Time-series Analysis on Singapore Public Transportation Train Network Project Overview

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Project Background

Singapore is a small country, yet it has a complex but comprehensive public transportation network. Consisting of train (known as Mass Rapid Transit, hereinafter known as MRT), bus, light and rapid trains (Light Rail Transport, hereinafter known as LRT), and taxis, the public transport in Singapore employs the hub-and-spoke strategy; busses serve as the means of transportation within a town, and MRT trains are used for long distance travel.

The demand for MRT ridership has significantly increased since 1997 as it served as a cheaper or faster alternative to car or taxi for long distance travel. However, since 2011 to the time of this paper, confidence in the MRT system have dropped as it has been plaque with service breakdowns. Some of these breakdowns can be as short as 45 minutes and some as long as a full day. Most Singaporeans feel that the train breakdown is attributed to the sudden increase of foreign workers in the country and that the MRT infrastructure cannot cope with the sudden increase of ridership, thus leading to the breakdowns.

Calls from the public to improve the MRT infrastructure have been a priority for the MRT operators. It is important that the operators understand the traffic patterns of the MRT ridership to be able to constructively understand and cater or improve the reliability and re-instill confidence in the MRT.

Should the MRT operators cater to the morning peak by increasing the frequency of trains in the morning, or should they increase the train frequency in the evenings when commuters end the day? Should policies be applied across all stations or should each station have different policies?
With the Government’s plans to have 6.9 million citizens in Singapore by 2020, we hope to use analytics to be able to understand the travel patterns of the MRT so as to improve the MRT services.

This paper attempts to explore the travel patterns of the MRT ridership in Singapore for the first week of November of 2011. This paper will continue the work done by Roy LEE’s Master Thesis and we seek to explore the areas that LEE do not cover in his Master Thesis.

Project Objective

• Business objective: To identify the MRT ridership patterns of the various station to improve the MRT services.
• Technical objective: To use data analytics techniques such like exploratory data analysis (EDA), and statistical methods to study and gain insights from the data to identify patterns that aid business objective. We will then use time series data mining methods to explore the different patterns.

Project Scope

• Perform data cleaning on the data set received to consolidate the important fields that are required for analysis.
• Perform EDA to identify patterns that will help in the study of MRT ridership.
• Use time series data mining to explore the patterns of the MRT ridership.

Project Deliverables

• A detailed report to explain the study and recommendations to improve MRT services
• A detailed description and interpretation of the analysis procedures that has been used in time series data mining.