Improved Prediction of Runway Usage for Noise Forecast

Transcription

1 Delft University of Technology Improved Prediction of Runway Usage for Noise Forecast Development and comparison of runway usage models to improve accuracy of runway usage prediction for noise forecast Dakshina Dhanasekaran

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3 Improved Prediction of Runway Usage for Noise Forecast By Dakshina Dhansekaran in partial fulfilment of the requirements for the degree of Master of Science in Aerospace Engineering at the Delft University of Technology, to be defended publicly on Friday August 29, 2014 at 2:00 PM. Supervisor: ir. Paul Roling TU Delft Thesis committee: Dr. ir. Dries Visser TU Delft ir. Maarten Tielrooij TU Delft ir. Roel Hogenhuis NLR This thesis is confidential and cannot be made public until August 29, An electronic version of this thesis is available at iii

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5 Preface This report is a result of a graduation project to complete the Master of Science in Aerospace Engineering (Control and Operations: Air Transport Operations) from Delft University of Technology. The graduation project was conducted at the National Aerospace Laboratory of the Netherlands, NLR. In conducting this research, the advice and knowledge of many experts were essential. In particular, I would like to thank my supervisor at TU Delft Paul Roling and my external supervisors at NLR Roel Hogenhuis, Sander Heblij, and Annette Kruger-Dokter for their invaluable sharing of knowledge and continuous support and enthusiasm throughout the different stages of the research. I am grateful to my supervisors for the usefulness of the progress meetings and for always asking the right questions. I would also like to thank Dries Visser for being the chair of my thesis committee and providing critical insights and feedback which helped me to further refine the research. Special thanks to Joyce Nibourg for her insights into the research, ATEP department of NLR for giving me this internship opportunity and the employees who treated me as their own and guided me through this period. Last but not the least, I would like to thank my parents and my brother who have always made my dreams come true and been the pillar of support for my efforts to complete my Master s Degree in this two years. Dakshina Dhanasekaran Delft, August 2014 v

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7 Summary The research deals with improved prediction of runway usage for noise forecast. Since the accuracy of the noise forecast depends on the robustness of runway usage prediction, improved accuracy of runway usage prediction will result in improved accuracy of noise load prediction. The main motivation behind this research is that the current method for runway usage prediction does not account for certain factors such as anticipating changes in weather forecast, additional meteorological phenomena, and operational disturbances. These factors influence the controllers in the runway configuration selection decision-making process. The main objectives of the research are to develop runway usage models with increased accuracy of runway usage prediction compared to the current models and to investigate the effect of the developed models on the results of the computations of the noise load around the airport. The novelty of this research comes from improving the accuracy of runway usage prediction and noise forecast and identification of the main factors that influence runway usage. Most of the recent research in this area focuses on runway usage prediction for tactical and strategic planning. There has been very few research carried out on runway usage prediction for noise forecast and this research aims to fill that knowledge gap. Based on literature study, it was identified that modeling with the use of historical data (empirical modeling) can be used to predict runway usage more accurately since it includes the controller s decision-making patterns. Two prediction algorithms were chosen for the development of runway usage models: Nearest Neighbour and Neural Networks. Two approaches were chosen for runway usage prediction: determination of runway usage directly and determination of runway usage from runway combination prediction. The combination of the prediction algorithms along with approaches was used to develop four runway usage models. The main factors that influence runway usage were identified and used as predictors for the models. vii

8 The developed models were verified by a comparison with the actual runway usage. Various predictors were analysed to see if it improves the runway usage prediction accuracy. The developed runway usage models were compared with each other in terms of noise forecast accuracy. Based on the effect of the developed runway usage models on the results of the computations of the noise load around the airport, the runway usage model that resulted in the highest noise forecast accuracy was identified to be the model developed using neural networks that determines runway usage from runway combination prediction (Model 4). Due to the chosen methodology, the prediction of runway usage is very fast. The main factors that influence runway usage were identified to be wind direction, wind speed, visibility, period of the day, required capacity, type of operation (landing/take-off), and origin/destination. The developed runway usage models have been validated for Schiphol airport and can be applied for other complex multi-runway airports like Schiphol airport. This will aid in noise load prediction around the airport for transparency with surrounding communities, determining annual usage plan and analysing noise mitigation measures. viii

9 Contents INTRODUCTION Problem Statement Research Objectives and Research Questions Scope and Relevance Research Methodology Document Structure... 6 BACKGROUND INFORMATION Runway Usage Prediction Methods Conventional Method Wind Rose Time Stamped Runway Usage Prediction Compass Rose (Current Method at Schiphol airport) Modelling with the Use of Historic Data Runway Usage at Schiphol Airport Runway Configuration Preferential Runway Selection Factors Influencing Runway Selection...11 THEORETICAL CONTENT Statistical Models Nearest Neighbour Neural Networks Neuron Feedforward Neural Networks Transfer Functions Feedforward Backpropagation Steps to Create Neural Network...23 METHODOLOGY Nearest Neighbour Model Model Neural Networks Model Model IMPLEMENTATION OF RUNWAY USAGE MODELS Nearest Neighbour: Model Runway Use Database Search Algorithm...39 ix

10 5.2. Nearest Neighbour: Model Runway Use Database Search Algorithm Neural Networks: Model Dataset Predictors Target Classes Network Architecture Neural Networks: Model Dataset Predictors Target Classes Network Architecture...47 RESULTS Nearest Neighbour: Model Comparison by Year of Use Comparison by Month Nearest Neighbour: Model Effect on Model 2 with Probabilistic Runway Assignment Neural Networks: Model Comparison By Year of Use Comparison By Month Neural Networks: Model Comparison of Runway Usage Models in terms of Runway Usage Prediction Accuracy Analysis of the Models Model Application Varying Predictor Class Sizes and Inclusion of Additional Predictors Varying Time Period for Peak Period Determination Inclusion of Maintenance Periods Importance of Predictors (Sensitivity Analysis)...58 COMPUTATIONS OF NOISE LOAD AROUND THE AIRPORT Airport Noise Modelling Determination of Other Parameters for Noise Computations Route Assignment Procedure Class Noise Computation Results...64 CONCLUSION AND RECOMMENDATIONS Conclusion Recommendations...71 REFERENCES APPENDIX x

11 A. Runway Usage Prediction Methods for Strategic and Tactical Planning...76 B. Schiphol airport Charts...80 C. Wind Conditions that Resulted in No Search Results by Models 1 and xi

12 List of Abbreviations AIP ATC BAG BAS BZO CDM CDO CGB ECAC EEA FAA FAST FFBP IAF ICAO ILS KNMI LVC MIT MIP MILP MSQ NASA NLR NPD PNN PRAS RAAS SCG SEL SPIRIT RA RVR SAE SNAP SMS SVM TNIP UDP : Aeronautical Information Publication : Air Traffic Control : Basisregistraties Adressen en Gebouwen : Bewoners Aanspreekpunt Schipol : Beperkt Zicht Operaties : Collaborative Decision Making : Continuous Descent Operations : Conjugate Gradient Backpropagation : European Civil Aviation Conference : European Environment Agency : Federal Aviation Authority : Final Approach Spacing Tool : Feedforward Backpropagation : Initial Approach Fix : International Civil Aviation Organization : Instrument Landing System : Koninklijk Nederlands Meteorologisch Instituut : Low Visibility Conditions : Massachusetts Institute of Technology : Mixed Integer Programming : Mixed Integer Linear Programming : Mean Square Error : National Aeronautics and Space Administration : Nationaal Lucht- en Ruimtevaartlaboratorium : Noise Power Distance : Probabilistic Neural Networks : Preferential Runway Advisory System : Runway Allocation Advice System : Scaled Conjugate Gradient : Sound Exposure Level : System for Probabilistic Interactive Runway Indicator : Runway Assignment : Runway Visual Range : Society of Automotive Engineers : Strategic Noise Allocation Planning : Surface Management System : Support Vector Machines : Transparent Noise Information Package : Uniform Daylight Period xii

13 1 Introduction In order to have transparency with the surrounding communities of an airport, to determine the annual usage plan and to analyze the noise mitigation measures, the prediction of annual noise load around the airport is required. To be able to make a good estimate of the future noise load, the distribution of arriving and departing aircraft over various runways of an airport should be known Problem Statement One method that is currently used to predict the runway usage is based on certain runway allocation rules depending upon weather conditions and other factors that influence runway usage in a particular airport. It uses strict limits set by wind for runway combination. Runway combination is the subset of runways at an airport that are used (active) for arrivals and departures at any time (Ramanujam & Balakrishnan, 2011). Certain factors, such as the following, that may affect the runway combination and result in a different runway combination are not considered: Additional meteorological phenomena (e.g. showers in the vicinity of the airport, wind at high altitude in the approach path of a runway) Anticipating predicted weather changes (e.g. change in wind direction) Operational Disturbances (e.g. planned and unplanned maintenance, birds, accidents, disaster/disturbance in the airport vicinity) The above mentioned factors ensure that the strict boundary between two runway combinations in practice is less strict than that is assumed in the current method. Due to these factors, in practice, the air traffic controller may not always choose the runway combination predicted by this method. So, there is a level of uncertainty in this method. Hence, there is a need for improved prediction of runway allocation. The goal of this research deals with this challenge Research Objectives and Research Questions The main objectives of the research are to develop runway usage models with increased accuracy of runway usage prediction compared to the current models and to investigate the effect of the developed models on the results of the computations of the noise load around the airport. The following are the research questions to be solved How to develop runway usage models with increased accuracy of runway usage prediction? 2

14 What is the effect of the developed runway usage models on the computations of noise load around the airport? The first research question can be broken down to the following sub-questions 1. What is the category of prediction methods that will be used to develop runway usage prediction models?[chapter 2] 2. What are the factors that influence runway usage? [Chapter 2] 3. What are the prediction algorithms that would increase the prediction accuracy of runway usage? [Chapter 3] 4. How can the identified factors and prediction algorithms be used to develop runway usage models? [Chapters 4 and 5] 5. How are the developed runway usage models verified? [Chapter 6] 6. Can the developed runway usage models improved further? [Chapter 6] The second research question can be broken down to the following sub-questions 7. How to compute noise load around the airport? [Chapter 7] 8. Based on the effect of the runway usage models on the computations of noise load around the airport, which runway usage model can be identified to improve noise forecast accuracy? [Chapter 7] The sub-objectives of this research are Design and implementation of runway usage models Verification of the models Identification/implementation of possible improvements Computation of noise load around the airport with the runway usage models and noise model Investigation of the effect of runway usage models on computations of noise load around the airport (validation of the runway usage models) Comparison of the developed runway usage models Identification of the runway usage model that results in highest noise forecast accuracy 1.3. Scope and Relevance Scope: Although runway usage modelling can be applied to any airport, there are only certain airports that are complex enough to have choices of runway combinations for a particular set of traffic and weather conditions. Hence, the scope of the research is limited to complex multi-runway airports such as Schiphol airport and Chicago O Hare airport. The research will be validated for Schiphol airport since it forms an ideal candidate for runway usage modelling due to its complex runway layout and adverse weather conditions subjected to sudden changes. Hence, the model will be customized for Schiphol airport. The scope of the research is also limited to the noise forecast application of the runway usage prediction. Academic Relevance: The primary findings will be the identification of the main factors that affect the runway usage and prediction algorithms that will improve the accuracy of the runway usage prediction and in turn improve the accuracy of noise forecast. Four runway usage models will be developed and compared with each other and with the current runway 3

15 usage to achieve this. Also, the parameters that influence noise load will be analysed to improve the noise forecast accuracy. The novelty of this research comes from improving the accuracies of runway usage prediction and noise forecast. Most of the recent research in this area focuses on runway usage prediction for tactical and strategic planning. There has been very few research carried out on runway usage prediction for noise forecast and this research aims to fill that knowledge gap. Practical Relevance: The runway usage models developed will aid in noise load prediction around the airport for transparency with surrounding communities, determining annual usage plan and analysing noise mitigation measures Research Methodology In order to help answering the research questions, the following research methodology was followed Literature Study: Literature study was carried out to determine the various runway usage prediction methods. From these runway usage prediction methods, the main factors which play a role in runway selection were identified and empirical probabilistic modeling (prediction method category) was identified to improve runway usage prediction accuracy since it includes the controller s decision making patterns. Data Pre-Processing: The traffic data and weather data were pre-processed to be used in the runway usage model. The data was also analysed to understand the runway usage at Schiphol airport. Statistical Modelling: The identified factors from literature study were used as predictors to build statistical models of runway usage using prediction algorithms namely Nearest Neighbor and Neural Networks. Verification: The developed runway usage models were verified by comparing it with the actual runway usage. The accuracy of the runway usage forecast was determined by how well the overall runway use can be predicted on an annual basis and not by actual hour-to-hour runway forecast. Data Analysis: Once the developed models were verified, data analysis was done to identify and implement possible improvements. Validation: The noise computations based on the developed runway usage models was used to compare with the noise computations based on the actual runway usage to validate the results of the developed models. Comparative Research/Exploratory Data Analysis: The developed runway usage models were compared with each other in terms of their effect on computations of noise load around the airport. Based on this, the runway usage model that resulted in the highest noise forecast accuracy was identified. The structure of the report is summarized in figure 1.1-4

16 Various Runway Usage Prediction Methods Identification of the Category of Prediction Methods Identification of Prediction Algorithms Identification of Factors Influencing Runway Usage Literature Study Implementation of Runway Usage Models Data Pre-Processing and Statistical Modeling Comparison with Actual Runway Usage Verification Method Identification and Implementation of Possible Improvements in the Developed Runway Usage Models Data Analysis Investigation of the Effect of the Developed models on Noise Computations Validation Method Comparison of the Developed Runway Usage Models Identification of Runway Usage Model Resulting in Highest Noise Forecast Accuracy Figure 1.1: Research Structure Comparative Research/ Exploratory Data Analysis 5

17 The first step was to identify the category of prediction methods and the various factors that influence runway usage from existing literature. Once the category of prediction methods that will be followed was identified, suitable prediction algorithms were chosen. Using the prediction algorithm and factos, runway usage models were designed and implemented. The developed runway usage models were verified by comparing with the actual runway usage. The developed runway usage models were improved by identifying and implementing possible improvements. Based on the effect of the developed runway usage models on computations of noise load around the airport, the runway usage model that resulted in the highest noise forecast accuracy was identified Document Structure Chapter 2 Brief background information on runway usage at Schiphol airport (runway configuration, preferential runway selection, factors influencing runway usage) and various existing runway usage prediction methods for noise forecast and strategic/tactical planning. Chapter 3 Theoretical content of the chosen prediction algorithms to build runway usage models Chapter 4 Methodology of the runway usage models developed Chapter 5 Implementation of the designed runway usage models Chapter 6 Verification results of the developed runway usage models by comparing with the actual runway usage and analysis of the models developed Chapter 7 Results of the noise load computations around the airport using the developed runway usage models. Comparison of the developed runway usage models and identification of runway usage model resulting in highest noise forecast accuracy Chapter 8 Recommendations and Conclusion 6

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19 2 Background Information This section discusses the various runway usage prediction methods and analyses them to identify the best category of prediction methods to be used for runway usage modelling. This section also gives background information on runway usage at Schiphol airport explaining the runway configuration, preferential runway system and factors that influence runway selection Runway Usage Prediction Methods The runway usage prediction is used for different applications Runway Usage Prediction for noise forecast Runway Usage Prediction for tactical and strategic planning Improved prediction for noise forecast can aid in environmental impact assessments, noise transparency with the communities around the airport, reduction of noise complaints, determination of annual usage plan and analysis of noise mitigation measures (Nibourg, et al., 2008). Improved prediction for tactical and strategic planning will aid in airport strategic planning, airport tactical planning, airline operational planning, airport capacity planning (Gilbo, 1993) (Hall, 1999), flight safety, management of noise load around the airport, efficient traffic flow and Air Traffic Control (ATC) technological developments like Collaborative Decision Making (CDM) (Gilbo & Howard, 2000) and Continuous Descent Operations (CDO) (Nibourg, et al., 2008). When there is an unplanned runway configuration change, it results in delays, additional fuel burn/local emissions and increase noise in the airport vicinity (Li & Clarke, 2010). Since this research focuses on runway usage prediction for noise forecast, the following section contains information only about runway usage prediction methods for noise forecast. For runway usage prediction methods for strategic and tactical planning, the reader is advised to refer to appendix A. The noise forecast is based on the expected traffic at the airport. In order to be able to make a good estimate of the future noise load, the distribution of arriving and departing aircraft over the various runways of the airport should be known since runway configuration change affects the noise load distribution around the airport. The accuracy of the noise load prediction depends on the robustness of runway usage prediction (Southgate & Sedgwick, 2004). This is because noise distribution is a derivative of the flight operations characteristics. The following are the various methods developed for runway usage prediction for noise forecast- 8

20 Conventional Method Wind Rose The conventional method for prediction of runway usage is through a wind rose. Wind rose is a graphical representation of the distribution of average wind speed and direction at an airport over a long period of time (10 40 years) (Southgate & Sedgwick, 2006). To find the percentage of time the runways are available, crosswind and tailwind limits are applied to the average wind data from wind rose. Based on the list of noise preferred runways, the runway allocation of the annual average day forecasted traffic is then done by applying the percentage of runway availability information to the forecasted traffic. For example, if the wind data makes it possible for 80% of noise preferred runways to be available, 80% of aircraft movements are allocated to those runways. The negative aspect of this method is that it does not match the variations of wind data with the variations of the aircraft movements, which changes at different times of the day and different seasons. A more sophisticated approach of this method is to apply the same method for an hourly traffic data and wind data. But, this would also be inaccurate as the prediction is done for an annual average day and temporal information about wind and aircraft movement patterns are lost. This method was used for the Sydney Airport third runway Environment Impact Statement (EIS) in 1990 and there was a huge discrepancy between the prediction and actual practice (Southgate & Sedgwick, 2006) Time Stamped Runway Usage Prediction In the late 1990s, the department of transport of the Australian Government developed a runway usage prediction based on time stamped data (future year(s) data) instead of average day data (Southgate & Firth, 2004).They developed a tool called TNIP runway allocator which is used for runway usage prediction and noise prediction. By using the fine resolution of wind data sets, the averaging process was avoided and this resulted in a movement by movement runway allocation method i.e. the runway allocation was done before averaging. Once the crosswind, tailwind and runway modes capacity constraints were applied, the wind data at the date and time of each aircraft movement was analyzed and each aircraft movements were allocated to a runway based on the hierarchy of allocation rules. This method proved to give expected noise exposure results (Southgate & Sedgwick, 2006) Compass Rose (Current Method at Schiphol airport) The compass rose is the current method followed in Schiphol airport for runway usage prediction. Based on the order of preferred use of runways and wind/visibility limits, runway combination is determined for expected future traffic and past weather conditions (historical weather years: year-to-year weather conditions). The number of flights in the schedule for every 20-minute period is used to determine the number of take-off and landing runways required for handling the traffic. Based on date and time, every flight from the schedule is associated with the meteorological data of the relevant meteorological year. The available runway combinations which are within the wind/visibility limits are arranged based on the runway preference order and the first runway combination in it is selected. The runways in the runway combination are allocated to the flights. If multiple take-off or landing runways are used, origin/destination is used to distribute the traffic (Frontier, 2006). The compass rose model uses strict limits set by wind for runway combination. Certain factors that may affect the runway combination and result in a different runway combination are not considered: Additional meteorological phenomena (e.g. showers in the vicinity of Schiphol airport, wind at high altitude in the approach path of a runway) 9

21 Anticipating predicted weather changes (e.g. change in wind direction) Operational Disturbances (e.g. planned and unplanned maintenance, birds, accidents, disaster/disturbance in the airport vicinity) The above mentioned factors ensure that the strict boundary between two runway combinations in practice is less strict than that is assumed in the compass rose method. Also, it chooses the first runway combination from the list of available runways arranged in terms of runway preference order Due to these factors, in practice, the controller may not always choose the first runway combination from the list of available runways arranged in terms of runway preference order as the compass rose method chooses. So, there is a level of uncertainty in compass rose method Modelling with the Use of Historic Data In a research carried out by MIT, a statistical model to characterize the runway configuration selection process was developed using empirical observations i.e. historical data was used to determine the runway selection pattern, and thus, predict the future runway configuration. A probabilistic model for runway configuration selection was calculated based on the available airport data for every 15 minutes (Ramanujam & Balakrishnan, 2011). The developed runway usage model identified the influence of various factors in the runway selection decision-making process. The model was tested for Newark (EWR) and LaGuardia (LGA) airports For the most frequently observed configurations, the probability of correct configuration choice was more than 0.9 (Ramanujam & Balakrishnan, 2011) Runway Usage at Schiphol Airport This section explains how the runway selection is done at Schiphol airport. The sub-sections contain information about the runway configurations, the preferential runway selection and the main factors that influence runway selection Runway Configuration As shown in figure 2.1, Schiphol airport has 6 runways. Figure 2.1: Runway Configuration with Runway Use Restrictions (Ummels, 2014) 10

22 Out of the 6 runways, Oostbaan (04/22) is smaller than the other runways and it is mainly used only for general aviation. However, in cases of strong south-western winds, runway 22 is sometimes used for landing larger aircraft. As shown in figure 2.1 and table 2.1, there are runway use restrictions for Polderbaan (18R/36L) and Aalsmeerbaan (18L/36R). Hence, 36R and 18R are not used take-offs and 18L and 36L are not used for landings. Due to the location of Schiphol airport being close to the sea, there are varying wind conditions throughout the year. The complex runway configuration at Schiphol airport results from the ability to carry out arrival and departure operations at almost all wind directions. Table 2.1: Runways at Schiphol airport Runway Name Orientation Length (m) Runway Use Restrictions Aalsmeerbaan 18L/36R 3400 Take-offs 36R and Landings 18L Zwanenburgbaan 18C/36C 3300 Polderbaan 18R/36L 3800 Take-offs 18R and Landings -36L Oostbaan 04/ Kaagbaan 06/ Buitenveldertbaan 09/ Preferential Runway Selection The active runways at Schiphol airport are selected by ATC according to a noise preference runway system. A noise preference runway system is the one that selects runways based on safety limits and noise regulations, which is used to meet the noise limits requirements. That is, if there is more than one runway configuration that meets the safety criteria, the one which is preferred in terms on noise load is used. The following principles are followed Safety, which is dependent on weather conditions, is always given first priority. If specially requested by pilot for safety reasons, a non-preferential runway use is permitted provided that the other air traffic conditions permit. Departure and arrival will generally take place on separate runways. The wind and visibility criteria are used to select runway combinations from the preference list. Aberration from a runway assignment for shorter taxi route is not permitted. The preference list for runway selection is determined by the Airport Authority in close cooperation with ATC. It is subjected to changes in any given period based on the noise load distribution left in the annual usage plan (Meerburg, et al., 2007) Factors Influencing Runway Selection When allocating a runway, several factors are taken into account which includes technical, social and environmental factors (safety, economy and environment). It is summarized in figure 2.2. Each of the factors is explained in this section. 11

23 Figure 2.2: Factors Influencing Runway Usage Wind The current and forecasted weather conditions play the most important role in runway combination selection. The crosswind and tailwind components for runway are found from the wind direction and wind speed. The runway is not used when the crosswind and tailwind limits are reached and noise abatement will not be a deciding factor in such a case (van Es & Karwal, 2001) (van Es, et al., 2001). According to ICAO regulations, the crosswind and tailwind (including gust) limits are 15 knots and 5 knots respectively (Ashford & Wright, 2011). These limits depend upon the condition of the runway (wet or dry), measured runway friction co-efficient and gust. The limit varies for each airport, but, ICAO regulations are followed (Kuikka, 2009). Table 2.2 shows the wind criteria for Schiphol airport 12

24 Table 2.2: Wind Criteria for Schiphol airport (Boeing Company, 2011) Visibility Conditions and ILS The two parameters to describe visibility are horizontal visibility and cloud base. Horizontal visibility is the maximum distance up to which an observer can see objects lying close to the horizontal plane. Cloud base is the height of the lowest layer of the cloud. Just as wind limits, when there are low visibility conditions (LVC), the runway that does not meet the visibility criteria will not be selected. Since ILS is required for certain visibility conditions, visibility conditions are frequently related to ILS. Runway Availability When there is runway check, friction test, runway maintenance, snow sweep, required equipment outage, the runway is not available for runway selection (Rosin, et al., 1999). Traffic Density (Demand) The traffic density and number of runways/runway capacity required to meet the demand varies during the day. As shown in figure 2.3., there are arrival and departure peaks. At Schiphol airport, aircraft arrive and depart in the inbound and outbound peak hours respectively (wave pattern) due its hub operations characteristic illustrated in figure 2.3. Figure 2.3: Wave Pattern at AAS (KLM, sd) Time periods are dived as inbound peak, outbound peak, inter-peak and off-peak. During inbound peak, 2 arrival runways and 1 departure runway are required and vice versa for outbound peak. When there is an inter-peak, 4 runways are required. During off-peak periods, only 1 arrival runway and 1 departure runway are used (Nibourg, et al., 2008). It is evident that traffic density is an important factor for runway configuration. Based on traffic density, it is made sure that the demand is met by the runway configuration capacity (Duarte, et al., 2010). Runway configuration capacity is the hourly rate of aircraft operations which may be reasonably expected to be accommodated by a single or a combination of runways under given meteorological conditions (Bazargan, et al., 2002). 13

25 Noise For all airports that use noise preferential runway system, noise is an important deciding criterion when safety limits are met. For a complex airport like Schiphol airport with several communities in the vicinity of the airport, there is a dire need of noise preferential runway system since it evades uneven distribution of noise to the surrounding communities (Kuiper, et al., 2011). If there is more than one runway configuration that meets the safety criteria, the one which is preferred in terms on noise load is used (Meerburg, et al., 2007). The noise preferred runway combination should meet the safety criteria and also have no adverse effects on the hourly capacity. There are also restrictions in the use of some runways at night. The runway use restrictions are shown in figure 2.4 Figure 2.4: Runway Use Restrictions (Bewoners Aanspreekpunt Schipol (BAS), 2013) Origin/Destination The runway usage is also determined by the direction of approach or exit sectors of the flight operation which are mainly dependent on the origin and destination of the arriving and departing flights respectively. Type of Operation The type of operation (take-off or landing) also plays a role in runway selection. For Schiphol airport, the most frequently used runways are (Boeing Company, 2011): As landing runway: 06, 18R, 36R, 18C, 36C, 27. As departure runway: 36L, 24, 36C, 18L, 18C, 09 Other Factors The following are the various other factors that influence a controller in runway usage selection which have not been taken into account in the current runway usage prediction method at Schiphol airport - Anticipating forecasted weather changes (e.g. change in wind direction) Additional meteorological phenomena e.g. snow, thunderstorms. Historical patterns of runway configuration decision Anticipation on the following peak period 14

26 Research Sub-Questions Answered: What is the category of prediction methods that will be used to develop runway usage prediction models? What are the factors that influence runway usage? Chapter Conclusion: The methods explained above indicate that there have been some methods in the past to predict runway usage, but, none of the existing methods except statistical modeling/probabilistic empirical modelling (use of historic/empirical data to develop a probabilistic model) account for certain factors such as anticipating changes in weather forecast that influence the controllers in the runway configuration selection decision-making process. Since empirical data is used, runway usage can be predicted more accurately as it includes the controller s decision-making pattern. From the various methods of runway configuration selection methods described above, the main factors which play a role in runway selection are identified as: wind direction, wind speed, visibility, traffic density, time, origin/destination and type of operation (landing or take-off). 15

27 3 Theoretical Content This chapter contains the theory of statistical classification and two most common prediction algorithms namely nearest neighbour and neural networks. Nearest neighbour prediction algorithms are simple to implement and is a good start for pattern recognition. Neural networks are very efficient for pattern recognition and modelling non-linear behaviour Statistical Models A statistical model describes how one or more random variables are related to one or more other variables stochastically. Mathematically, a statistical model can be represented as (Y, P), where Y is the set of possible observations and P is the set of possible probability distributions on Y (Wikle, 2014). Supervised learning, a machine learning technique which teaches the computer how to use historical data (known input and response data) to predict future data or recognize patterns, can be used to build a statistical model from known input and response data (Alpaydın, 2010). The algorithm uses a known dataset to build a model and makes predictions using that. The steps involved in supervised learning is shown below Figure 3.1: Step 1 - Supervised Learning 16

28 Figure 3.2: Step 2 - Supervised Learning Supervised learning consists of two types of prediction algorithms Classification and Regression. Classification is used for categorical response values (i.e. the data can be classified into classes) and regression is used for continuous-response values. Runway allocation is a classification problem. Classification deals with identifying the class a new observation belongs to among a set of classes, on the basis of a known training dataset containing observations and their corresponding classes. Each observation is represented as a set of quantifiable properties, known as predictor variables/predictors. The predictors can be categorical, ordinal, integervalued or real-valued variables. An algorithm that implements classification is known as a classifier or classification algorithm. The most common classification algorithms are Neural Networks, Decision Trees and Nearest Neighbors (Caruana & Niculescu-Mizil, 2006). Classifiers are categorized into probabilistic classifiers and non-probabilistic classifiers. Probabilistic classification is a prominent type of classification. Most classification algorithms give output as the best class for an observation while probabilistic algorithms give probability that an observation belongs to each of the possible classes as the output. Classifiers can also be divided into binary classifiers and multiclass classifiers. In binary classification, only two possible classes are present whereas in multiclass classification, several classes are present. Nearest Neighbour and Neural Networks will be explained in Section 3.1 and Section 3.2 respectively Nearest Neighbour Nearest neighbour classifiers are used for classifying observations, based on closest training examples available in the training dataset. It conducts a nearest neighbour search (NNS) for finding the closest or most similar points. The closeness can be expressed by distance function. Given a set X of n points, nearest neighbour search finds the closest points in X to another set of points Y or a query point. The nearest neighbour search technique are widely used for creating benchmarking rules. The simplicity of this technique makes it much more usable than other classification techniques. There are several variants of the nearest neighbour search 17

29 k-nearest neighbour Search It determines the top k nearest neighbours to a query point. It is usually used for classifying an observation based on the majority of its neighbours. Approximate Nearest Neighbour Search Sometimes, it is sufficient if an approximate nearest neighbour is identified. In these cases, an algorithm which provides least guarantee that the actual nearest neighbour will be identified in every case is implemented (Arya, et al., 1998). This can improve speed of classification or save memory needed for computation. In most cases, the algorithm will find the actual nearest neighbour, but this ability is mainly dependent on the dataset being used for query (Arya, et al., 1998). Fixed-Radius Nearest Neighbour Search For this search, all the points present within a fixed distance from a specific point in the Euclidian space are identified (Jon L, et al., 1977). In this, the query point is arbitrary while the distance is fixed. Fixed-Radius Nearest Neighbour Search is shown in figure 3.3., where A and B are the classes to which the query has to be classified to and the circle represents the fixed radius. Figure 3.3: Fixed-Radius Nearest Neighbour Search All Nearest Neighbours Search - When there is a need to find the nearest neighbour for many queries, a nearest neighbour search for each query could be done. But, the more efficient way would be an algorithm that makes uses the information redundancy between each of the query (Andoni, 2009). For example, when distance between point X to point Y is computed, the same distance can be used for distance between point Y to point X. i.e. the calculation can be reused for a different query and thus make the search more efficient Neural Networks Neural Networks are based on the biological nervous system and it can be used to solve a wide variety of complex problems. Neural Networks is useful especially for modeling nonlinear behavior (Diallo, 2012) Neuron A neuron is the fundamental building block for a neural network, which uses weights, biases and transfer functions to generate output for a given input. A neuron consists of three parts Weight: Weight is a scalar that is multiplied with a scalar input to get the weighted input. 18

30 Bias: Bias is also a scalar which is added to the weighted input to get the net input. Transfer function: The net input is fed to the transfer function to generate the output. Figure 3.4: Neuron with a Single Input The output of a single neuron can be expressed as Where, a = output f = transfer function w = weight p = input b = bias Feedforward Neural Networks The most commonly used type of neural networks for pattern recognition and prediction is feed-forward networks. Feed-forward networks are networks in which the inputs are connected one-way to output layers using weights, biases and transfer functions. An output layer is a layer of output neurons that generate output when the input is fed to it. A network consisting of a single output layer of neurons is called a single layer network. It is shown in figure 3.5., where P1 to P4 are inputs, W11 to W43 are weights, (WP1) to (WP3) are weighted inputs, n1 to n3 are net inputs and O1 to O3 are outputs. P1 W11 (WP1) + Bias n1 Transfer Function O1 W21 W12 P2 W31 W22 W41 W13 W32 (WP2) + Bias n2 Transfer Function O2 P3 W23 W42 W33 P4 W43 (WP3) + Bias n3 Transfer Function O3 Figure 3.5: Single-Layer Network (4 inputs, 3 neurons in Hidden Layer) A multi-layer feedforward network consists of more than one layer of neurons (output layer, one or more hidden layers). Hidden layer is a layer of neurons through which the input is fed 19

31 to before it passes through the output layer of neurons. The use of non-linear transfer function allows the network to map non-linear relationships between input and output and this makes multi-layer networks powerful. A multi-layer feed-forward network can be explained through the following example. Consider a network consisting of 4 inputs, 3 neurons and 2 target classes. The input of size 1 x 4 is multiplied by a hidden layer of weights of size 4 x 3 to get the weighted input. The weighted input of size 1 x 3 is then added with a hidden layer of biases of size 1 x 3 to get the net input. The net input is then fed to a transfer function and the output of the hidden layer is fed to an output layer of weights of size 3 x 2. The weighted input of the output layer of size 1 x 2 is added with the output layer of size 1 x 2. The net input of the output layer is fed to the output layer transfer function to output the target class probabilities of size 1 x 2. P1 W11 (WP1) + Bias n1 Transfer Function H1 W11 (WH1) + Bias N1 Transfer Function O1 W21 P2 W12 W31 W21 P3 W22 W41 W32 W23 (WP2) + Bias n2 Transfer Function H2 W31 W12 W42 W22 W33 P4 W43 (WP3) + Bias n3 Transfer Function H3 W32 (WH2) + Bias N2 Transfer Function INPUTS HIDDEN LAYER OUTPUT LAYER OUTPUTS Figure 3.6: Multi-Layer Neural Network (4 inputs, 3 neurons in Hidden Layer) A generalized two-layer feedforward network is shown in figure 3.7. Size: Num of inputs x Num of neurons O2 Input x Hidden Layer Weights Size: 1 x num of inputs + Hidden Layer Biases Size: 1 x Num of neurons Size: 1x Num of neurons Transfer function + Size: Num of neurons x Num of target classes Output Layer Weights + Hidden Layer Output Output Layer Biases Size: 1 x Num of target classes Figure 3.7: Multi-layer Network Transfer Function Output Size: 1 x Num of target classes 20

32 Transfer Functions Log-Sigmoid: The log-sigmoid transfer function, as shown in figure 3.8., is the most preferred transfer function used in the hidden layer of multilayer feedforward networks (Beale, et al., 2013). This is because it is required to calculate the derivatives of the transfer function to update the weights during training and it is easy to calculate the derivatives of the log-sigmoid transfer function and also because log-sigmoid is a non-linear transfer function and can map non-linear relationships. The log-sigmoid transfer function outputs between 0 and 1 as the net input varies from negative to positive infinity. Figure 3.8: Log Sigmoid Transfer Function (Beale, et al., 2013) The log-sigmoid is expressed as - Softmax: The softmax transfer function, as shown in figure 3.9., generates the output from the hidden layer s net input. It is used in the output layer of multi-layer classifiers. The softmax transfer function outputs in the range of 0 to 1. It is a generalization of the logistic function and it squashes a vector of real values into a vector of values in the range of 0 to 1. The softmax is expressed as Figure 3.9: Softmax Transfer Function (Beale, et al., 2013) When there is appropriate number of neurons in the hidden layer, a two-layer feedforward network with sigmoid hidden layer transfer function and softmax output layer transfer function can classify observations arbitrarily well. This is because multiple layers of 21

33 neurons with non-linear transfer function can map non-linear and linear relationships between input and output vectors. It is shown in figure Figure 3.10: Two-Layer Feedforward Network with Sigmoid and Softmax Transfer Functions (Beale, et al., 2013) Feedforward Backpropagation To train a feedforward network, the weights and biases are updated iteratively to minimize the error function. Error function is a function that defines the network s performance by comparing the obtained network output with the desired output. The most commonly used error function in a feedforward network is mean square error (MSE), which is the averaged squared error between the network outputs and desired outputs. The weights and biases that would minimize the error function are determined by the gradient of the error function. Backpropagation is a technique to determine the gradient by propagating through the network backwards and making computations. The simplest form of the backpropagation technique is adjusting the weights and biases in the direction in which the error function decreases the fastest i.e. the negative of the gradient. The backpropagation algorithm that uses the steepest gradient descent direction (direction of the negative gradient of the error function) to determine weights and biases is called gradient descent algorithm. An iteration of the backpropagation technique can be expressed as (Beale, et al., 2013)- = vector of current weights and biases = current gradient = learning rate A feedforward network that uses backpropagation learning algorithm during training is called feedforward backpropagation (FFBP) network. A FFBP can be a good start because, in theory, it can fit any non-liner relationship between input and output provided there is appropriate number of neurons. The backpropagation learning algorithms have certain training parameters that can be set to determine when the training should be stopped. For example, the training can be made to stop if the number of iterations exceeds a set value or if the error function drops below a set goal or if the magnitude of the gradient goes below a set value. The simplest backpropagation algorithm (gradient descent algorithm) cannot be applied to practical applications that require adjustment of several thousand weights because the convergence rate is very poor. Though the error function reduces the fastest in the negative of the gradient, it is not always in the direction that results in the fastest convergence. Hence, a more complex set of algorithms called conjugate gradient algorithms can be used 22

34 for such cases. In the conjugate gradient algorithms, for each iteration, a line search is carried out along the conjugate direction (direction which is orthogonal to the direction of line search in the previous iteration) to determine the step size (change in weight), which minimizes the error function along that line. The minimization is done along the conjugate direction so that the minimization done in the previous iteration is not spoiled. This produces faster convergence rate than gradient descent algorithms. The downside of the conjugate gradient algorithms is that it increases the complexity and memory requirements Steps to Create Neural Network The steps involved in creating a neural network are training, validation and testing (Legge, 2004). 1) In the training step, the weights and biases of the network are adjusted to minimize the error function using training dataset. 2) Validation: In the validation step, the network generalization is validated. This step is used to check if the increase in accuracy using training dataset can also be seen in a new dataset (validation dataset). If there are no changes in the validation dataset, the training is stopped since there is no improvement in the network generalization. 3) Testing: In the testing step, the network is tested on data other than training and validation data. Since the dataset is different from the training and validation dataset, the performance of the network can be measured. Research Sub-Questions Answered: What are the prediction algorithms that would increase the prediction accuracy of runway usage? Chapter Conclusion: The prediction algorithms that can be used for the development of runway usage models are Nearest Neighbour and Neural Networks. Nearest neighbour algorithm is simple to implement and a good start for pattern recognition. Neural Networks are efficient in modelling non-linear behavior. 23

35 4 Methodology This chapter describes the methodology of the runway usage models developed. Two prediction algorithms were chosen for the development of runway usage models: Nearest Neighbor and Neural Networks. Two approaches were chosen for runway usage prediction: determination of runway usage directly and determination of runway usage from runway combination prediction. The combination of the prediction algorithms along with approaches was used to develop four runway usage models as shown in figure 4.1. The main factors that influence runway usage were identified and used as predictors for the models. Figure 4.1: Runway Usage Models 4.1. Nearest Neighbour NLR provided the concept of a new methodology to predict runway use in the future (based on nearest neighbour prediction algorithm, which uses historic/empirical runway usage data. Two models based on nearest neighbour were developed. The methodology for both the models are explained below Model 1 This method first builds a runway usage database linking runway usage from a previous period to specific weather and traffic data. Weather data consists of wind direction, wind speed and visibility (good/bad). Traffic data consists of peak period, IAF/sector and type of operation (landing or take-off). For every weather and traffic data cluster, the percentages of the time the runways were actually used are stored in this database. The model then takes the percentage of runway usage information and links this to a flight schedule to generate 24

36 runway usage that can be used for noise computations. The method is explained in detail below - The first step to create the model is to build a runway usage database compiled from flight data, runway data and meteorological data for a reference period. The compilation of the database requires information on disturbances which have occurred in practice so that such periods can be disregarded in the database since runway usage is different for such periods. The following data is required to build the runway use database 1) Flight schedule data with the following information for each flight movement Type of Operation (landing or take-off) Date and time Origin/Destination Peak Period 2) Runway data Runway activity 3) Meteorological data with the following information per hour Wind direction Average wind speed Maximum wind speed/gusts Horizontal visibility Cloud base The database of runway usage is created as follows 1) Based on the historical weather data (different weather years), wind direction, wind speed and visibility are obtained. 2) Wind direction is then put to a wind direction class (for every 10 degrees) and wind speed is put into a wind speed class (for every 2 knots). The visibility is also put into a visibility class. There is good visibility when the horizontal visibility is greater than or equal to 5,000 meters and the cloud base greater than or equal to 1,000 feet, all other cases are classified as not a good visibility (marginal visibility or low visibility). 3) The flight schedules for the same time period is taken to obtain origin/destination, type of operation (take-off or landing). 4) The origin and destination of the flight schedules is used to determine directions of approach or exit sectors of the flight operations. 5) The peak period is determined from the runway activity by using time clusters. A time cluster is a period of time during which there is take-off or landing activity on a runway, with a maximum time between two take-offs or landings of 20 minutes. The peak period is linked with flight schedules based on date and time. 6) The historical weather data and flight schedules are also linked based on date and time so that weather conditions are linked to each flight movement. 7) To sum it up, as shown in figure 4.2., each observation of the database contains the following attributes/predictors: wind direction, wind speed, visibility, sector/iaf, type of operation, peak period and runway used. 25

37 Figure 4.2: Model 1 Generation of Predictors for Database and Query Once the database is compiled, the following steps are followed to determine the runway use 1) As shown in figure 4.2, the query dataset is generated containing the same predictors as the database. The wind direction, wind speed, visibility, sector/iaf and type of operation are obtained from the flight schedules and past weather data 2) The number of take-off and landing runways required for handling the traffic is determined based on the number of flights in the schedule for every 20-minute period and based on this peak period is determined. 3) For each aircraft movement in the query, the runway usage in the past for the same predictor values is searched in the database. 4) The distribution of flights over the runways is then done based on the percentages of runway used in the past. An aircraft movement is thus not necessarily placed on one runway entirely or correctly, but spread over several runways in a ratio derived from historic data. This is a relevant difference from the compass rose model, which always assumes the same runway under given circumstances. 5) No Results for query: When there are no results during double peak, landings in landings peak and take-offs in take-off peak are searched with other predictor values being the same. Or the database is searched with a lower wind speed (inward search the dial) up to 10 knots lower than the actual wind speed with other predictor values being the same. 6) If still no results are found, two options exist: 26

38 A. The traffic is scaled up to the total traffic volume, or B. The compass rose model can be applied for these flights. A simplified conceptual diagram of Model 1 is shown in figure 4.3. Figure 4.3: Model 1 - Conceptual Diagram The methodology of this model can be understood better from the following example shown in table 4.1. The first and second column of the table consists of the predictors and their corresponding values of an aircraft movement. These predictor values are searched in the runway database and the third column shows the percentage of runway use found in the database for the corresponding predictor values. This information is used to determine how the aircraft movement is distributed among the runways found from the database, which can be seen in the fourth column. Table 4.1: Model 1 - Example Predictors Predictor Values % of Runway Used from Runway Use Database Peak Period Departure peak Operation Take-off Type Runway 09: 38% Sector/IAF Sector 3 Wind 6 Direction Class Runway 36C: 62% Wind Speed 5 Class Visibility 1 Class Runway Use Prediction 0.38 of the aircraft movement at of the aircraft movement at 36C Model 2 The second model is very similar to the first model. The main difference is that it finds the runway combination from the historical runway use database and uses a runway assignment table to distribute among the runways when multiple arrival or departure runways are used, while the first model directly finds the runway used from the runway use database. Runway 27

39 assignment table consists of corresponding runways for every pair of runway combination and sector/iaf. A simplified conceptual diagram of Model 2 is shown in Figure 4.4. Figure 4.4: Model 2 - Conceptual Diagram Figure 4.5: Model 2 - Generation of Predictors for Database and Query As shown in figure 4.5., an additional predictor called UDP (Uniform Daylight Period) based on sunrise and sunset timings is introduced in model 2. The predictor UDP has two classes. If the aircraft movement is within the uniform daylight period, it is put into the class UDP. If the aircraft movement is not within the uniform daylight period, it is put into the class Not UDP. In model 2, the number of classes of visibility is increased from two to three and the wind direction class has a bin size of 1 knot instead of 2 knots. This is done so that the predictor 28

40 values are more precise and this provides more statistical information for pattern recognition. The main differences between the two models are pointed out in table 4.2. Table 4.2: Differences between the Two Runway Usage Models Developed Database Search Model 1 Model 2 Predictors: Predictors: Peak Period, Peak Period, Sector/IAF, Wind Operation Direction, type, Wind Wind Speed, Direction, Visibility, Wind Speed, UDP Visibility Categorical Responses: Runways 2 classes of visibility Wind Speed class for every 2 knots Determines runways directly Categorical Responses: Runway Combinations 3 classes of visibility Wind Speed class for every 1 knot Determines runways from runway combination 4.2. Neural Networks Neural networks can learn, and therefore can be trained to recognize patterns and classify data. Hence, it can be used to predict runway usage from historical data of runway usage in the past years. Among the various prediction algorithms, Neural Networks was chosen because it is useful especially for modeling non-linear behavior and it is suitable for classification problems (Diallo, 2012). Based on literature study in Chapter 2, the predictors for the neural network were selected by identifying the main factors that influence runway usage. Two models based on neural networks were developed and the methodologies for both the models are explained in Section and Section Model 3 This model is similar to Model 1, except for the prediction algorithm it uses. A simplified conceptual diagram of this model is shown in figure

41 Figure 4.6: Model 3 - Conceptual Diagram The first step of the model is to build a dataset compiled from flight data, runway data and meteorological data for a reference period. The compilation of the dataset requires information on disturbances which have occurred in practice so that such periods can be disregarded in the dataset since runway usage is different for such periods. The dataset is created as follows: 1) Based on the historical weather data (different weather years), wind direction, wind speed and visibility are obtained. 2) Unlike Model 1 and Model 2, the wind direction and wind speed are used as it is instead of forming wind direction and wind speed classes, which is more precise. The visibility is put into a visibility class containing 3 classifications good/marginal/bzo. 3) The historical traffic data for the same time period is taken from which origin/destination, type of operation (take-off or landing) and peak periods are obtained. 4) The origin and destination of the traffic data are used to determine directions of approach or exit sectors of the flight operations. 5) The peak period is determined from the runway activity by using time clusters. A time cluster is a period of time that there is take-off or landing activity on a runway, the maximum time between two take-offs or two landings being 20 minutes. The peak period is linked with the traffic data based on date and time. 6) Based on the time of the aircraft movement, it is determined whether the movement belongs to Day, Evening or Night Period (DEN). Day 7.00 to Evening to Night to ) Summarizing, as shown in figure 4.7, the following attributes/predictors are required to build the dataset wind direction, wind speed, visibility, sector/iaf, type of operation, peak period, DEN period and runway used. 30

42 Figure 4.7: Model 3 - Generation of Predictors for Training Dataset and Query The built dataset is then separated into training, validation and testing dataset. The training dataset contains 70% of the observations, validation dataset contains 15% of the observations and testing dataset contains 15% of the dataset. The next step is to create the network with the appropriate number of neurons and layers. Since runway assignment is a classification problem, a transfer function that limits the output value between 0 and 1 is selected. A two-layer feed-forward network, with sigmoid hidden and softmax output neurons can classify vectors arbitrarily well, given enough neurons in its hidden layer (Beale, et al., 2013). Once the network is created and configured, the initial values of weights and biases are set. The next step is to train the network using scaled conjugate gradient method, a type of conjugate gradient method. The problem with most conjugate gradient methods is that the calculation complexity per learning iteration gets increased due to the line-search that has to be performed to determine an appropriate step-size for weight adjustment. Hence, for this research, a variation of conjugate gradient method called Scaled Conjugate Gradient (SCG) is used, which avoids the line-search per learning iteration. To read more about the SCG algorithm, the reader is advised to refer to Moller s A Scaled Conjugate Algorithm for Fast Supervised Learning (Moller, 1993). Once the network is trained and validated, it is tested 31

43 using the testing dataset. During the testing stage, the network stops to learn and if improved accuracy is required, the network has to be trained again. Once the testing stage shows that the accuracy is sufficient, the following steps are followed to determine the runway use 1) As shown in figure 4.7., the query dataset is generated containing the same predictors as the training dataset. The wind direction, wind speed, visibility, sector/iaf, type of operation and DEN period are obtained from the flight schedules and past weather data. 2) The number of take-off and landing runways required for handling the traffic is determined based on the number of flights in the schedule for every 20-minute period and based on this peak period is determined. 3) When the query dataset is fed through the neural network, the network assigns each aircraft movement to one or more runways and outputs them along with the probabilities. A flight is thus not necessarily placed on one runway entirely or correctly, but spread over several runways in a ratio derived from historical data Model 4 Model 4 is very similar to Model 3. The first difference is the predictors used: peak period, wind direction, wind speed, visibility and UDP. The second difference is that the neural network outputs the runway combination and uses origin/destination to distribute among the runways when multiple arrival or departure runways are used, while in Model 3, the network directly outputs the runway. A simplified conceptual diagram of this model is shown in figure 4.8. Figure 4.8: Model 4 - Conceptual Diagram 32

44 Research Sub-question Answered: How can the identified factors and prediction algorithms be used to develop runway usage models? Chapter Conclusion: The methodologies for the implementation of the runway usage models were developed based on the identified predictors and prediction algorithms. The predictors used by model 1 are peak period, sector/iaf, operation type, wind direction, wind speed, visibility. Model 2 uses an additional predictor UDP. The predictors used by model 3 and model 4 are peak period, sector/iaf, operation type, wind direction, wind speed, visibility and DEN. Model 1 and Model 2 are based on nearest neighbour while Model 3 and Model 4 are based on neural networks. Model 1 and Model 3 determine runway usage directly while Model 2 and Model 4 determine runway usage from runway combination prediction. 33

45 5 Implementation of Runway Usage Models This chapter gives a detail account on the implementation of the four runway usage models Nearest Neighbour: Model 1 The steps carried out to implement this model are explained in the following sections Runway Use Database The first step of this model was to build a runway usage database compiled from flight data, runway data and meteorological data for a reference period. The reference period was chosen to be one year because the reference period should be a proper representative of all the weather conditions that occur throughout the year and the operational year 2012 (November 2011 to October 2012) was chosen due to data availability of runway maintenance periods in Schiphol airport. Since the runway usage is different during maintenance periods, the compilation of the database requires information on disturbances which have occurred in practice so that such periods can be disregarded in the database. Table 5.1 shows the periods that were disregarded in the database due to regular maintenance Table 5.1: Maintenance Periods Period Runway Unavailable March 2012 Kaagbaan May 2012 Polderbaan 4-10 June 2012 Zwanenburgbaan 3-23 September 2012 Aalsmeerbaan The following aircraft movements were disregarded Aircraft movements during periods of maintenance General aviation and helicopter movements Aircraft movements with both origin and destination within Netherlands Aircraft movements with missing data The data required to build the runway use database are 1) Flight Data Type of Operation (landing or take-off) Date and time Origin/Destination 34

46 Aircraft Type Runway Activity (Runway Combination Used) 2) Meteorological Data Wind direction Average Wind Speed Maximum wind speeds/gusts Horizontal visibility Cloud base From the flight and meteorological data, 6 predictors were obtained peak period, sector/iaf, operation type, wind direction, wind speed and visibility. The predictors from flight data (Peak Period, Sector/IAF, and Operation Type) were obtained by pre-processing the flight data and the predictors from meteorological data (Wind direction, Wind Speed and Visibility) were obtained by pre-processing the hour-to-hour weather data recorded by KNMI (KNMI, 2012). The method used to obtain each of these predictors are explained below Peak Period Information about the use of runways is required in order to determine what the peak period has been during the time of flight. The peak period was determined from the runway combination at the corresponding five minute period, which was obtained from the runway activity. Figure 5.1: Determination of Peak Period The runway combination was derived from runway activity based on time clusters. The methodology followed is affiliated with the methodology applied for the new experimental noise enforcement system for Schiphol airport. The following steps were followed 1) The recorded time of take-off/landing was taken. 2) Time clusters per runway were defined. A time cluster is a period of time that there is take-off or landing activity on a runway, the maximum time between two takeoffs or two landings being 20 minutes. If the time between two take-offs or landings was more than 20 minutes, the last take-off or landing was put in a new cluster. 3) In a time cluster, the start and end time were defined. The start of the cluster was rounded down to 5 minutes and the end of the cluster was rounded up to 5 minutes. 4) The minimum length of a time cluster was set to be 10 minutes for all runways except runway 22 for which it was set to be 30 minutes. Hence, the time clusters with length shorter than 30 minutes for runway 22 and 10 minutes for other runways were disregarded. This is illustrated in figure

47 Figure5.2: Minimum Length of Time Clusters for Runways 5) For every 5 minute period, the active runways in that period were determined. A runway was considered to be active in a five minute period if it satisfied the following condition - 6) The runway combination was then derived by combining the active runways for every 5 minute period. Runway was not considered unless the only active runway within the five minute period was runway 22. This resulted in no runway combination for certain five minute periods when no active take-off or landing runways were found for those segments. 7) The next step was to determine peak period which is a derivative of the runway combination. The following was applied 2 landing runways + 1 take-off runway = Arrival Peak 1 landing runway + 2 take-off runways = Departure Peak 2 landing runways + 2 take-off runways = Double peak 1 landing runway + 1 take-off runway = Off-peak 23:00 to 6:00 pm local time = Night Certain uncommon runway combinations occured due to the way time clusters were defined in this method. The following was done for such cases 3 landing runways + 1 take-off runway = Arrival Peak 1 landing runway + 3 take-off runways = Departure Peak 36

48 Assumptions: Time clusters were defined such that the maximum time between two take-offs or two landings on a runway was 20 minutes and time clusters shorter than 10 minutes were ignored (30 minutes for runway 22). Hence, even if a particular runway was used, due to the above definition of time clusters, it may not be considered as an active runway. Runway was ignored in the runway combination unless runway 22 was the only active runway in the five minute period considered. Sector/IAF The flight patterns to and from Schiphol airport are mainly dependent on the origin and destination of the arriving and departing flights. Based on the origins of arriving aircraft, they are directed to one of the three approach points about 65 km from Schiphol airport. The three approach points are ARTIP, RIVER and SUGOL. Departing aircraft are directed to one of the five exit sectors based on their destinations. The five sectors are Sector 1(North), Sector 2(East/South-east), Sector 3(South), Sector 4 (West/South-West), and Sector 5 (North-West). They are shown in figure 5.3. Figure 5.3: Sectors and IAF of Schiphol airport (Bewoners Aanspreekpunt Schipol (BAS), 2013) Based on a look-up table that links origin/destination to STAR/SID routes, origin/destination of the aircraft movements were used to convert them to STAR/SID routes respectively. This was further translated into IAF/Sectors based on Standard arrival chart Instrument and Standard Departure chart Instrument respectively (Appendix B). 37

49 Figure 5.4: Determination of Sector/IAF Assumptions: The look-up table used that links origin/destination to STAR/SID routes is deterministic. Certain airports were not available in the airport table. For those cases, another airport in the same FIR region was used to link airport to STAR/SID routes. This assumption holds true especially for airports far away from Schiphol airport. Operation Type Each of the aircraft movements was designated as take-off or landing based on their type of operation. Wind Direction The Wind direction from the weather data was put into a wind direction class for every 10 degrees. Wind Speed If the maximum wind speed was higher than the average wind speed by 5 knots or more, the maximum wind speed was taken to be the wind speed. In all other cases, the average wind speed was taken to be wind speed. Then, the wind speed was put into a wind speed class (for every 2 knots). Assumptions: The bind size of each wind speed class is assumed to be 2 knots. For more precision, it can be decreased to 1 knot. But, the chance that a search result will be negative will be higher since the clusters in the database will be less filled. The maximum wind speed is taken when it is higher than average wind speed by 5 knots or more. The threshold of 5 knots is assumed and this value can be changed if it results in higher accuracy. Visibility The visibility was put into two classes (good or bad) based on the following condition Table 5.2: Visibility Classification Visibility Classification Horizontal Visibility Range Cloud base Range Good Horizontal visibility>=5000 m AND Cloud Base > = 1000 ft. Bad Horizontal visibility<5000 m OR Cloud Base<1000 ft. 38

50 The horizontal visibility was directly taken from the weather data. However, the cloud base was not directly available in the weather data. Since the cloud base can be estimated from surface measurements of air temperature and humidity by calculating lifted condensation level, cloud base was calculated using the formula below (Ahrens, et al., 2012)- Assumptions: The visibility has only 2 classes. For more precision, an additional classification can be added. But, the chance that a search result will be negative will be higher since the clusters in the database will be less filled. Linking predictors from traffic data with predictors from weather data The flight data was available in local time while the weather data was recorded in UTC time. Hence, the weather data was first converted to local time. The predictors obtained from weather data were linked to the predictors obtained from traffic data based on the date and time of the flight. Each aircraft movement was associated with its corresponding weather conditions at that hour Search Algorithm Once the database was compiled, the following steps were followed to determine the runway use The search period for the query was chosen to be the operational years 2012 (November 2011 to October 2012) and 2013 (November 2012 to October 2013). The following aircraft movements were removed from the query- Aircraft movements during periods of maintenance General aviation and helicopter movements Aircraft movements with both origin and destination within Netherlands Aircraft movements with missing data The periods of maintenance (regular maintenance) removed are shown in table 5.3. Table 5.3: Maintenance Periods and 2013 Period Runway Unavailable March 2012 Kaagbaan May 2012 Polderbaan 4 10 June 2012 Zwanenburgbaan 3 23 September 2012 Aalsmeerbaan April 2013 Oostbaan 20 2 June 2013 Kaagbaan June 2013 Zwanenburgbaan and Polderbaan 1 7 July 2013 Buitenveldertbaan 39

51 1) Pre-Processing: Before applying the search algorithm, the first step was to generate the predictors for query. The predictors were found from flight schedules and weather data of the meteorological year. 2) Once the predictors were generated for the query, for each aircraft movement in the query, the runway use database was searched for exact match of all predictors. 3) When the search yielded no results for movements that belonged to double peak, they were searched for arrival peak if they were arrival flights and departure peak if they were departure flights with other predictor values matching exactly. 4) When there were no results for flights that were not during double peak, the database was searched with a lower wind speed (inward search the dial) up to 10 knots lower than the actual wind speed with other predictors matching exactly. 5) If no results were found after the above mentioned method, the traffic was scaled up to the total traffic volume. 6) The distribution of flights over the runways was then done based on the percentages of runways used in the past. The flow chart for the search algorithm is shown figure

52 Figure 5.5: Search Algorithm - Model 1 41

53 5.2. Nearest Neighbour: Model Runway Use Database Similar to the runway use database of model 1, the following aircraft movements were disregarded from the database Aircraft movements during periods of maintenance General aviation and helicopter movements Aircraft movements with both origin and destination within Netherlands Aircraft movements with missing data The predictors used in this model are as follows Peak Period Wind Direction Wind Speed Visibility UDP The predictors mentioned above were obtained in the same way as obtained for Model 1. The predictors which are different in this model are explained below - Visibility The only difference in the predictor visibility of this model is that an additional classification was introduced. The conditions for the visibility classification below are based on the horizontal visibility and cloud base thresholds specified/defined by Schiphol (Wijngaard, et al., 2007). The horizontal visibility and cloud cover were directly obtained from the weather data recorded by KNMI (KNMI, 2012). The cloud base was calculated based on the formula mentioned in section Visibility Classification Good Marginal BZO Table 5.4: Visibility Classification Horizontal Visibility Range Horizontal Visibility >5000 m 5000 m > Horizontal Visibility >=1500 m Horizontal Visibility < 1500 m 5000 m > Horizontal Visibility >=1500 m Horizontal Visibility < 1500 m Horizontal Visibility < 1500 m Horizontal Visibility >5000 m Cloud base Range AND Cloud Base> = 1000 ft. OR 1000 ft. > Cloud Base > 300 ft. OR Cloud Base <=300 ft. AND Cloud Base <=300 ft. AND 1000 ft. > Cloud Base > 300 ft. AND Cloud Base> = 1000 ft. AND Cloud Base <=300 ft. 42

54 UDP The predictors UDP (uniform daylight period) has two classes. If the aircraft movement was within the uniform daylight period, it was put into the class UDP. If the aircraft movement was not within the uniform daylight period, it was put into the class Not UDP. The UDPs obtained from Aeronautical Information Publication (AIP) was converted to local time from UTC time so that it can be associated with the date and time of the flights Search Algorithm Once the database was compiled, the following steps were followed to determine the runway use 1) Pre-processing: The query period was chosen to be the same as Model 1 so that the results could be compared. Before search algorithm can be applied, the first step was to generate the predictors for query. The predictors were found from flight schedules and weather data of the meteorological year. 2) For each aircraft movement in the query, the runway use database was searched for exact match of all predictors. 3) When the search yielded no results for movements that belonged to double peak, they were searched for arrival peak if they were arrival flights and departure peak if they were departure flights with other predictors matching exactly. 4) When there were no results for flights that were not during double peak, the database was searched with a lower wind speed (inward search the dial) up to 10 knots lower than the actual wind speed with other predictors matching exactly. 5) If no results were found after the above mentioned method, the traffic was scaled up to the total traffic volume. 6) The distribution of flights over the runways was then done based on the percentages of runway combinations used in the past. A flight was necessarily allocated to one runway combination entirely or correctly, but spread over several runway combinations in a ratio derived from historic data. 7) Post-processing: Once the runway combinations were found, the runway allocation was determined using a runway assignment table that links runway combinations and Sector/IAF with runways. 43

55 Assumptions: Sometimes the search matched with certain observations in the database that had the following unusual runway combinations No Departure Runways/No Arrival Runways ; No Departure Runways/1 Arrival Runway ; 1 Departure Runway/No Arrival Runways. These observations occurred in the night period due to the infrequent flights in the night and the way time clusters were defined. The following was assumed for such cases: - Remove observations with No Departure Runways/No Arrival Runways - Remove observations with No Departure Runways/1 Arrival Runway for departure flights in the query - Remove observations with 1 Departure Runway/No Arrival Runway for arrival flights in the query The route assignment table used is deterministic. A probabilistic airport table generated from empirical data might reflect reality better. The runways for runway combinations that were not found in the route assignment table were generated empirically from the traffic data. Due to the way time clusters were defined, there were some runway combinations with 3 arrival runways or 3 departure runways different from the usual 2 runways during a peak. Most of these cases were transition runway combinations which were used while transitioning from one runway combination to another. Hence, the observations with such transition runway combinations were not included as the runway combinations before and after the transitions were already included. Apart from transition runway combination, a third runway was sometimes added to the runway combination to match the excessive demand. But, this additional third runway was active only for a short period of time and it could be excluded. To sum it up, all observations with three runways in the runway combination were not included during the search. The flow chart for the search algorithm is shown in figure

56 Figure 5.6: Model 2 - Search Algorithm 45

57 5.3. Neural Networks: Model 3 As explained in Section , the network was trained using scaled conjugate gradient method and the dataset used for training, validating and testing the network was built from the traffic and weather data from the operational year Dataset Predictors After several iterations, the predictors that were used are Wind Direction Wind Speed Visibility Class Peak Period Sector/IAF Type of Operation DEN (Day Evening Night Period) The predictors were obtained the same way the predictors were obtained in model 1 and model 2. In the models developed based on Nearest Neighbour (Model 1 and Model 2), the wind direction and wind speed was put in wind direction class and wind speed class respectively. But, this model uses the exact wind direction and wind speed value in the predictors. Using precise values of predictors provides more statistical information for pattern recognition than using categorical values of predictors. Since neural networks can work only with numeric values of predictors, predictors like peak period and sector/iaf were converted to categorical values Target Classes The target classes used are the runways. Hence, it contains 12 classes. For example, if runway 18R is the first class and if a movement belongs to 18R, the target vector for that movement is written as T = [ ] Network Architecture As shown in figure 5.7., the network created is a two-layer feed forward network with 7 input variables, 10 neurons forming a hidden layer and 12 output variables. The hidden layer consists of a sigmoid transfer function and the output layer consists of a softmax transfer function. Figure 5.7: Model 3 - Network Architecture The network directly outputs the runway and the corresponding probabilities for each aircraft movement. 46

58 5.4. Neural Networks: Model 4 In this model, the dataset targets are runway combinations and not runways like Model 3. Since there are many runway combinations and most of the departure runway combinations are different from arrival runway combinations, the neural network was designed separately for departures and arrivals. Both the networks are similar in every way, except for the target classes. The networks were trained using scaled conjugate gradient method and the dataset used for training was built from the traffic and weather data from the operational year Dataset Predictors After several iterations, the predictors that were used are Wind Direction Wind Speed Visibility Class Peak Period UDP (Uniform Daylight Period) Target Classes The target classes used are the runways combinations. Based on the traffic data from the operational year 2012, it was identified that 23 landing runway combinations were used and 23 take-off runway combinations were used. Hence, the dataset targets for both the neural networks created consist of 23 classes Network Architecture As shown in figure 5.8, the network architecture is a two-layer feed forward network with 5 input variables, 20 neurons forming a hidden layer and 23 output variables. The hidden layer consists of a sigmoid transfer function and the output layer consists of a softmax transfer function. Figure 5.8: Model 4 - Network Architecture The network outputs the runway combination and corresponding probabilities. Origin/destination was used to distribute among the runways when multiple arrival or departure runways were used. Chapter Conclusion: Four runway usage models were implemented. Two models were based on nearest neighbour and two models were based on neural networks. The two models based on nearest neighbour were implemented by creating a runway usage database and a search algorithm. The two models based on neural networks were implemented by training a neural network. 47

59 6 Results This chapter summarizes the results obtained for the four runway usage models developed. The predicted runway use was compared with the actual runway use for the following periods - Comparison by month Comparison by year The accuracy of the prediction of the runway use is expressed in terms of percentage and it is calculated separately for take-offs and landings. The percentage is an indication of the number of movements predicted correctly for a runway on a balance. The accuracy is calculated using the formula below Note: - It is multiplied by a factor of 0.5 in order to prevent the differences in the use of a runway to be double-counted. - The difference between the number of predicted movements and realized movements is calculated for each runway direction separately and then summed together. Assumptions: While calculating the realized number of movements per runway, general aviation helicopter movements, movements during periods of maintenance, movements with both origin and destination in Netherlands and movements with missing information were ignored. The above procedure was followed for the verification of all the runway usage models developed. 48

60 6.1. Nearest Neighbour: Model 1 The model 1 results were compared with the actual runway use using the formula mentioned in the previous page. Both yearly and monthly comparison was done for operational years 2012 and It is shown in table Comparison by Year of Use Table 6.1: Model 1 - Annual Comparison Year Take-off Accuracy Landing Accuracy % of flights with no search results % 98% 3% % 97% 2% Year 2012 showed higher accuracy than 2013 though the percentage of flights with no search results is more for This is because a scale factor was used to balance this. Note: For year 2012, since the database and query are the same, when a particular month in the query is searched in the database, that particular month is excluded from the database during the search. When this was not done, it resulted in 100% accuracy, which is not realistic and a useful check. The comparison per runway is depicted in figures 6.1 and 6.2. Figure 6.1: Model 1 - Annual Comparison (2012) 49

61 Comparison by Month Figure 6.2: Model 1 - Annual Comparison (Year 2013) Figure 6.3: Model 1 - Monthly Comparison (Year 2012) As shown in figure 6.3, the accuracy is lower in certain months of the operational year 2012 for the following reasons 50

62 November 2011 and December 2011 These months contain a lot of days with bad visibilty conditions and some days with snow. This resulted in no search results for many aircraft movements. September 2012 The length of the month is quite small because of the removal of periods that did not have normal runway operations (maintenance periods). Hence, even if there are only a few movements that did not yield any search results, it would have a large effect on the accuracy. Figure 6.4: Model 1 - Monthly Comparison (Year 2013) As shown in figure 6.4, the accuracy is lower in certain months of 2013 for the following reasons February, March and April 2013 These months contain a lot of days with strong easterly winds which is not very common. This resulted in no search results for a relatively large number of aircraft movements. June and July 2013 The length of these months are quite small because of the removal of periods that did not have normal runway operations (maintenance periods). Hence, even if there are only a few movements that did not yield any search results, it would have a large effect on the accuracy. Sub-conclusion: From the above results, it could be seen that the accuracy is higher when a longer period is considered. This shows that model 1 works better when a long period is considered. 51

63 6.2. Nearest Neighbour: Model 2 Model 2 also showed a higher accuracy when a longer period was considered. The model 2 results are shown in table 6.2. Table 6.2: Comparison of Model 1 and Model 2 Results Take-off Accuracy Landing Accuracy % of flights with no search results Year % 93.1% 8% Year % 93.1% 5% Sub-conclusion: The accuracy for model 2 is comparatively lower because of using a deterministic runway assignment table. A probabilistic runway assignment table would increase the accuracy of the model Effect on Model 2 with Probabilistic Runway Assignment A probabilistic runway assignment table was generated from the traffic data to reflect reality better. It was generated by finding the percentage distribution of runways for each runway combination and sector/iaf. This percentage distribution was used when a particular runway combination was found by the model to distribute the aircraft movement among the runways in the runway combination. Implementing this probabilistic runway assignment in model 2 improved the landing accuracy of model 2. This is because there is lot of variation in assigning landing runways from a landing runway combination and using a deterministic table for this is inaccurate. The comparison of model 2 with deterministic and empirical runway assignment is shown in table 6.3. Table 6.3: Comparison of Model 2 Results with Deterministic Runway Assignment and Probabilistic Runway Assignment Model 2 (Deterministic RA) Take-off Accuracy Model 2 (Probabilistic RA) Model 2 (Deterministic RA) Landing Accuracy Model 2 (Probabilistic RA) Year % 96.0% 93.1% 96.0% Year % 95.7% 93.1% 95.0% Sub-conclusion: The landing accuracy for model 2 increased with the use of probabilistic runway assignment. 52

64 6.3. Neural Networks: Model 3 The yearly and monthly comparison results are summarized in sections and Comparison By Year of Use Table 6.4: Model 3 - Annual Comparison Year Take-off Accuracy Landing Accuracy % of flights with no search results % 97.0% 0% % 98.2% 2% The comparison is depicted in figures 6.5 and 6.6. Figure 6.5: Model 3 - Annual Comparison (2012) 53

65 Figure 6.6: Model 3 - Annual Comparison (2013) Comparison By Month Figure 6.7: Model 3 - Monthly Comparison (2012) As shown in figure 6.7, the accuracy is lower in certain months of the operational year 2012 for the following reasons November 2011 and December 2011 These months contain a lot of days with bad visibilty conditions and some days with snow, which are not very common. Hence, the network was not trained very well for these coniditions. September 2012 The length of the month is quite small because of the removal of periods that did not have normal runway operations (maintenance periods). Since the number of aircraft movements for which the runway usage has to be predicted is low, the accuracy is low. 54

66 Figure 6.8: Model 3 - Monthly Comparison (2013) As shown in figure 6.4, the accuracy is lower in certain months of 2013 for the following reasons March and April 2013 These months contain a lot of days with strong easterly winds which is not very common. Hence, the network was not trained very well for these conditions. June and July 2013 The length of these months are quite small because of the removal of periods that did not have normal runway operations (maintenance periods). Since the number of aircraft movements for which the runway usage has to be predicted is low, the accuracy is low. Sub-conclusion: Like runway usage models created using nearest neighbour, Model 3 also has a higher accuracy when longer period is considered Neural Networks: Model 4 The annual comparison for Model 4 is shown in table : Model 4 Annual Comparison Year Take-off Accuracy Landing Accuracy % of flights with no search results % 97.0% 0% % 96.1% 2% Model 4 also showed a higher accuracy when longer period is considered. 55

67 6.5. Comparison of Runway Usage Models in terms of Runway Usage Prediction Accuracy The models are compared with each other in terms of runway usage accuracy and percentage of flights for which no search results were found by the models. Table 6.6: Comparison of the Developed Runway Usage Models in terms of Accuracy Take-off Accuracy Landing Accuracy Model 1 Model 2 Model 3 Model 4 Model 1 Model 2 Model 3 Model 4 Year 98.0% 96.0% 98.3% 97.1% 98.0% 96.0% 98.8% 96.2% 2012 Year 97.0% 95.7% 98.6% 97.2% 97.0% 95.0% 99.5% 96.1% 2013 Average 97.5% 95.8% 98.4% 97% 97.5% 95.5% 99.1% 96.0% Table 6.7: Comparison of the Developed Runway Usage Models in terms of Percentage of Movements with No Results % of flights with no results Model 1 Model 2 Model 3 Model 4 Year % 8.0% 0% 0% Year % 7.5% 0% 0% The wind conditions which resulted in no search results by Model 1 and Model 2 were analysed and the results are summarized in Appendix C Analysis of the Models Model Application Model 1 and Model 3 determine the runway usage directly while Model 2 and Model 4 determine the runway by determining the runway combination first. Determining the runway directly results in higher accuracy in terms of number of movements. This is because determining the runway through runway combination involves two predictions prediction of the runway combination and then, prediction of the runway based on that. But, predicting the runway directly might result in some unrealistic runway combinations. Runway combination plays an important role in determining other parameters that influence noise like the procedure class and the route. Hence, a more realistic runway combination determination is required if the runway usage prediction is going to be used for noise forecast. Since Model 3 and Model 4 predicts runway combination based on historical use of runway combinations, Model 2 and Model 4 would fit better for noise forecast than Model 1 and Model 3, which use predicted runways to determine runway combination. However, Model 1 and Model 3 would be better than Model 2 and Model 4 if the goal is just to predict the number of movements on each runway, which would be useful to 56

68 determine annual runway usage. Hence, the developed runway usage models can be chosen based on the application. Sub-Conclusion Comparing the developed models based on application (Model 1 vs Model 3 and Model 2 vs Model 4), the models developed using neural networks (Model 3 and Model 4) show a higher accuracy on average. The differences between all the runway usage models developed are summarized in table 6.8. Classification Algorithm Predictors Used Table 6.8: Differences between the Runway Usage Models Developed Model 1 Model 2 Model 3 Model 4 Nearest neighbour Nearest neighbour Neural networks Neural networks Wind direction Wind direction Wind direction Wind direction in class (for every class (for every (in º) (in º) 10º) 10º) Wind speed class Wind speed Wind speed (in Wind speed (in (for every 2 class (for every knots) knots) knots) 1 knot) Visibility class Visibility class Visibility class Visibility class (good/bad) (good/marginal/ (good/marginal/ (good/marginal/ bzo) bzo) bzo) Peak period Peak period Peak period Peak period Operation type (takeoff/landing) Operation type (takeoff/landing) Operation type (takeoff/landing) Sector/IAF - Sector/IAF - - UDP DEN UDP Target Class Runway Runway combination Suitable Prediction of Noise forecast Application number of movements on each runway Runway Prediction of number of movements on each runway - Runway combination Noise forecast Varying Predictor Class Sizes and Inclusion of Additional Predictors Reducing the wind speed class size resulted in the database being less filled and there were more flights which yielded no search results. Increasing the wind speed class size affected the runway usage accuracy since it made the predictor value less precise. Inclusion of additional predictors also resulted in the database being less filled and there were more flights for which the model yielded no search results. This was tested by 57

69 including snowfall as an additional predictor. Without snowfall predictor, the aircraft movements belonged to the same cluster irrespective of snowfall. Due to the inclusion of snowfall, more unique clusters were formed in the database and the chance that the search result yielded no results increased Varying Time Period for Peak Period Determination Peak Period was determined by using time clusters. Time clusters per runway were defined to be a period of time that there is take-off or landing activity on a runway, the maximum time between two take-offs or two landings being 20 minutes. If the time between two takeoffs or landings was more than 20 minutes, the last take-off or landing was put in a new cluster. This resulted in lot of observations in the night with no runway combination because of the way active runways were defined. Hence, during the night period, the 20 minutes limit was reduced so that the number of observations with no runway combination gets decreased. But, this resulted in a decrease in accuracy. The minimum length of a time cluster was set to be 10 minutes. Increasing or decreasing this value resulted in more transition runway combinations with 3 arrival runways or 3 departure runways. This is because even when a runway was used for a short period, it was considered to be active due to the time cluster length and this resulted in more runways to be considered active. This decreased the accuracy and hence, the best minimum time cluster length was found to be 10 minutes Inclusion of Maintenance Periods Runway usage prediction was done with the inclusion of maintenance periods and compared with the accuracy of runway usage prediction with the exclusion of maintenance periods. Contrary to what was expected, including the long term maintenance periods did not increase the error percentage. In fact, inclusion of the long-term maintenance periods increased the accuracy due to the increase in number of movements to be predicted in a year. The reason that inclusion of maintenance periods did not spoil the accuracy was because large maintenance periods do happen every year and including them to determine the empirical distribution between various runways made the model reflect reality better Importance of Predictors (Sensitivity Analysis) Sensitivity Analysis was done for all the developed runway usage models to determine how the variation of the predictors affects the output and how robust the developed models are. The accuracy of the noise forecast depends upon the robustness of the runway usage models. The predictors obtained from the expected flight schedules such as peak period, type of operation, sector/iaf, UDP, DEN were not varied since those predictors are fixed depending upon the flight schedules. The predictors that were varied are the predictors derived from weather data and the analysis was carried out for the year The results are summarized in table

70 Increasing wind speed by 2 knots Decreasing wind speed by 2 knots Increasing wind direction by 10 degrees Decreasing wind direction by 10 degrees Increasing wind speed by 2 knots Decreasing wind speed by 2 knots Increasing wind direction by 10 degrees Decreasing wind direction by 10 degrees Increasing wind speed by 2 knots Decreasing wind speed by 2 knots Increasing wind direction by 10 degrees Decreasing wind direction by 10 degrees Increasing wind speed by 2 knots Decreasing wind speed by 2 knots Increasing wind direction by 10 degrees Decreasing wind direction by 10 degrees Table 6.9: Sensitivity Analysis % Change in % Change in Landings Take-off Accuracy Accuracy Change in Number of flights with No Search Results Model % -0.10% 93% -0.21% -0.15% 670% -0.50% -0.45% 301% -0.50% -0.40% 344% Model % -0.22% 111% -0.32% -0.25% 710% -0.60% -0.53% 369% -0.65% -0.57% 390% Model % -0.54% 0% -0.30% -0.40% 0% -0.44% -0.29% 0% -0.20% -0.07% 0% Model % -0.45% 0% -0.40% -0.50% 0% -0.37% -0.35% 0% -0.30% -0.10% 0% It can be seen that the decrease in accuracy is less when the predictor values are changed for all the models. However, for Model 1 and Model 2, the number of flights with no search results increases when the predictor values are changed. In that aspect, Model 3 and Model 4 are better. 59

71 Research Sub-question Answered: How are the developed runway usage models verified? Chapter Conclusion: The developed runway usage models were verified by comparing with the actual runway usage. All the developed runway usage models showed a higher accuracy when longer period was considered. Noise forecast is the suitable application for Model 2 and Model 4 while Model 1 and Model 3 are better suited for predicting the number of movements on each runway. The accuracy of Model 2 was improved by using a probabilistic runway assignment. Comparing the developed models based on application (Model 1 vs Model 3 and Model 2 vs Model 4), the models developed using neural networks (Model 3 and Model 4) showed a higher accuracy on average. Inclusion of maintenance periods improved the prediction accuracy of the runway usage models. Introduction of addition predictors and/or varying the predictor class sizes for model 1 and model 2 resulted in more flights with no search results. Model 3 and Model 4 are more robust than Model 1 and Model 2 since varying the predictor values resulted in the increase of number of flights with no search results for Model 1 and Model 2. 60

72 61

73 7 Computations of Noise Load around the Airport This chapter deals with the validation of the developed runway usage models by investigating its effects on the computations of noise load around the airport Airport Noise Modelling The noise computations for this research were done using the Dutch noise calculation model since Schiphol airport is taken as the case study and also the current runway use model (compass rose) uses the same noise calculation method which makes a comparison possible. The Dutch noise calculation model uses simulation modeling technique. A simulation model describes the aircraft flight path by a series of discrete points in space crossed by the aircraft at small intervals of time. The noise level at any observer point is determined by calculating the sound radiated from each point in the flight path to the observer point. The noise metrics can be derived from this. At Schiphol airport, the two important noise metrics used to compute the yearly average noise levels are L DEN and L Night (both are based on Sound Exposure Level (SEL) (van Benten, 2009). Sound exposure level is the equivalent A- weighted sound level when normalized to a time period of one second. L DEN is a noise metric which takes the total amount of noise produced during the 24-hour day period into account and L Night is a measure for nightly produced aircraft noise, in which the night is defined as lasting from to 7.00 h. The L DEN and L Night indices are calculated by a simple simulation technique based on L AE NPD tables, which define noise levels as function of thrust setting and distance to the observer (Licitra, 2012). The instantaneous noise immission levels versus time (L A (t)) are first calculated by simulation of the flight using L AE noise tables. Then, the SEL value (L AE ) of a noise event is calculated by the subsequent integration of the discrete noise contributions of the aircraft along the flight path over time (Montrone, et al., 2001) The L DEN and L Night are calculated as follows - 62

74 Where, N L AE T 1 T2 - Weighting factor for the aircraft according to the period of the day - Sound Exposure Level defined as the time-integrated A-weighted sound pressure level, normalized to a reference time of 1 second - Time constant defined as 10 times the base-10 logarithm of the number of seconds in one year - Time constant determined using the number of seconds per eight hour night period. The weighting factors, N for the day, evening and night periods are as follows: For Day (7.00 to 19.00), N=1 For Evening (19.00 to 23.00), N=3.16 For Night (23.00 to 7.00), N=10 Noise Contours are a way to visualize the noise load by plotting lines of constant noise level. They depend on runway usage, fleet mixture, number of flight movements and the flight routes. They are generated by calculating the noise at a large number of points on the ground and by plotting lines with equal values. The noise enforcement system at Schiphol airport uses the total number of houses, highly annoyed people and sleep disturbed people within the appropriate L DEN and L night contours as noise enforcement measures Determination of Other Parameters for Noise Computations To compute noise for an aircraft movement, the required aircraft movement information is as follows runway, route, aircraft category, and procedure class. The runways of the aircraft movements were obtained from the runway use model and the aircraft category was determined from the aircraft type. The route and procedure class were determined using the following steps Route Assignment First, an empirical route assignment table was generated using actual traffic data. For each runway combination, runway and Sector/IAF, the route was determined empirically. Route assignment varies for day and night periods. Hence, separate route assignment tables were generated for day and night. Figure 7.1: Determination of Route Procedure Class Procedure class describes the class of flight profile followed by the aircraft during take-off or landing. A procedure class empirical table was also generated using actual traffic data. It 63

75 was done separately for take-offs and landings since the parameters that affect procedure class are different for take-offs and landing. Also, the procedure class varies for day and night periods and hence, two procedure class tables were generated for day and night periods respectively. To determine the procedure class for take-offs, first a distance category was determined for each movement based on the distance to the destination. Then, for each combination of aircraft category and distance category, the procedure class was determined empirically from traffic data. Figure 7.2: Determination of Procedure Class for Departures For landings, it is more complex to determine the procedure class than for take-offs due to the variation of procedure class when multiple landing runways are used and due to the use of Continuous Descent Approaches (CDA) during night. Hence, for each combination of aircraft category, runway combination and runway, procedure class was determined. For landings also, two procedure class tables were generated for day and night periods respectively. Figure 7.3: Determination of Procedure Class for Arrivals 7.3. Noise Computation Results Noise calculations were performed using the Dutch Noise Calculation Model. The computations are based on traffic that is included in the runway use models, thus for instance excluding general aviation traffic. The results of the noise calculation are presented in L DEN metric. The L DEN metric was evaluated along the 48 and 58 dba L DEN values since these are typically used for noise policy related questions in the Netherlands. As mentioned in section , Model 2 and Model 4 are better suited for noise forecast application. Hence, only model 2 and model 4 will be compared with each other and actual noise forecast (actual runway usage and modelled routes). When no results are found in the search for some aircraft movements, a scale factor is found using the formula below 64

76 The scale factor is found separately for day, evening and night periods of take-offs and landings. The number of predicted movements per runway (in day, evening and night periods for take-offs and landings) is then multiplied by this scale factor. For the computation of the actual noise results, the actual runway usage, modeled routes and procedure class was used. The number of highly annoyed people was determined with dose-relationships developed for Schiphol airport (Fast, et al., 2012). The population density information was taken from the European Environment Agency (European Environment Agency (EEA), 2002). The data required to count the number of houses was obtained from the so called Basisregistraties Adressen en gebouwen (BAG), which is made available by the Ministry of Infrastructure and Environment (Ministry of Infrastructure and Environment, 2014). Since it is important to evaluate the number of highly annoyed people within the 48 dba L DEN and number of houses within the 58 dba L DEN for policy-making in Netherlands, the following table shows the comparison of those values computed using runway usage model 2 and runway usage model 4 with the same values computed using actual runway usage. Table 7.1: Comparison of the Number of Highly Annoyed People and Number of Houses Number of Highly Annoyed People within the 48 L DEN Number of Houses within the 58 L DEN Actual Model 2 % Difference Model 4 % Difference % % % % The values in the above table cannot be compared to actual values found for enforcement for several reasons: The use of other data sources for the location of houses and people The exclusion of traffic, for instance general aviation traffic The computations are not based on radar tracks but on modeled flight routes (as is done for formal enforcement computations) and no correction is applied for uncertainty in weather conditions (as is done for formal noise forecast computations). It can be seen that both the models underestimate the number of highly annoyed people within the 48 L DEN and overestimate the number of houses within the 58 L DEN. Model 4 shows only a range of +2 to - 2% differences in comparison with the actual results while Model 2 shows a range of +4 to -4% differences in comparison with the actual results. This shows that runway usage Model 4 results in higher noise forecast accuracy. Sub-Conclusion Runway usage model 4 (neural networks) is better than the runway usage model 2 (nearest neighbour) in terms of noise forecast accuracy. 65

77 The comparison of the 48 dba and 58 dba L DEN contours are shown in the figures 7.4, 7.5, and 7.6. Figure 7.4: Comparison of 48 dba L DEN and 58 dba L DEN contours computed using Actual Runway Usage and Runway Usage Models 2 and 4 As seen in figures 7.5. and 7.6., differences in L DEN values are large around runway This is because both the models do not predict the number of movements on runway well due to the exclusion of runway 04 from runway combinations and exclusion of runway 22 from runway combinations when it was used as a secondary runway. If runway 22 and runway 04 were included in the runway combinations, there would have been an overestimation of movements on runway 22 and runway 04, which would have resulted in bigger differences in L DEN values. The other major differences in the L DEN values are due the difference in the prediction of number of movements in runways 06, 24, 09 and 27. It can be seen the differences in the approach/ departure paths of the north-south runways are comparatively lower since these runways are more frequently used and there were more observations for the models to find the patterns. Comparing figures 7.5. and 7.6, the differences in the number of highly annoyed people within the 48 dba L DEN contour and the number of houses within the 58 dba L DEN contour can be accounted to the differences seen in the L DEN values due to the difference in runway 66

78 usage prediction by both the models. The difference in L DEN values is less for model 4 than model 2. Figure 7.5: Difference between Actual and Model 2 Figure 7.6: Difference between Actual and Model 4 67

79 Research Sub-questions Answered How to compute noise load around the airport? Based on the effect of the runway usage models on the computations of noise load around the airport, which runway usage model can be identified to improve noise forecast accuracy? Chapter Conclusion The noise load around the airport was computed using the Dutch noise calculation method. Runway usage model 4 (neural networks) is better than the runway usage model 2 (nearest neighbour) in terms of the predicted values of number of highly annoyed people within the 48 dba L DEN and number of houses within the 58 dba L DEN.. The differences in L DEN values are large around runway This is because both the models do not predict the number of movements on runway well due to the exclusion of runway 04 from runway combinations and exclusion of runway 22 from runway combinations when it was used as a secondary runway. If runway 22 and runway 04 were included in the runway combinations, there would have been an overestimation of movements on runway 22 and runway 04, which would have resulted in bigger differences in L DEN values. 68

80 69

81 8 Conclusion and Recommendations 8.1. Conclusion The following conclusions have been made - Among the various runway usage methods, probabilistic empirical modelling has been identified to improve the runway usage prediction accuracy since it includes the controller s decision-making patterns. Among the 4 runway usage models developed, it has been identified that the runway usage model developed using neural networks (Model 4) improves the runway usage prediction accuracy and noise forecast accuracy the most. Since the network used is a feedforward backpropagation network, the prediction of runway usage is comparatively faster than other models developed. The main factors that influence runway usage have been identified to be: wind direction, wind speed, visibility, period of the day, required capacity, type of operation (landing/take-off), and origin/destination. The developed runway usage models have been validated for Schiphol airport and can be applied for other complex multi-runway airports like Schiphol airport. To get better accuracy, the predictors that influence runway usage for the particular airport has to be analysed further and implemented. The developed runway usage models with improved accuracy will aid in improved noise load prediction around the airport for transparency with surrounding communities, determining annual usage plan and analysing noise mitigation measures. 70

82 8.2. Recommendations The following are the recommendations Use of Probabilistic Neural Networks Probabilistic Neural Networks (PNN) is good for classification problems. When an input is presented, the first layer computes distances from the input vector to the training input vectors and produces a vector whose elements indicate how close the input is to a training input. The second layer sums these contributions for each class of inputs to produce as its net output a vector of probabilities. The main advantage of PNN is that it works with one-step learning. The learning of backpropagation neural networks can be compared to a trial and error method. It takes a lot of time to train a backpropagation neural network and the size of the training dataset should be large to get good generalization. The amount of data needed for PNN is a lot smaller than the amount of data needed for backpropagation neural networks. Another advantage of PNN is that they are very flexible and new information can be added with almost noretraining while backpropagation neural networks need re-training when new information is added. PNN can also be more accurate than backpropagation neural networks and are relatively insensitive to outliers. Hence, using a PNN for runway usage classification can make identification of improvements an easier process than backpropagation neural networks. Although PNN has many advantages compared to backpropagation neural networks, it does have some disadvantages as well. While classifying new cases backpropagation neural networks can be faster than PNN and PNN requires more memory space to store the model. Use of Probabilistic route assignment and procedure class Due to the computational limitations, a deterministic route assignment and procedure class table was used. Use of a probabilistic route and procedure class table might improve the accuracy of the noise forecast further. Use of Compass Rose Model Instead of Scaling When there are no search results found for certain flights, Model 1 and Model 2 scale them to the total volume while computing noise load around the airport. But, instead compass rose model (current runway use prediction method) can be used for such cases. 71

83 References Ahrens, D., Jackson, P., & Jackson, C. (2012). Meteorology Today: An Introduction to the Weather, Climate and Environment (1st ed.). Nelson. Alpaydın, E. (2010). Introduction to Machine Learning (2nd ed.). London : The MIT Press. Anagnostakis, I., Idris, H. R., Clarke, J.-P., Feron, E., Hansman, J. R., Odoni, A. R., & Hall, W. D. (2000). A Conceptual Design of A Departure Planner. Napoli: 3rd USA/Europe Air Traffic Management R&D Seminar. Andoni, A. (2009). Nearest Neighbor Search: the Old, the New, and the Impossible. Massachusetts: Massachusetts Institute OF Technology. Arya, S., Mount, D., Netanyahu, N., & Wu, A. (1998). An optimal algorithm for approximate nearest neighbor searching. Journal of the ACM, 45(6), Ashford, N., & Wright, P. H. (2011). Airport Engineering: Planning, Design and Development of 21st Century Airports (4th ed.). New York: Wiley and Sons. Atkins, S. (2010). Toward System Oriented Runway Management. 29th Digital Avionics Systems Conference. 29th Digital Avionics Systems Conference. Bazargan, M., Fleming, K., & Subramanian, P. (2002). A Simulation Study to Investigate Runway Capacity Using TAAM. Proceedings of the 2002 Winter Simulation Conference. Beale, M. H., Hagan, M. T., & Demuth, B. H. (2013). Neural Network Toolbox - User's Guide. Massachusetts: MathWorks. Bertsimas, D., Frankovich, M., & Odoni, A. (2011). Optimal Selection of Airport Runway Configurations. Operations Research, 59(6), Bewoners Aanspreekpunt Schipol (BAS). (2013). Informatie - Banenstelsel. Schiphol. Boeing Company. (2011). Airport Noise and Emissions Regulations - Schiphol Airport. Opgeroepen op February 20, 2014, van Cheng, V., Crawford, L., & Menon, P. (1999). Air Traffic Control Using Genetic Search Techniques. Kohala Coast-Island of Hawai i: IEEE International Conference on Control Applications. Cole, R. E., Green, S., & Benjamin, G. S. (2000). Wind Prediction Accuracy for Air Traffic Management Decision Support Tools. 3rd USA/Europe Air Traffic Management R&D Seminar. Diallo, O. N. (2012). A Predictive Aircraft Landing Speed Model Using Neural Network. 31st Digital Avionics Systems Conference. 72

84 Dreyfus, S. (2002). Richard Bellman on the Birth of Dynamic Programming. Operations Research, 50, European Civil Aviation Conference. (2005). ECAC.CEAC Doc 29 3rd Edition - Report on Standard Method of Computing Noise Contours around Civil Airports. Paris: ECAC.CEAC. European Environment Agency (EEA). (2002). Distribution of Population Using Corine Land Cover FAA. (2001). Enhanced Preferential Runway Advisory System (ENPRAS). Opgeroepen op January 27, 2014, van Fast, T., van de Hazel, P., & van de Weerdt, D. (2012). Gezondheidseffectscreening - Gezondheid en milieu in ruimtelijke planvorming. GGD Nederland. Frontier. (2006). Daisy Traffic Management Tool. Galis, S. P., Brouwer, M. A., & Joustra, T. (2004). Optimization of Yearly Airport Capacity Within Noise Limits at Schiphol Airport. Prague: The 33rd International Congress and Exposition on Noise Control Engineering. Gilbo, E. (1993). Airport capacity: representation, estimation, optimization. IEEE Transactions on Control System Technology, 1(3), Gilbo, E., & Howard, K. (2000). Collaborative optimization of airport arrival and departure traffic flow management strategies for CDM. Proceedings of 3rd USA/Europe Air Traffic Management R&D. Hall, W. D. (1999). Efficient capacity allocation in a collaborative air transportation system. Ph.D. dissertation, Massachusetts Institute of Technology. Heblij, S., & Wijnen, R. (2008). Development of a Runway Allocation Optimisation Model for Airport Strategic Planning. Amsterdam: Nationaal Lucht- en Ruimtevaartlaboratorium. Hesselink, H., & Nibourg, J. (2011). Probabilistic 2-Day Forecast of Runway Use. Amsterdam: Nationaal Lucht- en Ruimtevaartlaboratorium. Isaacson, D. R., & Davis, T. J. (1997). Knowledge-Based Runway Assignment for Arrival Aircraft in the Terminal Area. New Orleans: AIAA Guidance, Navigation, and Control Conference. Johansson, E., Dowla, F., & Goodman, D. (1990). Backpropagation Learning for Multi-Layer Feed-Forward Neural Networks Using the Conjugate Gradient Method. Lawrence Livermore National Laboratory. Jon L, B., Donald, S., Williams, F., & Hollins, E. (1977). The complexity of finding fixedradius near neighbors. Information Processing Letters, 6(6), Jopson, I., Rhodes, D., & Havelock, P. (2002). Aircraft noise model validation how accurate do we need to be. Institute of Acoustics conference Action on Environmental Noise. KLM. (sd). Factsheet Network and Hub Systems. Schiphol. 73

85 KNMI. (2012). Klimatologie - Uurgegevens. Opgeroepen op August 10, 2014, van Kuikka, I. (2009). Wind Nowcasting: Optimizing runway in use. Espoo: Helsinki University of Technology. Kuiper, B. R., Visser, H. G., & Heblij, S. (2012). Efficient Use of an Allotted Airport Annual Noise Budget Through Minimax Optimization of Runway Allocations. Proceedings of the Institution of Mechanical Engineers. Journal of Aerospace Engineering. Kuiper, B., Visser, H., & Heblij, S. (2011). Efficient use of the Noise Budget at Schiphol Airport through Minimax Optimization of Runway Allocations. Virginia Beach: 11th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference. Li, L., & Clarke, J.-P. (2010). Airport Configuration Planning with Uncertain Weather and Noise Abatement Procedures. 29th Digital Avionics Systems Conference. Li, L., Clarke, J.-P., Chien, H.-H. C., & Melconian, T. (2009). A Probabilistic Decision- Making Model for Runway Configuration Planning Under Stochastic Wind Conditions. 28th Digital Avionics Systems Conference. Licitra, G. (2012). Noise Mapping in the EU (1st ed.). Boca Raton: CRC Press. Meerburg, T., Boucherie, R., & van Kraaij, M. (2007). Noise Load Management at Amsterdam Airport Schiphol. Amsterdam: National Aerospace Laboratory. Ministry of Infrastructure and Environment. (2014). Basisregistraties Adressen en Gebouwen. Opgeroepen op August 1, 2014, van Moertl, P. M., Hitt, I. J., Atkins, S., Brinto, C., & Walton, D. H. (2003). Factors for Predicting Airport Surface Characteristics and Prediction Accuracy of the Surface Management System. IEEE International Conference on Systems, Man and Cybernetics. Moller, M. F. (1993). A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning. Aarhus University, Computer Science Department. Montrone, L., Wubben, F., Roeloffs, A., & Vogel, P. (2001). Feasibility of coupling of an Airport capacity model to Airport noise models. The Hague: The 2001 International Congress and Exhibition on Noise Control Engineering. Nibourg, J., Hesselink, H., & van de Vijver, Y. (2008). Environment-Aware Runway Allocation Advice System. Anchorage: 26th ICAS Congress. Ramanujam, V., & Balakrishnan, H. (2011). Estimation of Maximum-Likelihood Discrete- Choice Models of the Runway Configuration Selection Process. San Francisco: American Control Conference. Rosin, A., Hecht, M., & Handale, J. (1999). Analysis of Airport-Runway Availability. IEEE Proceedings on Annual Reliability and Maintainability Symposium. Southgate, D. G., & Firth, J. P. (2004). Improving the Accuracy of Runway Allocation in Aircraft Noise Prediction. Gold Coast: Proceedings of Acoustics. 74

86 Southgate, D. G., & Sedgwick, S. J. (2006). Time stamped aircraft noise prediction - replacing the Average Day with the Composite Year. Honolulu: 35th International Congress and Exposition on Noise Control Engineering (Inter-Noise). Ummels, R. (2014, February 18). Aircraft Noise and Communication, 3rd ACI Airport Environmental Seminar. Australia. van Benten, S. (2009). The Development of Noise Abatement Departure Procedures for Amsterdam Airport Schiphol. Delft: Delft University of Technology. van Es, G., & Karwal, A. (2001). Safety aspects of tailwind operations. Amsterdam: Nationaal Lucht- en Ruimtevaartlaboratorium. van Es, G., van der Geest, P., & Nieuwpoort, T. M. (2001). Safety Aspects of Aircraft Operations in Crosswind. Amsterdam: Nationaal Lucht- en Ruimtevaartlaboratorium. Wijngaard, J., Vogelezang, D., & van Bruggen, H. (2007). Low Visibility and Ceiling Forecasts at Schiphol. KNMI. Wikle, C. (2014). Statistics for Spatio- Temporal Data (1st ed.). Cram101 Textbook Reviews. 75

87 Appendix A. Runway Usage Prediction Methods for Strategic and Tactical Planning Most of the recent research is focused on developing a decision support system for runway allocation that advise runway configurations to balance noise impact by suggesting alternate lower capacity configurations provided that, weather conditions and demand permit (Hesselink & Nibourg, 2011) (FAA, 2001) (Nibourg, et al., 2008) (Atkins, 2010). The following are some of the decision-aid systems developed Runway Allocation Advice System (RAAS) For tactical and strategic planning of runway usage, Runway Allocation Advice System (RAAS) has been operational at Schiphol airport since 1998 (Nibourg, et al., 2008). It aids the tower and approach air traffic controllers in the decision-making process of selecting runway configuration. It gives a list of different possible runway configurations based on safety, runway availability and noise constraints arranged in the order of noise preference. The system can be used at any airport that uses a noise preference runway system. Apart from safety and efficiency, it is also important for ATC to make its runway selection choice with a view to reduce the number of noise complaints. RAAS works based on the preference list fed to it (Nibourg, et al., 2008). A preference list is a list of preferred runway configurations in terms on noise load and it can be changed in any given period (Meerburg, et al., 2007). RAAS gets the current weather data from a meteorological data server. RAAS can be applied to any airport especially the ones that use noise-preferential runway system. A noise preference runway system is the one that selects runways based on safety limits and noise regulations, which is used to meet the noise limits requirements. That is, if there is more than one runway configuration that meets the safety criteria, the one which is preferred in terms on noise load is used. The disadvantage of RAAS is that in an unstable weather condition, it may give varying advisories over time and the varying advice will mostly not be followed by the approach supervisor due to cost incurrence and additional workload resulting from changing runways. System for Probabilistic Interactive Runway Indication (SPIRIT) It is currently being developed by NLR in co-operation with KNMI to give approach supervisor the probability that a certain runway combination can be used for a period of 1 to 30 hours, which will overcome the limitations of RAAS. This probabilistic runway forecast uses actual, nowcast and forecast weather data. Since weather forecast is a probability forecast, the runway forecast was also made probabilistic (Hesselink & Nibourg, 2011). The first step in this method was to determine what the available runway configurations were depending on the weather data. The probabilistic weather data was converted into a probabilistic runway configuration data. By comparing the predicted runway usage with the actual runway usage, different algorithms for the selection of highest probable runway configuration from the list of possible runway configurations (noise-preferred list) were evaluated. The algorithm rules were based on practical findings Controllers tend to choose the highest probable runway configuration when the probabilities are above 80% since they are reluctant to change runways during their shift (to avoid cost incurrence). Controllers also tend to choose the first or second runway configuration when all the runway 76

88 configuration probabilities are below 80%. This method has been evaluated for Schiphol airport for the year 2009 and 60 to 70% accuracy was achieved in runway usage prediction (Hesselink & Nibourg, 2011). The predictions were more accurate for night period than day period. Preferential Runway Advisory System (PRAS) PRAS was developed for Boston Logan International airport using research carried out in MIT (FAA, 2001). The system recommends runway configurations based on safety limits and takes into account required runway maintenance and forecasted demand. The core for the development of the decision-aid system for the controllers was the conceptual design of Departure Planner by MIT. The conceptual planner module of departure planner decision aid relates the operational capacity to the scheduled demand taking into account the weather and environmental limits. The available runway configurations are determined based on weather and noise limits and based on these the hourly capacity (number of hourly operations) can be determined. The expected traffic is then distributed over the runway configuration (Anagnostakis, et al., 2000). Surface Management System (SMS) NASA in co-operation with FAA developed SMS which has the ability to predict departure runway allocations (Moertl, et al., 2003). Various predictor variables were analyzed for Memphis International airport. Through logistic regression, the results concluded that the departure fix of the flight was the first best predictor and ramp area from which the flight pushed back was the second best predictor (Moertl, et al., 2003). The results of predictor variables associations with runway allocation were included in SMS prediction algorithm for departure runway prediction. If this method were to be used in any other airport, analysis should be done in determining the predictor variables associations. Final Approach Spacing Tool (FAST) A knowledge based runway allocation algorithm was developed for the Final Approach Spacing Tool (FAST), which is a controller advisory system. It has been implemented and operationally tested for Dallas/Fort Worth airport. The results indicate that there was a strong adherence to the controller advisories and it increased the capacity with no significant impact on controller workload. The knowledge base of several hours of simulation with expert ATCs was used to determine a set of hierarchical rules and decision logic for the algorithm. Performance and workload criteria were evaluated to determine the hierarchichal rules and decision logic (Isaacson & Davis, 1997). Runway Selection Optimization Modelling methods are explained below- Probabilistic Decision-Making Approach In a study carried out by Georgia Institute of Technology, a decision-making approach was developed for runway configuration selection under stochastic wind conditions. The objectives of the optimization were to maximize the airport throughput and minimize airborne and terminal delay for a given wind forecast data. Dynamic programming and backwards induction was used to solve the probabilistic optimality equation under stochastic wind conditions (Li & Clarke, 2010) (Dreyfus, 2002). Stochastic dynamic programming is useful in cases where there is a need for optimization of decisions, where the outcomes are uncertain, but partly under the control of the decision-maker (Meerburg, et al., 2007). This method was tested for JFK airport by simulation and it was found that it reduced delay and increased the throughput. 77

89 Georgia Institute of Technology also developed a runway resource allocation model for airport strategic planning. The model was used to optimize the runway selection and match capacity according to the demand by minimizing delay and noise costs. The constraints for this multi-objective optimization model were related to operational procedures, runway configuration capacity, safety, and weather conditions. Since the weather forecast is uncertain, a probability density function of forecast errors was used to quantify the weather forecast accuracy and the probability that a runway configuration is available under given conditions was calculated using the probability density function of forecast errors so that the optimization model will generate best solution in the sense of probability (Cole, et al., 2000) (Li, et al., 2009). Mixed Integer Programming For the optimal selection of runways, mixed integer programming (MIP) model can be used. According to the research carried out by Massachusetts Institute of Technology, a MIP model was used to solve the problem of runway configuration selection. The objective was to minimize the total cost of delays and the constraints included safety, weather, noise load distribution etc. The model results were compared to sophisticated baseline heuristic and it was found that the MIP model reduced the delay costs by at least 10% (Bertsimas, et al., 2011). NLR developed a runway allocation optimization model (Mixed Integer Linear Programming/ MILP model) called SNAP (Strategic Noise Allocation Planning) which uses a multi-objective optimization with a view to minimize noise, third-party risk and delay (Heblij & Wijnen, 2008). Runway availability, wind limits and runway capacity were taken as constraints. This does not use any preference lists and instead allocates the optimal runway configurations directly. The results proved that the overall risk and annoyance indicator reduced by 30% (Heblij & Wijnen, 2008). But, since it is a very simple model, it cannot be yet applied to a complex airport like Schiphol airport without some model extensions. A runway allocation planning tool based on linear programming was developed by TU Delft, which makes efficient runway allocation to make sure there is an equal distribution of the annual noise load budget at Schiphol airport (Kuiper, et al., 2011). To make the optimization formulation simpler, the wind data was clustered into wind patterns and the traffic data was aggregated into traffic patterns and each wind pattern was combined with each traffic pattern and unique situations were formulated in a list along with the probability of their occurrence (Kuiper, et al., 2012). The goal of the optimization problem was to allocate runway configuration for each situation. The noise cost associated with each choice of runway configuration was determined and included. Due to computational constraints, not all the 35 L DEN enforcement points at Schiphol airport were used. This study s results showed 10% reduction of maximum noise energy at any enforcement point than the allowable value without increasing the noise level at any enforcement points that were not included in the study (Kuiper, et al., 2011). This model can be generalized and can be applied to any airport. Genetic Algorithms Schiphol developed a model to predict the traffic distribution over runway combinations based on different operational usages of Schiphol airport. Also, using genetic algorithms, Schiphol developed a model that maximizes the yearly airport capacity and minimizes the 78

90 risk of exceeding the exceeding the yearly noise limits (Galis, et al., 2004). This model uses traffic forecast model, runway combination selection model and historical weather data. 79

91 B. Schiphol airport Charts Figure B.1: Standard Departure Chart - Instrument 80

92 Figure B.2: Standard Arrival Chart - Instrument 81