LTA, SMRT, StarHub and IBM Collaborate to Improve Transport with Data for Singapore Commuters

News Releases 02 Jun 2014 Big Data collaboration Fusion AnalyticS for public Transport Emergency Response (FASTER)

Applying advanced and predictive analytics for people-centered, real-time and dynamic public transport incident management
World Cities Summit, Singapore, 2 Jun 2014: The Land Transport Authority of Singapore (LTA), SMRT Corporation Ltd (SMRT) StarHub and IBM NYSE: IBM) today announced a collaboration to apply fusion analytics to create a state-of-the-art public transport system that will significantly improve the commuter experience across Singapore.
With over 6.3 million passenger trips made daily on the Singapore public transport system, any disruption can impact the travelling experience of hundreds of thousands of commuters. This research collaboration brings Singapore another step closer to a people-centered, intelligent and integrated public transport system designed to improve mobility and the liveability of Singapore.
The LTA and IBM will create a Fusion AnalyticS for public Transport Emergency Response (FASTER) blueprint for better crowd management and proactive information services by using data-driven analytic models that provide situational awareness, impact prediction and actionable insights for mitigating the anticipated impact.
As part of the FASTER agreement, the LTA and IBM will work together to combine farecard data and information from industry partners: SMRT Corporation Ltd (SMRT) who will provide the video data from select MRT stations; and StarHub will provide anonymized telecommunications data for aggregate-level mobility analysis. By using data to extract actionable insights, transport operators and authorities will be able to provide better information services to their employees and passengers. 
LTA's Chief Executive, Mr Chew Hock Yong said: “Through this research collaboration with IBM, SMRT and StarHub, we endeavour to harness intelligence from the myriad of data collected and in turn, these analytics will enable us and the transport operators to better manage public transport incidents and special events through improved resource allocation and pre-emptive crowd management.” 
"SMRT has strengthened its ability to be alert and ready for any eventuality on our train and bus networks. We now have faster responses by our train station staff during disruptions, near real-time updates on social media, increased urgency in the effort to upgrade our train network. FASTER is a potential game changer to help in all of these efforts. We expect to enhance our ability to sense-make and act in the dynamic environment that typifies emergency situations." - Desmond Kuek, President and Chief Executive Officer, SMRT Corporation
Said Mr Tan Tong Hai, Chief Executive Officer, StarHub: “StarHub is pleased to be part of this collaboration, which is very timely in Singapore’s transformation into an intelligent nation and global city by leveraging on Big Data across different platforms. The amalgamation of telco, video and smartcard analytics will give transport operators the visibility of commuters’ travel patterns as well as a basis for recommendations to improve resiliency of bus routes/schedules and to enhance response to unplanned incidents so as to better cater to the needs of commuters. These insights will also allow transport authorities a 360 view of the commuter situation.” 
“IBM is working with governments around the world to make cities’ systems smarter through data driven solutions to attain new levels of insight and efficiency. City leaders can now monitor, measure and manage a wide range of city services to predict future effects and perform “what-if” scenario analysis to proactively manage the negative impacts of an unplanned incident or special event,” said Ms Janet Ang, Managing Director, IBM Singapore. 
About LTA 
For more information on LTA visit 
About SMRT 
For more information on SMRT visit 
About StarHub 
For more information on StarHub visit 
About IBM 
For more information on IBM visit

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