Caracterització de la demanda del servei d'atenció a passatgers de mobilitat reduïda en un aeroport emprant xarxes neuronals
Tutor / Supervisor
Student
Esteban Rodríguez, Alex
Document type
Bachelor thesis
Date
2024
rights
Open Access
Publisher
Universitat Politècnica de Catalunya
Degrees
UPCommons
Abstract
People with reduced mobility (PRM) have the right to accessibility at the airport, which can be ensured through a dedicated service for this community. However, this service impacts airport operations and is increasingly relevant due to the growing demand. Therefore, demand forecasting is necessary to provide quality service and optimize airport resources. This is challenging because less than two-thirds of PRMs notify in advance of their need for the service. A model based on neural networks is proposed to predict the number of PRM passengers, which determines the amount of resources the airport needs to prepare for each hour of the day. To this end, data from an anonymous airport over a specified period is used, including information on each flight, including the amount of PRMs separated by categories. The network is developed using Python, employing libraries such as TensorFlow and Keras. Data analysis is performed using Excel, with tables and graphical representations. The results show that the network is accurate when predicting a high volume of PRMs. A detailed analysis focuses on January and July, with average relative errors of 16% and 11% during peak hours, respectively, compared to the 36% error rate that would result from only considering prior notices.
