Methods of air traffic management in the airport area including the environmental factor

In the modern approach to air traffic management (ATM), issues related to air quality and climate protection have led to the introduction of a growing number of new restrictions instead of having these issues be the objectives of actions taken. The aim of the study was to investigate whether it is possible to improve air quality while controlling pollution emission by introducing changes in the organization of air traffic control at the airport. For this purpose, mathematical models of emissions were created. These concern the movement of aircraft near the airport and emission of carbon dioxide and the spread of human health related substances emitted from a landing airplane's engine. The first case refers to the possibility of using different arrival procedures. The second case was to adapt the Pasquill formula by treating landing aircraft as a sequence of point-source emissions. As a result of applying the designed models and software tools, it was proven that both a change in the arrival trajectory and a change of the runway can contribute to a reduction of CO₂ emission into the atmosphere. The emission maps created for different aircraft approach profiles show the ability to control the local concentration of harmful emissions. The study clearly shows that the current approach of maximizing throughput is not beneficial to air quality. ATM services should consider the use of other variants of air traffic organization, particularly during periods of reduced traffic.     

Determinants of daily fluctuations in air passenger volumes. The effect of events and holidays on Milan Malpensa airport

Air traffic flows show large seasonal variability, but arrivals and departures may also be significantly influenced by specific events which generate peaks, which generate peaks rising above baseline traffic. While seasonal variations of air flows are well studied in literature, the daily variations and their causes are seldom analysed and quantified. The paper aims at filling this gap by exploring and quantifying the effect of holidays and events (conferences, trade fairs, sport events) in terms of passenger daily fluctuations.We identified the elements affecting these variations and searched for correlations with daily demand fluctuations using an OLS econometric model applied to Milan Malpensa airport. The model allows one to reproduce the observed daily traffic, identifying the baseline component of traffic (depending on the calendar) and the additional effect ascribable to holidays and occasional events.Results show which types of events generate a visible traffic increase. The effect of some of them can be very significant indeed. The largest international design and fashion shows taking place in Milan generate up to more than 20% extra passenger traffic compared to the normal baseline traffic. In addition, the analysis showed that their effect is not limited to the event days, but impacted on the surrounding days as well. Holidays also influence the patterns of demand, creating additional traffic on certain days and more pronounced peaks, which also differ according to seasons.     

Differential pricing strategies of air freight transport carriers in the spot market

The single pricing approach fails to reflect different demand changes in response to its cargo space prices at different sales periods. This might be an obstacle to maximize the cargo space revenue of air freight carriers. This paper conducts the empirical analysis on pricing strategies in the spot market for two competitive air freight transport carriers operating on the same segment. It concludes that two carriers gain more revenues with the optimized differential pricing approach than with the single pricing approach. In theory, the research enriches the current literature in terms of pricing strategy optimization under the competition of two parties. In practice, it provides references for air freight transport carriers in their decision-making of applying the differential pricing strategy.