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Urban mobility in the smart city age: DataSpark

Zafirah Salim | Jan. 6, 2016
In this email interview, Dr. Amy Shi-Nash, Chief Data Science Officer at DataSpark, Singtel, talked about how DataSpark is contributing towards Singapore's smart city vision, especially in the aspect of public transportation; and how the Internet of Things (IoT) have changed the game for smart cities, including data privacy issues.

DataSpark's priorities are in mobility and urban planning. For mobility, we deliver insights on current transportation network usage to facilitate planning, recommend optimal routes for travellers and facilitate an effective emergency response for breakdowns in public transportation networks. We are also actively supporting the urban planning initiative by providing insights on the density of footfall in residential and commercial precincts, and even the utilisation rate of individual buildings. With a thorough understanding of Singapore's geography in residential and commercial areas and the commuting patterns of the population between these areas, a holistic approach may be achieved in both mobility and urban planning. 

What is Singtel doing to help understand how people use public transportation; and how does this aid in enabling better operation and future planning of underlying transport networks in Singapore?

DataSpark's proprietary algorithms and platforms can be used to produce MRT-related insights to facilitate effective planning on both the operational and expansion fronts. We collect cellular data, from both outdoor and indoor cell towers in train stations and tunnels for analysis. The insight includes train service frequency, train loads, crowds on platforms, crowd movement within stations and route utilisation.

Also, with real-time capabilities, we are able to detect signs of train service disruptions early so that preventive measures can be put in place. Emergency response services such as feeder buses can also be optimally allocated by understanding the geographical make-up of residential and work areas, as well as the commuting patterns of people between these areas. These capabilities can also be applied to expressway traffic to deal with congestion and speeding.

Public transportation works best when a lot of people want to go to the same place at the same time, which makes sense for highly populated cities like London and New York. How can public transportation become smarter in smaller cities like Singapore where there is less demand for it? 

There is actually very high demand for public transportation in Singapore, probably comparable to that of London and New York. Singapore is a densely populated city with a well-developed public transportation system, and travelling in private vehicles is a very expensive alternative due to high costs of vehicle ownership.

Insights on the commuting patterns of the population can help public transportation planning to become smarter in all cities. By identifying the most frequent routes of commuters, authorities will be able to plan and develop transportation infrastructure around these routes. Commuting patterns can also be used to help identify new routes or optimal placements for transport hubs through simulation.

Can you explain the traffic measurement system the DataSpark team have developed that aims to monitor the subway and expressway traffic, leveraging telco location data?


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