaction elements can be viewed on the accompanying video. We have identified six candidate design concepts which we will further research and develop. These designs correspond with the early feasibility studies stage of maturity as defined by the NASA Technology Readiness Level framework. We developed the Combination Display Framework from a review of the literature, and used it for analysing display designs in terms of display technique used and how they are combined. The insights we gained from this framework then guided our inventions and the human-centered innovation process we use to iteratively invent. Our designs are based on an understanding of user work practices. We also developed a simple ATC simulator that we used for rapid experimentation and evaluation of design ideas. We expect that if this project continues, the effort in Year 2 and 3 will be focus on maturing the concepts and employment in a operational laboratory settings

Safety‐oriented discrete event model for airport A‐SMGCS reliability assessment

A detailed analysis of State of the Art Technologies and Procedures into Airport Advanced-Surface Movement Guidance and Control Systems has been provided in this thesis, together with the review ofStatistical Monte Carlo Analysis, Reliability Assessment and Petri Nets theories. This practical and theoretical background has lead the author to the conclusion that there is a lack of linkage in between these fields. At the same of time the rapid increasing of Air Traffic all over the world, has brought in evidence the urgent need of practical instruments able to identify and quantify the risks connected with Aircraft operations on the ground, since the Airport has shown to be the actual ‘bottle neck’ of the entire Air Transport System. Therefore, the only winning approach to such a critical matter has to be multi-disciplinary, sewing together apparently different subjects, coming from the most disparate areas of interest and trying to fulfil the gap. The result of this thesis work has come to a start towards the end, when a Timed Coloured Petri Net (TCPN) model of a ‘sample’ Airport A-SMGCS has been developed, that is capable of taking into account different orders of questions arisen during these recent years and tries to give them some good answers. The A-SMGCS Airport model is, in the end, a parametric tool relying on Discrete Event System theory, able to perform a Reliability Analysis of the system itself, that: • uses a Monte Carlo Analysis applied to a Timed Coloured Petri Net, whose purpose is to evaluate the Safety Level of Surface Movements along an Airport • lets the user to analyse the impact of Procedures and Reliability Indexes of Systems such as Surface Movement Radars, Automatic Dependent Surveillance-Broadcast, Airport Lighting Systems, Microwave Sensors, and so on… onto the Safety Level of Airport Aircraft Transport System • not only is a valid instrument in the Design Phase, but it is useful also into the Certifying Activities an in monitoring the Safety Level of the above mentioned System with respect to changes to Technologies and different Procedures.This TCPN model has been verified against qualitative engineering expectations by using simulation experiments and occupancy time schedules generated a priori. Simulation times are good, and since the model has been written into Simulink/Stateflow programming language, it can be compiled to run real-time in C language (Real-time workshop and Stateflow Coder), thus relying on portable code, able to run virtually on any platform, giving even better performances in terms of execution time. One of the most interesting applications of this work is the estimate, for an Airport, of the kind of A-SMGCS level of implementation needed (Technical/Economical convenience evaluation). As a matter of fact, starting from the Traffic Volume and choosing the kind of Ground Equipment to be installed, one can make predictions about the Safety Level of the System: if the value is compliant with the TLS required by ICAO, the A-SMGCS level of Implementation is sufficiently adequate. Nevertheless, even if the Level of Safety has been satisfied, some delays due to reduced or simplified performances (even if Safety is compliant) of some of the equipment (e.g. with reference to False Alarm Rates) can lead to previously unexpected economical consequences, thus requiring more accurate systems to be installed, in order to meet also Airport economical constraints. Work in progress includes the analysis of the effect of weather conditions and re-sequencing of a given schedule. The effect of re-sequencing a given schedule is not yet enough realistic since the model does not apply inter arrival and departure separations. However, the model might show some effect on different sequences based on runway occupancy times. A further developed model containing wake turbulence separation conditions would be more sensitive for this case. Hence, further work will be directed towards: • The development of On-Line Re-Scheduling based on the available actual runway/taxiway configuration and weather conditions. • The Engineering Safety Assessment of some small Italian Airport A-SMGCSs (Model validation with real data). • The application of Stochastic Differential Equations systems in order to evaluate the collision risk on the ground inside the Place alone on the Petri Net, in the event of a Short Term Conflict Alert (STCA), by adopting Reich Collision Risk Model. • Optimal Air Traffic Control Algorithms Synthesis (Adaptive look-ahead Optimization), by Dynamically Timed Coloured Petri Nets, together with the implementation of Error-Recovery Strategies and Diagnosis Functions

Automatic Flight Control Systems

The history of flight control is inseparably linked to the history of aviation itself. Since the early days, the concept of automatic flight control systems has evolved from mechanical control systems to highly advanced automatic fly-by-wire flight control systems which can be found nowadays in military jets and civil airliners. Even today, many research efforts are made for the further development of these flight control systems in various aspects. Recent new developments in this field focus on a wealth of different aspects. This book focuses on a selection of key research areas, such as inertial navigation, control of unmanned aircraft and helicopters, trajectory control of an unmanned space re-entry vehicle, aeroservoelastic control, adaptive flight control, and fault tolerant flight control. This book consists of two major sections. The first section focuses on a literature review and some recent theoretical developments in flight control systems. The second section discusses some concepts of adaptive and fault-tolerant flight control systems. Each technique discussed in this book is illustrated by a relevant example

Capacity assessment of railway infrastructure: Tools, methodologies and policy relevance in the EU context

The transport sector is increasingly faced with several issues related to the rising of traffic demand such as congestion, energy consumption, noise, pollution, safety, etc.. Due to its low external and environmental costs, railway can be considered (together with inland waterways and short-sea-shipping) as a key factor for the sustainable development of a more competitive and resource-efficient transport system (European Commission, White Paper 2011). In order to reinforce the role of rail in European transport , there is a strong need of addressing the efficiency of the system and customers' satisfaction through targeted actions, i.e. rising reliability and quality of service. This becomes particularly pressing as many parts of the existing railway infrastructures are reaching their maximum capacity thus shrinking their capability to provide users and customers a higher or even adequate level of service. Taking also into account that transport demand forecasts for 2030 clearly show a marked increase of rail activity across the whole Europe, we aim to address the issue of rail congestion in the context of relevant policy questions: Is the actual rail Infrastructure really able to absorb forecasted traffic, without significant impacts on punctuality of the system? Would the already planned interventions on the European railway infrastructure guarantee an adequate available capacity and consequently adequate reliability and level of service? To which extent would the coveted competition in an open railway market be influenced by capacity scarcity, mainly during peak hours or along more profitable corridors? An accurate estimation of capacity of the rail network can help answer these questions, leading policy makers to better decisions and helping to minimize costs for users. In this context this report explores the issue of capacity scarcity and sets this issue in the context of other relevant policy issues (track access charges, cost/benefit and accessibility measures, maintenance programmes, freight services’ reliability, external, marginal congestion or scarcity cost for rail, impacts of climate changes, etc.), providing a methodological review of capacity and punctuality assessment procedures. To better explore the real applicability and the time and/or data constraints of each methodology, the study reports some practical applications to the European railway network. Finally in the last section the report discusses the topic from a modelling perspective, as the quantitative estimation of railway capacity constraints is a key issue in order to provide better support to transport policies at EU level.JRC.J.1-Economics of Climate Change, Energy and Transpor

Collaborative decision making in complex work settings: a process of managing inter dependencies

There exists disparity between the conceptualization and occurrence of Collaborative Decision Making (CDM) in everyday work activities of complex work settings. Current notions in the field of Computer Supported Cooperative Work (CSCW) based on studies of decision making in groups typically portray CDM as an isolated event in which multiple personnel jointly undertake decision making. In the real world, however, decisions are made during work performance and interlaced with other processes and activities. Moreover, the complex work setting is a cooperative arrangement in which decision making is distributed. This research aims to alleviate the disparity by investigating how people in a complex working environment make decisions collaboratively. The original contribution to knowledge made by this thesis is the theory of CDM as a process of managing interdependencies. Field-studies conducted in an airport to examine the way CDM is undertaken during Air Traffic Control operations inform theory development. The study takes a qualitative approach and is guided by Grounded Theory Methodology (GTM). The findings of this research indicate that undertaking decision making in the cooperative arrangement of complex work settings requires managing the distributions and interconnections inherent in this setup. In addition, participation and contribution of personnel in decision making is found to be structured by the dependencies between their activities. These findings form the central focus of the theory leading to the depiction of CDM as a process of managing interdependencies. The theory presented in this thesis clarifies and extends existing views by explicating the differentiated process of CDM in the cooperative arrangement of a complex work setting. Based on this a new definition of CDM is formulated. In addition, a conceptual framework of ten parameters is derived to serve as a tool for analysing CDM taking place in a particular work setting. Application of this framework is demonstrated by analysing an aircraft accident report to draw insights about the occurrence of CDM in this setting