Predicción de la demanda de pasajeros a clústeres de estaciones del Metropolitano usando métodos de Data Mining, la metodología Box-Jenkins y Sarima

The level of passenger demand for the Metropolitan service has increased and the planning called JICA, currently used, is not enough, causing the saturation of passengers in their 38 stations. According to experts and reports made by the Metropolitan Municipality of Lima and ProTransporte in 2018, claim that the maximum station capacity of 700,000 passengers was exceeded daily, which was planned, being twice as much as 2010 and suggesting updating demand planning. So, it was proposed to predict the passenger demand of station clusters using SARIMA from a spatio-temporal analysis using two data mining methods and the Box-Jenkins methodology to get the best possible model for cluster. The results of the spatio-temporal analysis showed similar behavior between stations when grouped into clusters with weekly seasonality. The models didn't make a correct prediction for the annual holidays, as they were interpreted as outlier´s values, so the demand that recorded these dates was replaced to make the models more accurate; finally getting good results with a RMSPE, MAPE and ¿ 2 between 6.37% - 8.13%, 4.19% - 5.93% y 0.91 - 0.98 respectively between the four models, below the ceiling for each forecast metric that was proposed as targets. Despite the problem, model predictions can be used to optimize the Metropolitan's resources in the distribution of its buses, adequately taking care of the demand that saturates its stations, not counting the annual holidays.

Name thesis:degree_name

Ingeniero de sistemas

Level thesis:degree_level

Título Profesional

Discipline thesis:degree_discipline

Ingeniería de sistemas

Grantor dc:publisher

Universidad de Lima

Year dc:date.issued

2022

Author dc:creator

• Roque Rojas, Edwin

Advisor dc:contributor.advisor

• Cárdenas Garro, José Antonio

• Transporte de pasajeros
• Minería de datos
• Prospectiva
• Transporte urbano
• Passanger transport
• Data mining
• Forecasting
• Urban transportation
• Lima Metropolitana (Perú)

Statement dc:rights

• info:eu-repo/semantics/openAccess

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-sa/4.0/

Language dc:language.iso

spa

Handle dc:identifier.uri

https://hdl.handle.net/20.500.12724/16675

OAI identifier oai:identifier

oai:repositorio.ulima.edu.pe:20.500.12724/16675