LTA extends collaboration with Microsoft for [email protected]

 |   Singapore News Center

Expanded DataMall now features five new datasets including real-time bus arrival timings and taxi availability to improve commute experience and nurture new generation of urban transport technopreneurs

SINGAPORE – 19 April, 2015 – The Land Transport Authority of Singapore (LTA) and Microsoft Singapore today announced an extended collaboration for [email protected] ( From today onwards, DataMall will feature five new datasets, including real-time bus arrival and crowd information, taxi availability data, as well as locations of MRT exits, covered linkways and footpaths, expanding the range and quality of data available on the site to empower all land transport users – including commuters, motorists and cyclists – to make more informed decisions in their daily commute. The extended collaboration also supports the Singapore Urban Transport Solution (STARS) initiative, aimed at developing Singapore as a centre for world-class urban transport solutions.

In addition, a secondary aim of the extended collaboration is to support talent development for the urban transport industry. With new real-time bus and taxi information, as well as pedestrian pathway locations now available on DataMall, this opens up new opportunities for the community to co-create innovative land transport applications, and helps to nurture a new generation of technopreneurs to support Singapore’s Smart Nation vision.

Since November 2011, LTA has been collaborating with Microsoft to share its rich repository of land transport datasets for public usage through the DataMall hosted on Microsoft’s enterprise-grade cloud platform Azure, which enables seamless data download by third-party application developers for the creation, development and testing of transport-related mobile applications. To cater for variance in user load as a result of fluctuating motorist and commuter traffic throughout the day, Azure also enables the DataMall to be automatically scalable, in order to optimise timely and accurate dissemination of the land transport information to the public. In anticipation of expanding land transport data sources, DataMall’s unique design further enables other collectors of land transport-related datasets to publish their collected data for use by the public.

To date, DataMall has received strong interest from business partners, research institutions and third-party developers, garnering an average of 4 million data downloads per month. This in turn generated between 8 to 10 million monthly hits from more than 40 transport-related mobile applications and services created with DataMall for iOS, Android and Windows phones and tablets. Popular mobile applications include TrafficLah, Carpark@SG and SG Traffic Cam.

“Our experimentation with open innovation through data sharing has enabled very exciting third-party apps to be developed. Crowdsourcing for ideas, co-creating with third-parties and collaborating with renowned research institutions have helped LTA achieve what could have been not possible to achieve,“ said Mrs Rosina Howe-Teo, Group Director, Innovation & InfoComm Technology Group, LTA.

“Advances in technology are allowing new paradigms in managing people-centric urban mobility to be explored – not just by governments and businesses, but also by technopreneurs and the general public. Today, the technologically untrained is able to liberate data as a platform for situation awareness and better decision-making. LTA’s [email protected] is a great example of how city leaders can involve citizens and communities to co-develop and co-create innovative transport solutions by leveraging open data to benefit the community and general land transport users,” said Nobuhiro Ito, Director for Developer Experience & Evangelism, Microsoft Singapore.

This is exactly what a group of students from Temasek Polytechnic is aiming to do with their Easy Bus Trips mobile application. The application, which is currently at prototype stage, is targeted at alleviating commuter frustration from “bus bunching”, which occurs during peak hours when buses along the same bus route arrive at the same instead of regular intervals. Elaborating on the benefits of the application, Ian Hartono Budianto TK, Student Team Leader at Temasek Polytechnic, said, “By providing rich and insightful information including bus arrival timings, crowd indices of the buses and suggested bus routes based on commuter’s preferences such as bus fare, travel distance, and occupancy of the bus, commuters with these information at their fingertips will be able to make better decisions by avoiding delayed and overcrowded buses, and avoid unnecessary frustration in their daily commuting journey.”

Developers of existing land-transport related applications also welcome the addition of richer and more real-time datasets, as well as easier application development on cloud platforms like Azure, so that they may create more useful applications that empowers users with insights for better decision-making. “As more related datasets are made available to integrate into the existing datasets used in the applications use, the insights and recommendations generated become more accurate and relevant to users,” said Tan Chun Siong, Application Developer for SGCarparks – a free application which shows the availability of parking lots in Singapore of various popular shopping centres.

With Microsoft’s Azure machine learning capabilities enabling the automatic prediction of big data patterns, the possibilities for the expanded DataMall could be limitless. One good example is that of Brazil, where Microsoft partnered with Federal University of Minas Gerais, one of Brazil’s largest universities, on a Traffic Prediction Project that pulls together all traffic data, including historical numbers where available, from transport departments, road cameras, Microsoft’s Bing traffic maps, and even drivers’ social networks, to find established patterns that can help foresee traffic jams between 15 to 60 minutes before they happen. This same traffic prediction model has also been tested in major cities such as London, Chicago, Los Angeles and New York, with a prediction accuracy rate of 80 percent. With growing traffic congestion experienced in urban cities around the world, these added capabilities enabled by richer land transport datasets can potentially create a big and lucrative market for mobile application developers.

Microsoft was also awarded the Multi-Tier Cloud Security Standard for Singapore (MTCS SS) Level-1 certification for Azure, both as platform-as-a-service (PaaS) and infrastructure-as-a-service (IaaS), as part of its commitment to offer a trustworthy cloud that is secure and transparent to Singapore government, businesses and communities.


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