Notwithstanding the global economic crisis, long-term forecasts of air transport growth speak about a doubling in air transport demand in Europe by 2030 (Eurocontrol, 2008). Characteristically, although the planned capacity of 138 Eurocontrol Statistical Reference Area (ESRA) airports is planned to increase by 41% in total by 2030, the demand will still exceed capacity of the airport system by as many as 7.0 million flights in a high-growth scenario for 2030 (Eurocontrol, 2008). Under such a scenario, 14–39 European airports will need to operate at full capacity 8 h per day to accommodate only a part of the demand, similar to what most severely congested airports do now (Eurocontrol, 2008). A direct consequence of the mismatch between capacity and traffic growth is the increase of congestion and delays both in the air and on the ground (at airports). Airports constitute the terminal nodes of a continuously expanding air transport network that should both efficiently and safely accommodate growing traffic. The anticipated traffic growth in decades to come will again push capacity to the limit, thus triggering unprecedented levels of congestion with far reaching impact on the environment and the safety of operations. The latter concerns will pose serious challenges towards close airport performance monitoring and improvement. Airport decision makers should be able to cope with multiple – even conflicting – objectives and priorities assigned by various stakeholders regarding the multifaceted performance of the airport system such as the level of service offered to the travelling public, the efficiency of airport and air traffic management (ATM) operations, the quality of the surrounding environment, and the safety of the entire air transport system. The assessment of the airport performance requires a deep understanding of the manifold aspects of airport performance supported by advanced modelling capabilities and decision support systems, or tools for measuring it. Such decision aids should be diverse in that they should: (1) capture the behaviour of various entities (e.g. aircraft, passenger, baggage) processed through the system, (2) address different airport elements simultaneously (e.g. runway system, taxiway system, apron area, terminal), and (3) consider a large set of airport performance measures like capacity, delays, safety, security, noise and costs. In order to deal with the multi-faceted aspects of the airport decision making process, a wealth of decision support models and tools have appeared in both literature and practice (Odoni, 1991; Tosic, 1992; Odoni et al., 1997; Lucic et al., 2007; Correia et al., 2008; Long et al., 2009). Early modelling efforts developed rather focused applications (e.g. models, tools) both in terms of integration scope and degree of coverage. They basically constituted ‘monolithic’ modelling structures exhibiting either analytical or simulation modelling approaches with focused decision support capabilities mainly with view to a single airport performance measure, for example, runway capacity (not accounting for trade-offs). At the same time, early modelling efforts had a targeted/narrow coverage of specific elements of either airside (mainly runways) or landside. Since their early stages of development in the 1960s, airport performance models and tools have evolved substantially with a common orientation being the pursuit of more integration and expanded coverage capabilities. More recent research initiatives since 1990s attempted the integration of pre-selected and pre-existing tool configurations in order to model and evaluate simultaneously airport airside and landside and assess their interdependencies (Andreatta et al., 1999; Zografos and Madas, 2006). These efforts primarily suffered from the lack of a harmonized, fully integrated and automated computing environment needed to execute the various models, as well as limited trade-off analysis capabilities. Despite the rich experience in both models and tools for airport performance assessment, modelling capabilities until 10 years ago or so addressed only partial aspects of the airport performance and exhibited several deficiencies: (1) they were concerned with specific flows or entities (e.g. aircraft, passengers, baggage), (2) they focused on specific airport elements (e.g. runway system, apron, terminal), (3) they considered one (or very few) airport performance indicator at a time (e.g. capacity, noise, safety, emissions), and (4) they were tailored for a specific level of decision making, either strategic, tactical or operational. At the outset, there was a clear lack of integrated modelling capabilities for assessing multiple performance measures simultaneously (and their trade-offs) for the airport system in its entirety (i.e. ‘total airport’), that is, for both airport airside and landside simultaneously. The latest developments in the airport modelling landscape involve the emergence of integrated platforms or systems1. Currently, there are a limited number of software products/decision support systems (mainly ‘off-the-shelf’) with integrated impact analysis capabilities for total airport operations. Most of them are purely simulation platforms basically integrating detailed simulation tools at a microscopic level. As a result, they do not exhibit macroscopic/aggregate analysis capabilities at the strategic decision making level with the use of analytical models. Furthermore, the existing, simulation based tools are quite complicated, rather data intensive, have a costly set up process for different airports, and require substantial tool familiarity and prior computational expertise. Another common feature for most of these tools is that they capture the airside-landside interaction, but still provide limited trade-off analysis capabilities, since they primarily focus on capacity and delay metrics. As a result, the basic modelling challenge remains, that is, to develop systems and tools that will not only capture the manifold aspects of airport performance in isolation, but will be also able to analyse, with reasonable effort, the various trade-offs and interdependencies among these performance measures, entities, or airport elements. In response to the identified modelling needs, an integrated Decision Support System (DSS), the ‘Supporting Platform for Airport Decision Making and Efficiency Analysis’ (SPADE DSS), has been developed recently. The proposed system has the form of a computational platform that seamlessly integrates a variety of existing analytical models and simulation tools in order to capture the interdependencies among various measures of airport effectiveness (e.g. capacity, delays, level of service, noise, safety, costs and benefits) and enable performance trade-off analyses at various levels of detail (e.g. strategic, tactical/operational). Furthermore, the SPADE DSS allows decision makers and analysts to evaluate the efficiency of the entire airport complex simultaneously (including also interaction effects among airport elements). However, the most important and innovative element of the proposed modelling approach is the adoption of the ‘use case’ paradigm as the main building component of the system implementation structure. The use case driven implementation approach supports a problem or decision oriented approach that is capable of addressing airport planning decisions in a user-friendly manner and at a reasonable effort without requiring prior familiarity of the user with the selected tools (e.g. build baseline/‘what-if’ scenarios, prepare and exchange data sets, perform trade-off analysis). The objective of this chapter is twofold: 1. to introduce the structure and constituting elements of the SPADE system; and 2. to demonstrate the decision support capabilities of the system under ‘real-world’ conditions by means of two manifestations of the system for strategic decision making (Athens International Airport) and operational/tactical decision making (Amsterdam Airport Schiphol). The remainder of this chapter consists of four main sections. Section 2.2 provides an overall description of the high level structure of the SPADE DSS with special emphasis placed on the use case driven modelling concept. Section 2.3 provides a demonstration of two application instances of the system for strategic and operational/tactical decision making, respectively. Section 2.4 presents the concluding remarks and lessons learnt during the system implementation, whilst reporting some brief results from the evaluation of the system. Finally, the final sections present the acknowledgements and a list of reference sources.

A Decision Support System for Integrated Airport Performance Assessment and Capacity Management

ANDREATTA, GIOVANNI;
2013

Abstract

Notwithstanding the global economic crisis, long-term forecasts of air transport growth speak about a doubling in air transport demand in Europe by 2030 (Eurocontrol, 2008). Characteristically, although the planned capacity of 138 Eurocontrol Statistical Reference Area (ESRA) airports is planned to increase by 41% in total by 2030, the demand will still exceed capacity of the airport system by as many as 7.0 million flights in a high-growth scenario for 2030 (Eurocontrol, 2008). Under such a scenario, 14–39 European airports will need to operate at full capacity 8 h per day to accommodate only a part of the demand, similar to what most severely congested airports do now (Eurocontrol, 2008). A direct consequence of the mismatch between capacity and traffic growth is the increase of congestion and delays both in the air and on the ground (at airports). Airports constitute the terminal nodes of a continuously expanding air transport network that should both efficiently and safely accommodate growing traffic. The anticipated traffic growth in decades to come will again push capacity to the limit, thus triggering unprecedented levels of congestion with far reaching impact on the environment and the safety of operations. The latter concerns will pose serious challenges towards close airport performance monitoring and improvement. Airport decision makers should be able to cope with multiple – even conflicting – objectives and priorities assigned by various stakeholders regarding the multifaceted performance of the airport system such as the level of service offered to the travelling public, the efficiency of airport and air traffic management (ATM) operations, the quality of the surrounding environment, and the safety of the entire air transport system. The assessment of the airport performance requires a deep understanding of the manifold aspects of airport performance supported by advanced modelling capabilities and decision support systems, or tools for measuring it. Such decision aids should be diverse in that they should: (1) capture the behaviour of various entities (e.g. aircraft, passenger, baggage) processed through the system, (2) address different airport elements simultaneously (e.g. runway system, taxiway system, apron area, terminal), and (3) consider a large set of airport performance measures like capacity, delays, safety, security, noise and costs. In order to deal with the multi-faceted aspects of the airport decision making process, a wealth of decision support models and tools have appeared in both literature and practice (Odoni, 1991; Tosic, 1992; Odoni et al., 1997; Lucic et al., 2007; Correia et al., 2008; Long et al., 2009). Early modelling efforts developed rather focused applications (e.g. models, tools) both in terms of integration scope and degree of coverage. They basically constituted ‘monolithic’ modelling structures exhibiting either analytical or simulation modelling approaches with focused decision support capabilities mainly with view to a single airport performance measure, for example, runway capacity (not accounting for trade-offs). At the same time, early modelling efforts had a targeted/narrow coverage of specific elements of either airside (mainly runways) or landside. Since their early stages of development in the 1960s, airport performance models and tools have evolved substantially with a common orientation being the pursuit of more integration and expanded coverage capabilities. More recent research initiatives since 1990s attempted the integration of pre-selected and pre-existing tool configurations in order to model and evaluate simultaneously airport airside and landside and assess their interdependencies (Andreatta et al., 1999; Zografos and Madas, 2006). These efforts primarily suffered from the lack of a harmonized, fully integrated and automated computing environment needed to execute the various models, as well as limited trade-off analysis capabilities. Despite the rich experience in both models and tools for airport performance assessment, modelling capabilities until 10 years ago or so addressed only partial aspects of the airport performance and exhibited several deficiencies: (1) they were concerned with specific flows or entities (e.g. aircraft, passengers, baggage), (2) they focused on specific airport elements (e.g. runway system, apron, terminal), (3) they considered one (or very few) airport performance indicator at a time (e.g. capacity, noise, safety, emissions), and (4) they were tailored for a specific level of decision making, either strategic, tactical or operational. At the outset, there was a clear lack of integrated modelling capabilities for assessing multiple performance measures simultaneously (and their trade-offs) for the airport system in its entirety (i.e. ‘total airport’), that is, for both airport airside and landside simultaneously. The latest developments in the airport modelling landscape involve the emergence of integrated platforms or systems1. Currently, there are a limited number of software products/decision support systems (mainly ‘off-the-shelf’) with integrated impact analysis capabilities for total airport operations. Most of them are purely simulation platforms basically integrating detailed simulation tools at a microscopic level. As a result, they do not exhibit macroscopic/aggregate analysis capabilities at the strategic decision making level with the use of analytical models. Furthermore, the existing, simulation based tools are quite complicated, rather data intensive, have a costly set up process for different airports, and require substantial tool familiarity and prior computational expertise. Another common feature for most of these tools is that they capture the airside-landside interaction, but still provide limited trade-off analysis capabilities, since they primarily focus on capacity and delay metrics. As a result, the basic modelling challenge remains, that is, to develop systems and tools that will not only capture the manifold aspects of airport performance in isolation, but will be also able to analyse, with reasonable effort, the various trade-offs and interdependencies among these performance measures, entities, or airport elements. In response to the identified modelling needs, an integrated Decision Support System (DSS), the ‘Supporting Platform for Airport Decision Making and Efficiency Analysis’ (SPADE DSS), has been developed recently. The proposed system has the form of a computational platform that seamlessly integrates a variety of existing analytical models and simulation tools in order to capture the interdependencies among various measures of airport effectiveness (e.g. capacity, delays, level of service, noise, safety, costs and benefits) and enable performance trade-off analyses at various levels of detail (e.g. strategic, tactical/operational). Furthermore, the SPADE DSS allows decision makers and analysts to evaluate the efficiency of the entire airport complex simultaneously (including also interaction effects among airport elements). However, the most important and innovative element of the proposed modelling approach is the adoption of the ‘use case’ paradigm as the main building component of the system implementation structure. The use case driven implementation approach supports a problem or decision oriented approach that is capable of addressing airport planning decisions in a user-friendly manner and at a reasonable effort without requiring prior familiarity of the user with the selected tools (e.g. build baseline/‘what-if’ scenarios, prepare and exchange data sets, perform trade-off analysis). The objective of this chapter is twofold: 1. to introduce the structure and constituting elements of the SPADE system; and 2. to demonstrate the decision support capabilities of the system under ‘real-world’ conditions by means of two manifestations of the system for strategic decision making (Athens International Airport) and operational/tactical decision making (Amsterdam Airport Schiphol). The remainder of this chapter consists of four main sections. Section 2.2 provides an overall description of the high level structure of the SPADE DSS with special emphasis placed on the use case driven modelling concept. Section 2.3 provides a demonstration of two application instances of the system for strategic and operational/tactical decision making, respectively. Section 2.4 presents the concluding remarks and lessons learnt during the system implementation, whilst reporting some brief results from the evaluation of the system. Finally, the final sections present the acknowledgements and a list of reference sources.
2013
Modeling and Managing Airport Performance
9780470974186
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