Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/223764
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dc.titleINTRA-URBAN PUBLIC TRANSPORT CONNECTIVITY AND SINGAPORE HDB PRICES
dc.contributor.authorYEE WEN NA ELLIE
dc.date.accessioned2020-05-05T08:37:18Z
dc.date.accessioned2022-04-22T20:41:32Z
dc.date.available2020-06-10
dc.date.available2022-04-22T20:41:32Z
dc.date.issued2020-05-05
dc.identifier.citationYEE WEN NA ELLIE (2020-05-05). INTRA-URBAN PUBLIC TRANSPORT CONNECTIVITY AND SINGAPORE HDB PRICES. ScholarBank@NUS Repository.
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/223764
dc.description.abstractOver the years, Singapore’s public transport system has undergone significant changes to adapt to the changing landscape and demands. Such changes require complex analysis of transport accessibility and multi-modal connectivity in the policy development process. This paper aims to explore the goals set out in the 2040 Land Transport Master Plan (LTMP) and how the existing transport system performs relative to the objective of having a 45-minute city. In addition, this paper analyses the impact of Singapore’s public transport connectivity through a new approach of utilizing commuter’s travel duration as opposed to linear distance measurements. The travel duration is obtained through the Google Maps API which simulates the route with the shortest duration using Singapore’s multi-modal transport system. The impacts of public transport connectivity will be analysed through its impact on HDB resale prices. A deep neural network model will be built to model the non-linearities of property prices and travel duration. The results obtained showed that travel time from the shortest public transport commuting route has approximately four times more significance in contributing to property prices as opposed to linear measurements. Overall, this study contributes by (a) using travel time as a measure of connectivity to include for qualitative measures, such as transport reliability, waiting time and congestion, along with quantitative measures such as transport frequencies and speed, (b) using the Google Maps API as an alternative source of optimal transport routes and (c) utilizing neural networks to model the relationship between transport connectivity and HDB resale prices. Thus, allowing for a deeper understanding of intra-urban transport connectivity and its impacts.
dc.language.isoen
dc.sourcehttps://lib.sde.nus.edu.sg/dspace/handle/sde/4727
dc.subjectReal Estate
dc.subjectLi Qiang
dc.subject2019-2020 RE
dc.subjectRE
dc.typeDissertation
dc.contributor.departmentREAL ESTATE
dc.contributor.supervisorLI QIANG
dc.description.degreeBachelor's
dc.description.degreeconferredBACHELOR OF SCIENCE (REAL ESTATE)
dc.embargo.terms2020-06-10
Appears in Collections:Bachelor's Theses

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