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Paper Template Analytics Practicum Report - 2015 Analysing Mass Rapid Public Transportation Travel Patterns of Singapore Through Time-Series Data Mining Koh Ying Ying Trecia; Luqman Haqim Bin Ab Rahman; Singapore Management University ABSTRACT The adoption of Ezlink smart card technology allows transportation analyst to discover new insights of the consumption and lifestyle of their commuters’ in the transportation network. As smart cards contain rich data and all the transactions are in temporal sequences, it gives an opportunity to analyse the complex and voluminous time- series data using time-series data mining techniques. This is particularly interesting as there is a need to transform these rich data into actionable information and knowledge, which users can understand. Therefore, this paper seeks to explores the problem of the transportation network and validate against the implementation current policy of the free rides and discusses the use of time-series data mining techniques to achieve insights that will provides a picture on whether the policy matches with the findings. INTRODUCTION Singapore is a small country, yet it has a complex and comprehensive public transportation network. Consisting of train which can be further split apart into 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 Land Transport Authority governs the public transport network in Singapore. (Hereinafter known as LTA) 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 has 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 attribute train breakdowns to the sudden influx of foreigners into the country. This influx has been purported to increase the frequency of ridership. 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-instil 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 does not cover in his Master Thesis. REVIEW OF SIMILAR WORK The literature review of the report is broken down into four parts; smart cards, the role smart cards play in public transport, conventional data mining and time-series data mining. In the first part of the report, we introduce smart cards. The second part introduces how smart cards play a role in the public transport system. The third part introduces the conventional data mining techniques and its limitations when being applied to time-series data. Lastly, 1 we introduce time-series data mining techniques. SMART CARDS Smart card has been around since 1968, developed by two German inventors (Shelfer and Procaccino, 2002). The Japanese went on to further improve the smart card technology. And in the later 1970s, Motorola successfully secured its first smart cards, which was being implemented and used by French banking system. Further in the 1990s, smart cards have become much more substantial with the introduction of the Internet. [9] Since then, smart cards have outgrown and replaced the traditional and unsecure magnetic cards or tickets that were used, thus improving business processes. As smart cards are designed to store and process data (Lu, 2007), it is suitable for different domains to adopt such technology. Nowadays, it is being used in various areas ranging from banking, healthcare, and telephony services to transportation. Smart cards is a powerful tool for analysis as it has the capability to store rich contextual data of users such like demographics, photos, fingerprints, banking data, transportation fares and others. PUBLIC TRANSPORT Public transport service is a key to the country’s development, which increases the competitiveness and market share of smart card technologies. [1] With public transport services, it allows commuters to travel conveniently at any period of the day. To enhance the transportation services, it is important for transit operators to further understand and study about the commuter’s travel patterns to make better decision for the economy as well as the people’s livelihood and lastly for the country. In larger cities, smart cards come into play to manage the transit network, which provides greater flexibility by allowing commuters to use the card at various times of the day and in different parts of the transit network. Within the smart card, it has the capability of validating and collecting fares, which simplify the fare collection process and reduce the overheads with cash-based payments. This allows analysts to easily retrieve the transit data from the database server, which contains several columns of transactional, and the volume size of transactional data is usually huge. These give analysts the opportunity to analyse and discover real-time activity of travel patterns of the commuters using time-series data mining techniques. However, to adopt smart card, technology, the country must have enough funding and be open to accept new technology. Thankfully, Singapore has adopted the technology. On the other hand, in smaller cities, transport information is gathered through on-board travel surveys, regional travel surveys or through visiting households. This process is tedious, time-consuming and erroneous, as the data have been aggregated. This leads to transit analysts using conventional statistics from synthetic models to draw insights on the commuter’s travel pattern. [1] Using conventional statistics has limitations with regards to its capability to analyse data in the temporal form. Singapore Public Transport Land Transport Authority (LTA) started working on the development of a new contactless smart card based ticketing system since 1994 and finally decided to replace the existing magnetic ticket based system to an Enhanced Integrated Fare System (EIFS). [7] As a result, Ezlink smart card was introduced in Singapore in April 2002 to simplify the fare collection process in public transportation. [2] Ezlink Pte Ltd is a subsidiary of Singapore LTA, which sells Ezlink cards. In 2007, one of the telecommunication companies, Starhub decided to integrate functions of Ezlink into the smart phones. Ezlink monopolized public transportation in Singapore until the introduction of CEPAS Ezlink, which was introduced in during 2009. CEPAS Ezlink provides capabilities in both debit and credit, areas that is able to easily adapt into a wide variety of payment schemes from different card or system operators, replacing the original Ezlink smart card. Ezlink furthered their plans to work with NETS (a point of sale cashless debit payment service) to create a new multi-purpose contactless card, which integrates with CashCard (Stored value card). This will allow commuters to use a new payment mode for both public transports as well as a payment mode for car park, Electronic Road Pricing (ERP) and retail purchases. In late 2009, in collaboration with NETS, EZlink introduced the NETS FlashPay card, which allows storing a higher value, of up to SGD 500 at any ATMs, ticketing offices at Mass Rapid Transit (MRT) stations, bus interchanges, convenience stores, self-service iNETS kiosks with NETS Flashpay access. This Flashpay card could be used in more than 30,000 retail places island-wide. [10] Later in 2010, by integrating with VISA to provide the Auto-top-up service which automatically top up a commuter’s card to a predetermined amount enabling them to use the card as both credit card as well as contactless smart card for 2 ticketing. [11] With the Ezlink smart card capabilities, it provides a new window for analysts to study and discovered patterns of commuter’s travel in both bus and rapid transit routes, which allows them to easily extract trips information from the Ezlink smart cards such as occupancy, velocity, arrival and departure time of each stops and commuter’s demographics for analytical purposes. [2] Since the implementation of the automated fare collection system, there are large potential benefits to improve the public transport planning and operation. [8]. As these information are usually in form of spatio-temporal characteristics, data mining techniques need to be applied to explore the insights of commuter’s travel pattern. CONVENTIONAL DATA MINING Data mining is also known as Knowledge Discovery in Databases (KDD). KDD is a process involving processing large amount of unprocessed and raw data stored in data warehouse or data marts waiting to be transformed into meaning insights that can be easily interpreted and understood by business users. There are three types of data mining techniques: association rules mining, classification and statistical. All these mining techniques mentioned above are usually suitable for transactional database to monitor daily transactions. However, businesses and us want to discover greater insights or knowledge from time-stamped data being stored in transactional database.
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