There are many outdated routines and processes in today's aviation industry that major airlines lack the motivation to update. While this may not hold any direct security concerns, it creates bottlenecks at checks and high salary costs for otiose airport personnel. This study aims to together with the company Objective Solutions examine the possibility to increase the costeffectiveness in the security checks at Arlanda, tested on terminal 5, using a machine learning model which would serve as the basis for the scheduling of personnel. When performing this study, appropriate model alternatives were identified based on model characteristics and the task given. Three models were extensively explored and developed, Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX), Holt Winters Exponential Smoothing (HWES) and Long Short Term Memory (LSTM). These were tested using real data collected from the airport database obtained through SQL. The model was built using Python in the Google Colab platform, the data was first handled and restructured and was then run through the different models with equal prerequisites. The models were evaluated using three different measuring tools; Mean Squared Error (MSE), Mean Absolute Error (MAE) and graphically. One of the models, Long Short Term Memory (LSTM), showed better accuracy than the others and was deemed successful in fulfilling the defined objectives of accurately identifying and predicting trends over the desired time period of two months. While this model was successful in reaching the defined requirements such as identifying trends and irregularities, the stochastic design of it entailed some instability which sometimes generated shifting results between runs, and it is up to Objective Solutions to decide if it is deemed appropriate to finalize the model into an end product ready for practical implementation.

This diploma thesis is focused on modernization of traction passenger lift TOV 250. The first part diserts on todays technical conditions of lift. The second part is concerned with way of modernization of lift and the last third part is focused on basic static calculation of construction traction passenger lift.

Over the last decade, Ubiquitous Real-time Passenger Information (URTPI) has become popular among public transport passengers. The effectiveness of URTPI and hence the value of the investments into the necessary systems can be increased with a clear understanding of how URTPI influences passenger behaviour. However, such an understanding is still limited and fragmented. In particular, very little is known about the impact of URTPI on route choice. This study fills this gap evaluating the impact of URTPI on bus passengers' route choice. A revealed preference survey methodology was adopted for data collection and two questionnaire surveys targeting bus users were carried out. Categorical Regression and discrete choice models, such as Binary Logit Model and Multinomial Logit Model, have been applied to analyse the survey data. The study reveals that trip length, passenger age and profession are the main factors influencing the use of URTPI.Having access toURTPI, the frequency of its use is strongly influenced by the attributes of information and social norms. Bus arrival time and bus stop location are the two most important contents of information. Changing time ofdeparture from the start and the boarding time are the two most popular actions taken by bus passengers after consulting URTPI. Passengers' decisions are influenced by information on bus arrival time, bus route, and walking distance. As a result of the impact of URTPI on passengers' choices, the demand distribution for bus runs could potentially be changed by 33% and for bus lines by 22%. The overall network demand distribution could be affected in 42% of cases as a result of consulting URTPI.This study implicates that while investing in tailoring the sources of URTPI, passengers' preferred attributes and contents of information should be considered. Transport planners and operators should take the potential impact of URTPI into account to make better predictions of the PT demand distribution.