Detalhes bibliográficos
| Ano de defesa: |
2019 |
| Autor(a) principal: |
JESUS, Angélica Lopes de
 |
| Orientador(a): |
ALBUQUERQUE JÚNIOR, Gabriel Alves de |
| Banca de defesa: |
CALLOU, Gustavo Rau de Almeida,
DANTAS, Renata Cristine de Sá Pedrosa |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal Rural de Pernambuco
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Informática Aplicada
|
| Departamento: |
Departamento de Estatística e Informática
|
| País: |
Brasil
|
| Palavras-chave em Português: |
|
| Área do conhecimento CNPq: |
|
| Link de acesso: |
http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/8563
|
Resumo: |
More than commuting and transportation, urban mobility is an interaction of the movement of people and goods with the city. However, the growth of cities and the concentration of people in urban areas make it difficult to provide a quality mobility service in urban centers. Overcrowding, long travel times and delays are some of the problems of public transport. In addition to negatively impacting the productivity of industries, it can damage citizens’ health. Using modeling techniques in a system performance analysis allows participants to interfere with their operation, test the inclusion of new components, and compare resource alternatives. The aim of this work is to evaluate the performance of systems public transport to optimize resource use. For this, two models, with different formalisms (SPN and CPN), were developed. The proposed methodology consists of a sequence of steps ranging from understanding the system to evaluating the results obtained from the model.This methodology can be applied to different transport (or modal) systems that have an intermediate stop and queue structure between the origin and destination point. A case study was performed using the methodology and the proposed models. The study showed that, considering the input parameters analyzed, the arrival time, the departure time of the buses, the number of vehicles in the fleet and the average bus speed, the average bus speed average bus speed is the main factor affecting travel time and number of buses is the factor that most influences waiting time and average queue length. In addition, in relation to bus capacity, the highest percentage obtained was 79.2% when the experiment was performed with 4 buses in circulation, an interval between trips of 13 minutes, the time passenger arrival at each stop of 1, 5, 7 and 11 minutes and the average speed reached by buses of 15 km/h. The lowest percentage of capacity found was 12.3% obtained with the use of 15 buses in the fleet, a 7 minute travel interval, 3, 9, 14 and 20 minute arrival time at each stop and average speed of 25 km/h. Given the current system situation, according to CTGR data used in the case studies, a 10 km/h increase in average bus speed would reduce waiting time by 39%, queue length by 44.9% and travel time by 38.4%. |
