Análise da Capacidade do Estacionamento para Professores e Servidores do Instituto Federal de Goiás - Campus Goiânia utilizando Teoria de Filas

Abstract

The Federal Institute of Education, Science and Technology of Goiás – IFG, is located in the central region of Goiânia, an area of ​​the city that presents several problems related to traffic, such as congestion, accidents, pollution, etc. As in other metropolises, the city has increasingly prioritized automobiles, generating a high demand for parking spaces. The Goiânia Campus, the subject of this undergraduate thesis, has experienced remarkable growth in the number of courses and students since the 2000s, expanding beyond technical education to higher education, which represented an increase in the number of faculty members to meet this demand. This expansion also increased the need for parking on campus for professors and staff. Thus, this work sought to analyze the capacity of the parking lot intended for professors and staff of the Federal Institute of Goiás (IFG) - Goiânia Campus, using Queueing Theory. The methodology is quantitative and descriptive, using statistical techniques for data analysis. The data was collected through parking lot monitoring images on November 18, 2024 (Monday) and January 28, 2025 (Tuesday). Based on the data obtained in the field research, it was possible to dimension the parking lot intended for professors and staff of the Institution, using the M/M/c model, chosen for this study. This model was adopted because it is suitable for analyzing queuing systems with multiple servers (in this case, parking spaces), with random arrivals and service. The technical analysis demonstrated a minimum need for approximately 104 parking spaces for system stability. In order to improve the quality of the service offered, parking spaces were dimensioned based on three possible scenarios. The first scenario (ρ=1.00) establishes 104 spaces as sufficient to eliminate the current deficit, operating without a safety margin. The intermediate scenario (ρ=0.85) requires 123 spaces, incorporating an operational reserve of 18% aligned with institutional standards. The optimal scenario (ρ=0.75) projects 139 vacancies, providing a 33% margin that ensures full absorption of fluctuations and future demand growth.