United Nations Public Service Awards Case Studies

1. Singapore is a densely populated city with over 5 million residents inhabiting 714 km2 of space. Being a major transportation hub in the region, its land transport system plays a critical role in keeping the economy and its people moving. The Land Transport Authority of Singapore (LTA) keeps traffic free-flowing by ensuring efficient traffic management and effective regulation of both private and public transport. LTA manages Singapore’s short and long-term transport needs by providing an efficient and cost-effective transport system that includes roads, rail, buses, taxis and private vehicles.

2. By 2020, Singapore’s travel demand is expected to increase to over 15 million journeys a day. As the demand for greater mobility grows over the years, LTA strives to maintain the delicate balance between the efficient use of land space while meeting the diverse transport needs of the people to support a vibrantly growing economy, a bigger population, higher expectations and diversified lifestyles.

3. To address the demands that entail these trends, the Land Transport Master Plan (LTMP) was unveiled in 2008 outlining the strategic thrusts shaping the land transport policies and developments for the next 10-15 years. The key strategic thrusts are to Make Public Transport a Choice Mode; Manage Road Use; and Meet the Diverse Needs of People. In making Public Transport a Choice Mode, this translates to:
a) Enabling more data-driven planning and policies for LTA;
b) Optimising operations for Public Transport Operators (PTOs);
c) Giving commuters’ access to more travel information that enables them to plan their journeys better.

4. In order to equip itself with better capabilities to do so, LTA needed to create a knowledgebase to derive new insights, capture tacit knowledge and make it explicit in a central information store. However, the rapid growth in public transport data posed a considerable challenge to LTA’s existing information infrastructure. Each day, over 12 million records of public transport data are captured through ticketing data in their CEPAS** card (each fare-card contains more than 20 data elements on information such as the origin/destination, time, distance travelled etc). This affected its capability to perform advanced analytics for policy planning, transport modelling for strategic planning and predictive simulation in the deployment of various LTMP initiatives.

5. Furthermore, existing system limitations only allowed planners to analyse three months worth of data which is insufficient for meaningful trend analysis or long-term land transport policy planning. Thus, LTA decided to build a data warehouse and business intelligence system (BI) that provides it with advanced capabilities to perform large-scale business analytics and transport modelling called “Planning for Land Transport Network (PLANET)”, for strategic planning and predictive simulation in the deployment of various LTMP initiatives.

**CEPAS – Contactless e-Purse Application is a Singaporean standard for an electronic money smart card specification, which allows interoperability of multi-purpose stored value (MPSV) card payment schemes from different card issuers and system operators. Thus, the standard allows a single MPSV card for all micro-payments ranging from transit, taxi, motoring, retail and other payments in Singapore.

What is the initiative about? (the solution)
6. Recognising that data is a strategic asset with the potential to help achieve the key strategic thrusts underlined in the LTMP, the Data Management Steering Committee (DMSC) was set up to explore data management and ways to maximise its value. At the time, user groups collected data individually and purged the data after 3 months. Thus, a more powerful system with the ability to cross-reference trillions of records was needed to derive an enhanced land transport planning model. 7. PLANET takes the systematic approach of hosting and centralising the data collection of public transport commuter data. Moreover, its design enables it to store and analyse real-time traffic data in future. PLANET enables empirical decision-making, enhances knowledge-sharing and collaboration among land transport professionals such as policy analysts, transport planners and transit regulators. Effective data-mining allows for the fine-tuning of policies alongside traditional methodologies of feedback collation, surveys and post-policy implementation comparison studies. 8. PLANET can store up to 3 years worth of data and carry out advanced predictive algorithms of travelling patters by transport mode and, the impact of traffic analytics on commuting. Beyond achieving 67% improvement over traditional processing, it gives its users new ways to view the data gathered – 70 new analytical reports were generated for policy review, post-analysis of projects and trend patterns to optimise resource planning. 9. With comprehensive information to analyse historical performance and travelling patterns, LTA can be more effective in administering regulatory audits, operational monitoring and review of existing policies and formulation of new policies. Insights from these analyses allow LTA to implement new measures to better address the needs of commuters. 10. In 2009, LTA took on the role of Central Bus Planner for a more holistic approach to planning the bus network, taking into consideration other transport networks and infrastructure. Beyond using community feedback and land-use information, LTA leverages on data from PLANET to take on this new role. For example, when studying if an existing bus route should be amended, LTA uses ticketing data analysis to assess the benefits of new connections and trade-offs such as lost links or detours for existing commuters. PLANET also enables LTA to maintain greater oversight over public transport service quality and performance. Besides ground checks, ticketing data analysis is used to determine the utilization level of existing bus services to ensure that finite resources are judiciously distributed to enhance existing services for maximum benefit to the community. 11. Commuters also benefited as anonymised data from PLANET is publicly shared through DataMall@MyTransport.SG, a cloud computing platform. This catalysed the creation of many applications that give commuters access to travel information on-the-go, such as bus arrival timing; traffic alerts; travel advisory services; and parking availability in downtown Singapore. This helps to improve the commuters’ travel experience as they have the convenience or making more informed decisions; some of which were not possible before PLANET. Thus, providing public access to selected PLANET data has enabled the co-creation of applications and services to improve the commuter’s travelling experience.

Who proposed the solution, who implemented it and who were the stakeholders?
12. PLANET was conceptualised in 2008, spearheaded by the LTA Senior Management team, including the Chief Information & Chief Data Officer, and various key business and technical stakeholders to steer, govern and set in place the implementation of the project including members of Data Management Steering Committee from: a) Innovation and InfoComm Technology Group b) Transportation and Ticketing Technology Group c) Policy and Planning Group d) Vehicle and Transit Licensing Group e) Engineering Group 13. The LTA project team who implemented PLANET comprises: a) Project team from Innovation and InfoComm Technology (IIT) Group b) Technology partners and Data warehouse consultants from the private sector including the following: Partners Products Used Wipro Limited Wipro is the overall system integrator and strategic partner for this project. Teradata The DWH layer is built on Teradata equipment Informatica Informatica Suite used for data transformation, metadata and master data management functions SAP SAP Business Objects reporting platform IBM The infrastructure of the data transformation and reporting platform Cisco The network infrastructure 14. The stakeholders include BI users like policy analysts, transport planners, research analysts and transit regulators from various LTA groups: I. Research and Publications Division II. Strategic Planning Division III. Fare System Division IV. Road Pricing Division V. Bus and Vocational Licensing Division VI. Transit Regulation Division

Describe how and when the initiative was implemented by answering these questions
a. What were the strategies used to implement the initiative? In no more than 500 words, provide a summary of the main objectives and strategies of the initiative, how they were established and by whom.
15. Building upon a world-class land transport infrastructure, LTA endeavours to develop a more people-centred transport system that is technologically intelligent, yet engagingly human so that commuters can look forward to a more integrated and user-friendly public transport system. This requires a continual effort of policy reviews and operational refinements. 16. The data analytics capabilities provided by PLANET is an integral part of government policy planning to support the key strategic thrusts of the LTMP. PLANET focuses on business data analytics for land transport planning and statistical analysis; regulatory and operational reporting and performance management of the quality of service for public transportation. It consolidates anonymised data from disparate transactional system, performs high-volume data crunching and presents the information from different perspectives to serve multiple purposes. The main objectives of PLANET are to: I. Analyse Business Data for the purpose of planning and statistical analysis, regulatory and operational reporting and performance management of public transport operators. II. Empower Policy Planners to analyse various dimensions of business data : III. Allowing Policy Formulation with Analytical Capabilities IV. Enabling Metrics-based Regulatory Framework V. Enhancing Responsiveness of Business Operations VI. Fine-tune Policies through effective data-mining alongside traditional methodologies of feedback collation, surveys and post-implementation comparison studies. VII. Create a Knowledge Base to derive new insights by through empirical decision-making, knowledge-sharing and collaboration among land transport professionals from different expertise, such as Policy Analyst, Transport Planner, Research Analyst, Bus & Transit Regulator. VIII. Accelerate innovative decisions and enhance organisational learning by deriving tacit knowledge, capturing it and making it explicit in a central information store.

b. What were the key development and implementation steps and the chronology? No more than 500 words
17. LTA formed a Data Management Committee which included senior management from the business units as well as data owners. The committee was critical in establishing strategic directions to steer the project implementation for PLANET, as a well as guiding the data management, data governance, data quality and data-sharing policies of voluminous data in LTA. 18. PLANET was conceptualised in August 2008, the team went through numerous rounds of requirements elicitation to ascertain that the key requirements of the project were clearly defined. Using a Proof-of-Concept (POC) study, the IT team conducted an evaluation of suitable enterprise data warehouse technologies jointly, together with the key system owners from the business units over a period of fifteen months. The POC yielded promising results with a 99% improvement in response time. 19. The POC enabled the project team to gain a good understanding of the technology and assess the appropriate tools in meeting LTA’s business users’ requirements according to the roles they play, the information they need, and the manner in which information is consumed and analysed. 20. Strong support from senior management also aided the management being able to meet the tight project deadline. LTA selected the implementation partner and technology platform to use in March 2009 and set an aggressive project schedule of twelve months for the implementation of PLANET. PLANET was commissioned in July 2010.

c. What were the main obstacles encountered? How were they overcome? No more than 500 words
21. In the initial stage, the project team faced challenges in getting in the specific requirements from various stakeholders within LTA. Firstly, the project team had to justify the value of PLANET and the marginal benefits of having a powerful data warehouse system with BI capabilities as opposed to maintaining the existing technology infrastructure given the high investment costs and the new technologies being used. The formation of the Data Management Steering Committee comprised of senior management from the relevant business units and data owners helped to provide a platform to understand the strategic interest and benefits of the project and helped address the concerns various internal (LTA) stakeholders understood the potentials of such a system. 22. Secondly, getting the various internal stakeholders involved at the committee level also helped to expedite the design process of the system specifications. The committee helped LTA to classify data collections, establish data ownership and define enterprise metadata taxonomy. This was helpful in getting buy-in from staff at the working level to provide their user-requirements and inputs on the system. By working together in various levels, these help different user groups to understand the strategic importance of the project and work cohesively to achieve the target. 23. Finally, there are concerns on the data-privacy issues on the travelling pattern of each commuter. The Data Management Steering Committee addressed the concern for maintaining commuters’ data-privacy by anonymising and aggregating the commuters’ records in PLANET.

d. What resources were used for the initiative and what were its key benefits? In no more than 500 words, specify what were the financial, technical and human resources’ costs associated with this initiative. Describe how resources were mobilized
24. The financial and technical costs of PLANET amount to over USD$12 million. Payment was made in stages, after goods and services rendered in accordance to the LTA Financial Procedures Manual. The bulk of human resource costs can be attributed to the time and effort taken by the various users and stakeholders to identify their user requirements during the design, development and testing stages of the project. 25. PLANET achieved 67% and 99% improvement over traditional methods in the processing of daily data information and running business queries respectively. Apart from the technical benefits of improved turnaround time, PLANET has also enabled swifter Policy and Planning Decisions and yielded financial cost savings, as well as productivity gains for LTA. 26. With PLANET, we estimate an annual saving of 19% compared to the old data and reporting systems. Over 10 years, PLANET will avoid S$30 million in costs for enhancing and maintaining the legacy data warehouse. Based on conservative estimates, there is a projected cost avoidance of S$20 million over 10 years from productivity gains of at least 15% reduction in manpower required for preparation of reports. 27. Comprehensive information for the analysis of historical performance, travelling behaviour of commuters is now available at the fingertips, and hence speeding the access of such information greatly. Staff can be more effective in carrying out regulatory audits, operational monitoring, and review of existing policies and formulation of new policies or measures to better address the needs of land transport users. Moreover, in-depth research studies are being conducted using data mining techniques to look into the correlation analysis, factor analysis and descriptive statistics. The implementation of PLANET has unlocked the intrinsic value of data to perform: I. Strategic Analysis: In order to understand the impact of making transport network changes, it is important to know information about the whole journey, starting from the first station or bus stop, to the eventual destination. Such information will reflect the various dimensions of distance, time, speed, and fares for the train and bus passengers. Ridership and passenger-km travelled on public transport will also provide the insight on the relative importance of the train and bus network. II. Locale Analysis: As planners and designers of land transport network, information on the number of boarding, alighting, transfer volumes are keys to evaluating the level of provision of commuter facilities, such as size of bus shelters, passageway widths for transfer points and location of passenger service-counters at high volume areas. Bus Hubs, key transfer nodes, are designed based on bus throughput and passenger boarding rate. Post analysis could also be performed on specific part of the transport system. III. Operational Performance: Performance characteristics of the train and bus network, such as passenger loading, route running time, headway etc allow planners to determine the level of efficiency and whether there is a need to adjust the level of resources deployed.

Is the initiative sustainable and transferable?
28. In terms of sustainability, PLANET is an integral system within LTA used by various groups for their work. It is the single source of trusted data for transport professionals from LTA and the PTOs. The insights from data analyses using PLANET help various groups to perform their functions in a more robust manner and allow LTA to make policy and planning decisions that are supplemented by data. The operation and maintenance of PLANET is funded by LTA as part of its total operating cost. Operationally, PLANET is supported by an eco-system of public transport operators (PTOs), the Contactless e-Purse Application (CEPAS) cards used for ticketing in public transit, and TransitLink which takes on the role of the transit acquirer that processes and clears transit transaction. 29. The current phase of PLANET includes data for the analysis of public transport information from the CEPAS cards. In terms of transferability, PLANET has the capability to include real-time traffic information in the next phase of its implementation. It has plans to extract and analyse road traffic data from various systems to gain insights into private transport. Eventually, this will enable LTA to integrate both private and public transport information, and gain a more holistic and multi-modal analyses of commuters’ travelling patterns so that LTA can refine its transport planning to provide commuters with seamless and efficient journeys.

What are the impact of your initiative and the lessons learned?
30. PLANET provides LTA with enhanced capabilities in its effort to provide a more people-centred land transport system for Singapore. Since its implementation in 2010, PLANET has helped LTA to redefine the public transport planning and travelling experience by providing commuters access to more travel information, as well as giving transport professionals at LTA and the PTOs access to more the historical analysis of travelling patterns and public transport performance; with which to better refine policies and implement new initiatives. The transaction data that feeds into Planet includes information about ridership and journey times. Planet's analytics enable users to: a. Formulate, validate and refine land transport policies. b. Locate transport facilities, such as seats and shelters. c. Optimize operations, such as passenger loading and frequency. d. Support the LTA's metrics-based regulatory framework. 31. Since its implementation, the impact of PLANET has gone beyond improving LTA’s internal efficiency. Anonymised data from PLANET is highly sought after by international academia for research into urban transport studies and third-party developers to co-create new information services. 32. Some of the critical success factors in using PLANET to enable LTA to redefine the public transport planning and travelling experience include: a. Managing varying user needs. The range of use cases for PLANET involves different kinds of users The LTA must coordinate this work to make the most efficient use of Planet. Therefore, the LTA captured and centrally stored the extensive, complex business b. Establishing data management framework. The LTA created a data management steering committee to classify data collections, establish data ownership and define enterprise metadata taxonomy. These disciplines promote cooperation among different user groups. c. Formulating system interface standards. Planet involves the integration of multiple systems, and the LTA had to create standards for the interfaces between these systems. The process involved rounds of discussion to ensure a good understanding of the overall design so that standards and procedures would facilitate seamless system integration while ensuring Planet remains flexible and secure. 33. Currently, PLANET only captures public transport commuter data but it has potential for more analyses. In the future, LTA plans to use PLANET to analyse areas of congestion in the land transport network with spatial analytics. In addition, intermodal data can be analysed with advanced predictive algorithms of travelling patterns whether by car, bus or train, and the impact of traffic on commuting. With the help of PLANET and other information sources, LTA can better set strategic plans for the next generation via a more complete view of the land transport planning model and landscape. PLANET will also enhance LTA’s effort in modelling transport and optimising land use. To complete these tasks, the LTA will need to: a) Develop new analytics skills. b) Promote data sharing. c) Increase the productivity of Planet users.


Institution Name:		Land Transport Authority
Institution Type:		Government Agency
Contact Person:		Gin Howe Goh
Title:		Deputy Director, IT Planning and Governance
Telephone/ Fax:		+65 62996341
Institution's / Project's Website:		www.lta.gov.sg
E-mail:		gin_howe_goh@lta.gov.sg
Address:		1 Hampshire Road, Block 6, Singapore 219428
Postal Code:		219428
City:		Singapore
State/Province:		Singapore
Country:		Singapore

The Problem

Solution and Key Benefits

Actors and Stakeholders

(a) Strategies

(b) Implementation

(c) Overcoming Obstacles

(d) Use of Resources

Sustainability and Transferability

Lessons Learned

Contact Information