Air traffic has grown substantially in the past by about 4%-5% per year and according to forecasts of institutions like ICAO or manufacturers like Airbus or Boeing, demand for air traffic is expected to grow in the future by about the same pace. That means that global air traffic doubles every 15 years if airportcapacity is sufficient to handle the increased demand for flights. However, as we have seen in the past, air traffic is heavily concentrated on a rather small number of large airports: About 4% of the airports worldwide with scheduled traffic, i.e. 100 airports, handle more than 50% or 28 m aircraft movements. Hub traffic is essential for the global air traffic network to achieve a high de- gree of connectivity between any two origin – destination pairs efficiently. However, at the same time, it is becoming more and more difficult to expand hub airports like e.g. London Heathrow or Frankfurt to account for the in- creased demand for flights. In many cases the runway system is the critical bottleneck in long term airportcapacity, thus enhancing airportcapacity means adding new runways and possibly a lengthy plan approval process. Therefore, the purpose of this paper is to present an econometric model of runway expansion delays at airports that are operating near or at their capaci- ty limit. A runway expansion delay means that runway capacity is insufficient to meet the actual demand and results in modification of demand, e.g. a tem- poral or regional demand shift or a demand loss. The model is based on the idea, that the main driver of runway expansion delays is the opposition from the population surrounding the airport caused by the noise emissions. The degree of opposition depends on various factors like e.g. welfare level, num- ber of aircraft movements and location of the airport with respect to the urban agglomeration. Depending on those factors, the degree of opposition at an airport may range from marginal opposition to such a degree of opposition that building a new runway is virtually impossible. The model is based on dis- crete choice and Markov chain models and calculates the expected time span of delayed runway expansion at a congested airport. In a case study we com- pare a scenario of unconstrained 3.5% per annum growth of aircraft move- ments at the largest 100 airports worldwide with a scenario in which capacity constraints and delayed runway expansions are included.
CFD has become a mature tool supporting the consistent investigation of wake vortex behavior under various environmental conditions and in ground proximity and even specific phenomena like the formation of double rings or vortex bursting have become tangible. However, CFD has not yet had a significant impact on aircraft spacing for airportcapacity enhancement. This leads to the question what will be required to give safety regulators more confidence in CFD for safety purposes. The advantage of CFD is that almost all variables of interest are readily available for analysis. Although benchmarks between different simulation codes feature satisfactory agreement for various scenarios, substantial differences for vortex behavior in, for example, turbulent environments remain depending on the characteristics of the adopted turbulence. First examples of consistent consideration of the aircraft type, configuration and flight phase are emerging but need further development and validation to become a means for reliable assessments of specific scenarios.
While we have seen a strong growth of air traffic world wide in the past, and can expect a continuation of growth for the long term future we have to take note of the fact that some important airports are faced with capacity constraints so that airlines have problems in scheduling the planned traffic in the preferred way. There are many airports with traffic volumes that reach capacity only in certain peak times, for instance in some morning and evening hours, however, there are also airports with high traffic loadings which experience near capacity utilisation during many hours of the day every day, like London-Heathrow, Frankfurt, R. Reagan Washington National, or New York LaGuardia, and others. On the other hand, there are many airports, in fact the great majority, with low traffic volumes and no capacity problems. The question is whether or not airportcapacity constraints become such a problem in the global air transport network as to form a barrier to future growth of demand. The objective of this contribution is to give some statistical insight into the global capacity constraint situation and discuss in a more abstract way potential measures of remedying capacity shortages and describe options of how to incorporate these measures in long term forecasting of air transport demand and supply.
An important question in forecasting air traffic at airports is: How does the future traffic volume compare with capacity, i.e. will the forecast demand for using airport infrastructure not exceed the existing or planned capacity? We concentrate in this paper on air transport movements (ATM’s) and runway capacity, since runways form in many instances the airport component most critical for expansion, due to environmental constraints. Air traffic forecasts like those of aircraft manufacturers typically yield annual volumes, whereas capacities are measured more often in short time periods, i.e. hours. Annual “capacities” are used for long term planning purposes as a measure of available service volume as well, however, not for measuring the true through-put of the system. In answering the question of conformity of the demand (here in ATM’s) with capacity, future annual volumes have to be converted therefore into peak hour volumes and then compared with capacity. The paper discusses the problem of selecting a suited peak hour and of defining capacity. For capacity estimation, simulation, functional relationships, consensus processes (in the case of declared capacity) and/or benchmarks may be used. Based on OAG data, the paper informs on the annual capacity utilisation of airports world wide in form of “ranking functions”, which show the distribution of the number of hours at each volume level (between zero and highest hourly volume) over all hours of operation within a year. These functions are the basis for deriving functional relationships between peak hour and annual ATM volumes for each airportcapacity type, which are presented and discussed in the paper.
Typically, for airports with up to two or three runways, the option of directly investing into runway systems would clearly bring the greatest capacity gains of all options, in some regions of the world, however, such as in the London area, no enlargement of airportcapacity has been realised in the last decades, in spite of many political propositions, whereas other regions, such as China or the Middle East, have experienced rapid extensions of runway systems or the construction of new airports. In many other instances, in particular in Europe, new facilities, especially runways, have been added only with great delays, caused by strong opposition of the population living in the vicinity of airports. These delays have been in the order of up to 20 and more years, a time span in which air traffic may have doubled. In such situations public authorities, airport operators, airlines and Air Navigation Service Providers (ANSP) have to look for measures which optimise the throughput of facilities rather than increase the capacity, by applying in broad terms non-investment options, like operational, regulatory or pricing options.
The purpose of this paper is to present an econometric model to forecast runway expansion delays. The focus of the model is on runway capacity, because in the long run this is the most critical bottleneck in airport expansion plans and typically requires lengthy plan approval procedures in many countries. It is not rare that runway expan- sion plans at large airports like e.g. Frankfurt take up to 10 years or even more until they are finished. We briefly outline the global airportcapacity utilisation situation to motivate the research presented. After describing the model in detail we construct a “simple” 20 years forecast by applying a rather conservative 3.5% CAGR to airport traffic values of the year 2012 and identify possible capacity gaps at airports. However, this approach is not a true forecast in itself, because it lacks differentiation of growth rates between regions of the world and redistribution of traffic that exceeds airportcapacity. The approach serves to identify possible gaps in runway capacity with regard to the underlying growth scenario.
For both tasks questions like how to define and describe airportcapacity, how to measure aircraft delays and how to identify capacity influencing factors and to improve the capacity situation arise. For a given airport these methodological and data problems may be solved, for a global analysis there is a high probability that this task is bound to fail. The main reason is that comparable delay data of flights in relation to the degree of capacity utilization are missing for all airports in the global network which may have to struggle with capacity problems. Never the less, our research has a global dimension. The wider objective is to describe the level of capacity constraint in the global airport network; in this study we pursue a methodological objective, the measurement of airport constraint.
• Airportcapacity constraints dampen the number of flights at certain airports, resulting in lower growth rates (and lower noise & emissions) compared to unconstrained forecasts. However, there is a large difference between constrained and unconstrained forecasts for airports with capacity
Table 3 shows the results of model application to the demand forecast. To fulfill the demand forecast 107 new runways at specific airports are needed until 2032. The unconstrained de- mand forecast serves as a benchmark, against which the three scenarios with airportcapacity constraints are compared. In the most likely scenario, 70 of those 107 runways needed are realised until 2032. This means that there is a capacity gap of 2.77% of aircraft movements compared to the demand forecast. As a result, the CAGR corrected for capacity constraints is reduced from 3.50% to 3.36%. Altogether, there are 95 delayed runway expansions until 2032: 70 are realised until 2032 and 25 are still in progress (marked as “delay ‘in progress’ until forecast horizon” in Figure 6). Adding a new runway is on average delayed by 11.2 years with a standard deviation of 24.2 years. The high value of the standard deviation illus- trates the uneven distribution of delays: There are 21 runway expansions that are delayed by 10 years or even more, but about 60 runway expansions are delayed by 5 years or less.
This paper examines the performance capability of prioritisation principles in the case of the operation of a runway. From a transport perspective, traffic throughput and effective delays are of particular relevance. The flight plan borrowed from the reality is also executed completely as a pseudo demand in all simulated scenarios. Through utilisation of the low-load night hours, all requesters are always served; sometimes, however, with impractical individual delays. The influence on the achievable traffic throughput can therefore not be assessed, as demand is capped. Instead, particular attention is paid to the quality of the operation. For each scenario, a comparison is made of the amount of additional delay which would be produced if the assumed demand were to occur in exactly the form in the reference flight plan. From the difference between ETA and ATA, the Airborne Flight Delay AFD is calculated for each planned flight. However, as the aircraft arrive at the destination airport with differing amounts of delay and, for example, connecting flights or ground transport modes, which need to be reached, are linked to one another on the basis of planned values, it is equally important to find out how the prioritisations affect the overall punctuality. This balance of STA and ATA, known as Total Delay TD, therefore supplements the approach. In the case of negative indicators, both AFD and TD are set to zero in order to rule out an effect-reducing influence of the too-early arrival on the mean value formation. These two delay values for TD and AFD are applied in the aircraft- related analysis of the effects. From the perspective of the passengers, this may seem unfair, as a different quantity of passengers could be affected per flight. The Total Seat Delay TSD and Airborne Seat Delay ASD indicators represent the respective corresponding products of available seats and aircraft delay. The evaluation of the delay values is accompanied by the comparison with aviation-standard benchmarks. The 15-minute criterion decides whether a flight is punctual or unpunctual. This punctuality is measured at the gate and not on the runway; a corresponding delay on the runway can, however, generally no longer be made up for by the time of reaching the gate position. Furthermore, a 30-minute benchmark exists. This value is derived from the requirement of the flight planning, which states that flights should carry enough reserve fuel for a 30-minute holding pattern. Airborne waiting times which go beyond this therefore potentially pose a risk of emergency caused by fuel shortages.
Figure 5. The AirportCity growth cycle
Source: Schiphol presentation
Due to the required time for expanding facilities, it can only be done stepwise and In this case the airport takes the economic risk on the investment. The structure and business model of the airport will in turn provide answers on the question of if the airport is capable to invest in capacity expansion programmes (either new infrastructure or optimisation of existing). Schiphol Airport is a state-owned company that has direct access to the financial markets; before the financial crisis in 2008 the airport had a AAA-status and in 2016 the airport has an A1-status. 4 Until now Schiphol has been able to invest in new capacity timely and was able to get a positive return on its investments. The new status of Schiphol implies that more risk is put into the investments therefore they have to be evaluated carefully in order to avoid potential economic downturns. This in turn may cause that investments in airportcapacity expansion will be delayed or limited therefore new capacity will be available at a later stage.
Pricing slots is strongly opposed by airlines who perceive such an approach as another way for the airport or the ministry in charge (or whoever sells the slots) to erode airline profit margins.
Some participants remarked that auctioning slots could potentially solve the challenge of funding new airportcapacity. Government could sell slots and use the funds to develop new infrastructure or compensate local communities for the adverse environmental impacts of aviation (such as noise or pollution). For such a mechanism to be effective it would have to be subject to regulatory oversight to contain any tendency for gold-plating airport facilities. Where expansion is not feasible, revenues from slot auctions should be regarded as a public resource, accruing to the public exchequer rather than the airport. In the literature, an argument that airports could finance new infrastructure through slot sales is pitched against the argument that the airport may be incentivised to ensure that the infrastructural development lags behind demand in order to maximise the rents collected from selling scarce airportcapacity (see, for example, Sentance, 2003).
The principle of individual utility maximisation is employed to allow for capacity constraints within an airport and access mode choice model based on discrete choice analysis. The main idea is to minimise the loss of personal welfare of an air traveller caused by limited airportcapacity to handle air travel demand. The central assumption of the model is as follows: The more unequal an air traveller prefers the alternatives in his choice set the greater are his efforts to depart from his favourite airport. This relation is described by the utility differences (V i –max(V j ), i≠j) in figure 3.
The TSLS coefficients on the change in airport size are larger in magnitude than the OLS co- efficients for all instruments and for both employment and GDP. The results from the Hausman tests show that the differences are generally significant for employment though not for GDP. The TSLS coefficients being larger than the OLS coefficients deserves some explanation as it suggests negatively-biased OLS coefficients, the opposite of what one would expect if employment has a positive effect on airport size. However, this phenomenon is common in studies that use instrumen- tal variables to estimate the effects of transportation infrastructure on economic outcomes. Two possible explanations offered by Duranton and Turner (2012) are that consumption amenities are missing from the estimation and correlate negatively with initial infrastructure, and that there may be reverse causality whereby employment negatively affects infrastructure. The first of these seems
Figure A.3: Automatically generated airport overview and parking maps of Kansas City International airport. A modified symbology set for windsocks, airport beacons, and ARP is used, as well as a different color schema for taxiways, taxilines, aprons, and parking stands. Stopbars are de- picted in yellow and larger fonts are used for most label types.
Vienna Enhanced Resolution Analysis ( VERA) is a objective, automatic analysis procedure for meteoro- logical parameters over complex terrain developed by the University of Vienna (Steinacker et al., 1997). It is able to resolve mesoscale structures caused by topography by including meteorological a priori knowledge in the analysis. The scheme is used for both error detection and correction, and interpolation of irregularly distributed data onto a regular grid. The emphasis is put on the transfer of information from data sparse to data rich areas. For this purpose the so called “fingerprint technique” is used. It adjoins additional orographic information to the measurements. The error detection mode checks the single measurements concerning their spatial physical plausibility and calculates correction suggestions where necessary. The method runs independently from any first guess or model field. For the use in Wetter & Fliegen VERA has been installed at DLR/IPA over a domain with reduced size covering southern Ger- many. For winter weather the analysis system can provide information on surface temperature and hu- midity, observed precipitation and especially fronts, where the exact position, movement, strength is of great importance for timely warning and model forecast verification. Figure 4 shows an example of ana- lysed surface temperature. The 0° C contour run close to the airport MUC, dividing colder near ground air to the northeast from warmer temperatures to the southwest. Such information could be quite valua- ble when there is precipitation in the area, thus allowing the estimate of possible freezing conditions at the ground.
When a vehicle is moving across the whole airport surface, it could be breakdown anywhere and cause an accident (e.g. collision) or delays if there is not situational awareness for the rest of vehicles, ATCU and AOPC. In this situation the affected vehicle must send a DV-ALERT service to notify ATCU/AOPC that the vehicle is in a state of emergency or in an abnormal situation. Depending on the situation, new services will be launched such us D-OTIS, DV-PLAN and others like ACL, AOPCL related to new vehicles entry in operation at airport surface.
Current weather conditions are usually recorded at each airport in the form of METARs (Meteorological Aviation Routine Weather Report [ 39 ]). METARs are reported in combination with a Terminal Area/Aerodrome Forecast (TAF). While TAF provides forecast values, METAR data are measured values. The unscheduled special weather report (SPECI) is another format representing significant changes in airport weather conditions. The time of update and the update interval of a METAR weather report are not harmonized and implemented differently worldwide. For example, at larger airports in Germany, a METAR is released twice an hour (20 min past and 10 to the full hour) while, at small sized airports like Moenchengladbach (EDLN), a new METAR is available once an hour only during the operating times of the airports. Current and historical METAR and also TAF data are accessible at different public available websites (such as https://www.ogimet.com ). In addition to information about the location, the day of the month and the UTC-time (“EDDF 190850Z”), the METAR contains information about wind, visibility, precipitation, clouding, temperature, and pressure that are relevant for the air traffic, especially for the airport operations (see Table 2 ).