Public transport demand modeling for Metro Manila

Metropolitan Manila, the capital region of the Philippines, is composed of 16 cities and a municipality. Currently, Metro Manila has four urban and commuter rail systems, LRT 1, LRT 2, MRT 3, and the Philippine National Railway (PNR). In addition to these rail systems, there are also road based tran...

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Bibliographic Details
Main Author: Ting, Sean Johnlee Q.
Format: text
Language:English
Published: Animo Repository 2015
Subjects:
Online Access:https://animorepository.dlsu.edu.ph/etd_masteral/5139
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Institution: De La Salle University
Language: English
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Summary:Metropolitan Manila, the capital region of the Philippines, is composed of 16 cities and a municipality. Currently, Metro Manila has four urban and commuter rail systems, LRT 1, LRT 2, MRT 3, and the Philippine National Railway (PNR). In addition to these rail systems, there are also road based transit, provided by public utility jeepneys (PUJ), public utility buses (PUB), and Asian utility vehicles (AUV), or commonly known as FX. The known demand of public transportation from the Metro Manila Urban Transportation Integration Study Update and Capacity Enhancement Project (2012) study is the updated trips generated and trips attracted per traffic analysis zone (TAZ) in Metro Manila and neighboring provinces. However, changes in socioeconomic and land use data will also change the demand for public transportation. Therefore public transportation demand models were developed using two approaches, multiple linear regression (MLR) and artificial neural network (ANN). Results show that ANN models were far better in terms of fit than MLR models. Bus demand from the bus questionnaire survey was then compared to MUCEP bus data. Results show that the data from survey and MUCEP has small difference in terms of the percent share per city. Future forecast of demand per public transport mode for the year 2020, 2025, and 2030 were also executed using both ANN and MLR models. Recommendations include a better and more accurate socioeconomic data from the local government and a better system of survey using technologies and softwares in order to have faster data processing and analysis.