This article is published in the Case Study category.

The Singaporean Crowd Analytics Mobility Use Case

Analysing a city’s mobility pattern is quite a challenge. While public transportation companies have tried to access this information by analysing ticketing sales or marketing companies by reading location-based social media posts, how can a municipality really know from which neighbourhood people leave and come back during specific hours?

For a few years now, Singapore has become one of the most quoted examples of Smart City concept implementation. Actually known as a Smart Nation, where the whole country is part of a technology plan aiming at providing a better living space for its citizens, Singapore and its government have developed a great range of innovative projects such as smart buildings, smart transportation systems and encouraged education in the field of ICT as shown in this video recently posted by Yaacob Ibrahim, Singapore’s Minister for Communication and Information, on Facebook.

It is in this particularly connected context that DFRC, established in Singapore since 2012, has installed several LBASense sensors all over the Island in order to analyse the citizens’ mobility patterns.

DFRC’s LBASense long- and short-range sensors passively detect anonymous signals from mobile phones carried by citizens, enabling an algorithm to transform this data into crowd analytics. In other words, we are able to know the number of persons present in a determined area almost in real-time.

Counting the number of people in a place can seem abstract, yet this information can be very useful for event organisers, marketing agencies or law enforcement representatives who would want to make sure that, for security reasons, a place is not too crowded.

Moreover, what does mobility analysis mean? This analysis, first, implies the necessary deployment of at least two sensors. Due to the fact that each phone is unique, by using a complex algorithm, LBASense technology is capable to recognise signals coming from the same device and aggregates those data to create a bigger and more meaningful picture, providing the global trends of population movement between those sensors.

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According to the period of the year (holiday or not), the day of the week, the time of the day, the weather (Singapore’s heavy rains are quite famous) the mobility analysis reflects a social demographic map of the way the citizens move in their urban environment. Public transportation routes relevancy can be verified for example thanks to this data, and other applications such as economic studies for businesses (where to open a coffee shop targeting the morning workers) or real-estate researches can profit from this information.

As many fear the overwhelming place of Big data in our modern societies, sensors can help analysing a big picture: the map of the city’s use by citizens. In the Smart City context, the real goal is to understand sociological global trends, forged by citizens themselves.


Data team

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