Forecasting model development and application in the aviation industry

Forecasting models have been applied to many industries as a decision-making tool for over 100 years. Their application in the aviation industry benefits a wide variety of stakeholders such as airliners and airport authorities, who use past data to predict demand and passenger choices so that they can better define fares, manage their fleet and make decisions on the airport layout and future expansions, among others. The main objective of this dissertation is the development of a forecasting model capable of predicting the number of flight movements at Lisbon Airport. The model was based on an autoregressive model, which uses past data in order to forecast future figures. Weekly data regarding the flight movements at Lisbon Airport was the sample for this study, which was processed through RStudio programming software. Once the Autoregressive Moving Average (ARIMA) models were defined, the forecasting data was created and further tested for accuracy using extant data. The impact of COVID-19 had to be considered in this situation, leading to the breakdown of the original time-series into three different samples. The forecasting models were subsequently created through each of these models. The results were expressed through the three different models, and since two of them have extant data, meaning an existing sample to compare the predicted data, it was possible to determine the accuracy level. However, these models cannot be applied immediately since the impact of COVID-19 is still present and flights have not resumed normality. Once this normality resumes, the models can be applied.Modelos preditivos têm sido aplicados a variados setores como ferramenta de tomada de decisão há mais de 100 anos. A sua aplicação na indústria aeronáutica beneficia uma ampla variedade de interessados, como companhias aéreas e autoridades aeroportuárias que utilizam dados para prever a procura, definir preços, gerir frotas e tomar decisões relativas ao layout do aeroporto, expansões futuras, entre outros. O principal objetivo desta dissertação é o desenvolvimento de um modelo de previsão capaz de prever o número de movimentos de voos no Aeroporto de Lisboa. O modelo foi baseado num modelo autorregressivo, que utiliza dados passados para prever valores futuros. O Aeroporto de Lisboa foi o objeto escolhido para esta dissertação. Dados semanais relativos aos movimentos aéreos no Aeroporto de Lisboa consistiram na amostra para este estudo, os quais foram processados através do software de programação RStudio. Assim que os modelos Autoregressive Moving Average (ARIMA) foram definidos, os dados de previsão foram criados e testados quanto à precisão usando os dados existentes. O impacto do COVID-19 teve que ser considerado nesta situação, levando à divisão da série temporal original em três amostras diferentes. Os modelos de previsão foram posteriormente criados através de cada um desses modelos. Os resultados foram expressos através dos três modelos, e como dois deles possuem dados existentes para comparação com dados previstos, foi possível determinar o nível de precisão. No entanto, os modelos não podem ser aplicados imediatamente, uma vez que o impacto do COVID-19 ainda está presente e os voos não voltaram à normalidade. Uma vez resumida essa normalidade, os modelos podem ser aplicados

Updating Cruise Tourism Theme: A Methodology of Systematic Literature Review

The aim of this study was to synthesize the major themes of research studies conducted in the context of cruise tourism in the period of 2015–2023 in an international research database (Web of Science – ISI SSCI) to provide an up-to-date review of the relevant literature and identify any associated academic gaps for future research using a Systematic Literature Review (SLR) synthesis process method. The study’s findings may be categorized into the following five major topics: 1) Customer Research, 2) Supply-Side Research, 3) Cruise Ship Research, 4) Overview Research, and 5) Employee Management Research. According to the results of the study, future research should look at the connection between cruise passengers’ behavioral intentions and their perceptions of the value of their trip, their perceptions regarding cruise ports and destinations, and their overall satisfaction. For scholars, cruise destination managers, and decision-makers in the field of cruise tourism development, the findings offer both theoretical and practical insights and recommendations

Machine Learning for Forecasting Future Reservations’ Ratings : Radisson Blu Seaside in Helsinki

In the current age of internet and big data, it is imperative for hotels to enhance their online reputation to remain competitive and profitable. This research presents a new perspective on how hotels can maintain and improve their online reputation through the use of machine learning techniques to predict the ratings of reservations. The approach involves analysing data that customers provide when booking a room. Additionally, the study explores how insights gleaned from online textual reviews can be used by hotel managers to address negative ratings. The study's primary objective is to assess the effectiveness of machine learning in predicting negative instances, a critical factor in managing online reputation. The best performing models achieved a 60% accuracy in classifying negative instances. However, increasing the number of predicted true negative instances also increased the number of false negative instances. This result was primarily due to the unpredictability of customer behaviour, making it difficult to accurately predict ratings. Despite not achieving the desired result, this study presents a novel direction for future research and provides suggestions for future research ideas. By utilizing machine learning algorithms to analyse customer data, hotels can better understand their customer's preferences, allowing them to improve their online reputation and ultimately improve their bottom line