مقاله سال 2016

These days many people would agree that airports should invest in enhanced facilities and higher levels of service quality in order to attract passengers, thereby generating significantly higher nonaeronautical revenue, which is critical to airport profitability. Merkert and Assaf (2015) even argue that service quality is a significant performance indicator to an airport and should be treated with the same level of importance as profitability.

The issue of airport service quality has been widely examined in airport literature. For example, Yeh and Kuo (2003) evaluated the service quality of fourteen major Asia-Pacific international airports using a fuzzy multi-attribute decision making approach. Arif et al. (2013) assessed customer satisfaction at the United Arab Emirates’ three airports. Transfer passengers’ experiences at Bandaranaike International Airport in Sri Lanka were studied by De Barros et al. (2007). In Australia, Melbourne Airport was once ranked within the top 5 airports that handled 15e25 million passengers according to the Airport Quality Service Survey conducted by Airports Council International (ACI) in 2008, but not any longer since then. In 2011 it did not even make the top 100 according to ACI’s rankings. The ACCC (2015) has found that service quality at all the monitored Australian airports has declined in the last decade in part due to the fact that customers’ expectations are constantly changing, and Melbourne airport received the lowest quality of service rating amongst Australian airports in the last few years. This research selects Melbourne airport as a case study in order to reexamine the service quality issue using both expectation and perception data to identify areas requiring focus and investment of resources, so as to deliver satisfactory services that fulfil the needs and expectations of airport passengers.

مقاله سال 2016

This paper proposes a novel data envelopment analysis (DEA) model for evaluating the performance of Iran’s airlines. Our proposed model not only ranks all Iranian airlines but also fully uses all inputs and outputs. To prevent alternative solutions, Sueyoshi and Sekitani (2007) integrated DEA and strong complementary slackness condition (SCSC) and proposed the DEA/SCSC model. Nonetheless, the DEA/SCSC model does not guarantee that the ties among efficient decision-making units (DMUs) are broken. To rank efficient DMUs, Sueyoshi and Sekitani (2007) used DEAediscriminant analysis (DEAeDA). Barros and Wanke (2015) used the technique for order of preference by similarity to ideal solution (TOPSIS) to assess the relative efficiency of African airlines. Merkert and Pearson (2015) developed a new approach for measuring the impact of an airline’s customer service on profit. To calculate the efficiency scores of airlines (DMUs), this paper integrates one of the DEA models called range-adjusted measure (RAM) and SCSC. To rank DMUs, DEAeDA is applied.

مقاله سال 2016

Air Traffic Services (ATS) systems constitute an essential support element of the Air Traffic Management (ATM) system. Such systems enable a safer and more efficient operation of air transit by integrating information from a variety of sources such as flight plans, Communications, Navigation and Surveillance (CNS) systems or aeronautical and meteorological information providers. In addition, modern ATS systems automate routine functions, thus contributing to a reduction of the operator workload. Hence the importance of acquiring proper ATS systems and optimizing its performance throughout its life-cycle.

Commercial Off-The-Shelf (COTS) software has proved to be one of the principal factors influencing decision making when it comes to the procurement of new Information and Communication Technology (ICT) systems or upgrades for legacy systems.

COTS software is commercial software that can be built into and integrated within other software projects. It has been widely studied in the general domain given the supposed benefits they offer to speed up the development process and reduce overhead costs. Some studies focused on proving the advantages of COTS software solutions over bespoke ones (Blanchette, 2005; Morisio et al., 2002). According to those studies, COTS solutions do not always reduce the investment of time and effort required to successfully complete the project, rather they change the way the project has to be handled. Greater focus has been put on the integration issues of COTS software such as detection and solution of interoperability problems (Wile et al., 2010; Couts and Gerdes, 2010).

مقاله سال 2016

As alliances can improve profitability and market share, they enjoy increasing popularity in the airline industry (Topaloglu, 2012). Between 2003 and 2010, the number of alliance members grew by 60% (Hu et al., 2013). Most large and mid-sized network carriers are engaged in one of the three big alliances e Star Alliance, Oneworld and SkyTeam (compare Table 1). The case of Lufthansa and StarAlliance as examined later in this contribution underlines this increasing diffusion of code-sharing. Competition among alliances is replacing competition among individual airlines, as each alliance strives for the best customer service and the most extensive network.

The success of alliances is explained by their benefits. Airlines use the alliance’s brand awareness to attract more passengers and to access partners’ infrastructure – compare Youssef and Hansen (1994) for an exemplary analysis of the case of SwissAir and SAS. Alliances establish standards of safety, technical equipment, and customer service. They promise passengers better connections, higher service quality, more lounges, and frequent flyer miles – see Goh and Uncles (2003) for a critical analysis. However, alliances also challenge airline planning: They increase process complexity, which can negatively impact performance.

This contribution highlights alliance challenges particularly for revenue management. Revenue management describes the art of selling the right seats to the right customers for the right prices at the right times (compare Smith et al., 1992). It is a central component of the airline planning process: Given a demand forecast, optimization determines revenue-maximizing inventory controls, which are implemented in the sales process.

مقاله سال 2016

There is a large literature on how noise (or conversely; peace and quiet) is capitalised into housing values. This literature is important as to understand the social cost of noise pollution from different transport modes, which in turn, is important to determine what investments in noise abatements should be prioritised. However, within the body of literature one can find a large spread of estimates of the costs associated with noise pollution. This paper aims at partially explaining these differences by looking at potential differences in transport modes (road and air) in relation to costs associated with noise pollution. One large benefit directly resulting from this approach is that when establishing the relation between transport modes, planners can predict the cost of aircraft noise pollution when only estimates for road noise pollution are available, and vice versa. The findings suggest that the cost of aircraft noise is larger, all else equal, than the cost of road noise. To which there may be several reasons, one being the difference in intensity and unpredictability between the two transport modes. Road noise is normally an ongoing event (when you live in proximity to a road) while aircraft noise is attributed to the landing and take-off cycle. Differences in exposure to noise stemming from different transport modes have been examined in the medicinal field. For example, Stansfeld et al. (2005) argue that aircraft noise has a greater impact on children’s reading capabilities than road noise. Basner et al. (2011) found differences in sleep patterns related to the source of noise pollution.

erences in sleep patterns related to the source of noise pollution. Peace and quiet are goods that cannot be purchased on the open market, yet they are in high demand. This results in a lack of available observable prices for these goods. The common denominator of the included studies of this paper is that they all have approached the valuation of the social cost of noise in the same way, namely though estimation of the Noise Depreciation Index (NDI) using the hedonic modelling framework, where noise levels are associated to property values (rents or prices). The approach is not without its problems but it gives good estimates of the social cost of noise pollution. The included studies do of course differ in a number of ways. Early studies of aircraft noise used Noise Exposure Forecast (NEF) as the measurement of noise (Abelson, 1979; Maser et al., 1977; Nelson, 1979; O’byrne et al., 1985) while estimations ofroad noise have traditionally used different types of equivalent levels (Leq, LAeq) or noise levels throughout the day (Ldn, Lden) as measurement (e.g. Bateman et al., 2001; Grue et al., 1997; Renew, 1996). Later, studies of aircraft noise adopted the same measures as road noise studies. Studies also differ in terms of data availability. Some rely on aggregate prices at, for example, the census tract (e.g. Nelson, 1979; Price, 1974; De Vany, 1976). With better data availability, later studies have access to more disaggregated data and focus on individual sales (e.g. Brandt and Maennig, 2011; Blanco and Flindell, 2011; Levesque, 1994) or rents (e.g. Baranzini et al., 2010; Püschel and Evangelinos, 2012). Furthermore, studies may differ in terms of model specification where most use a semilogarithmic approach, with a few exceptions. These differences, aside from the mode of transport under study, may all be causes of the differences in estimated costs of noise pollution.

The remainder of the paperis structured as follows. In Section2the most common approach to estimating the cost of noise pollution will be described. In section 3 the meta-analysis performed here is discussed, with the results presented in section 4. Section 5 concludes.

 Assessing performance variability of ground handlers to comply with airport quality standards

مقاله سال 2016

Ground handling covers a wide range of services that support airline air operations. It may include both technical services, such as maintenance, fuel & oil services, freight handling, and passengers’ essential services, i.e. check-in, disembarkation, surface transport, baggage handling, comfort.

This industry experienced a very dynamic development and growth, since the introduction of Council Directive 96/67/EC (EU, 1996). In accordance with the liberalisation of the air transport, the Directive aims at developing the competition among players to increase the efficiency of the ground handling activities, to decrease the average costs, increase the quality levels of services and enhance the choice for airlines (Soames, 1997). The definition of quality, which has to include safety matters, baggage delays, environmental matters, etc., evolves in a two-party service level agreement between airlines and ground handlers.

greement between airlines and ground handlers. After the Directive’s implementation, Airport Research Center (2009) showed a decreasing trend on prices and an increase of competition, given the large number of third party handling companies entering the market. Anyway, statistics on quality level were unclear. While many airport managing bodies and airlines are generally satisfied, others observe that quality levels suffered from this competition on costs (Costantino et al., 2013).