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Brussels Smart City: all about data

Céline Vanderborght, Smart City Manager, Brussels-Capital Region
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Céline Vanderborght

For the past three years I have been raising awareness and sharing my knowledge about the subject of Smart City by talking about new technologies and city management, illustrated with concrete projects. I focussed on Smart City objectives such as sustainability goals, which are well-known and generally accepted. However, now, three years later, I can’t help but notice that “Smart City” is still a bit of an unclear concept, a “catch-all” as I am regularly told. Our citizens seem confused and honestly, in my eyes, the same rings true for our politicians

Now, I believe that the best way to introduce the notion of Smart City is by focussing on the common denominator of the projects which it englobes: data.

Recent technological developments and their impact on our ways of life are constantly growing. The launch of the first iPhone in 2008, an unseen technological feat and the ultimate connected object, has allowed us to live in a more connected, mobile way which leaves behind countless digital traces. Social networks and the digitalisation of processes generate an enormous data flow. This exponential growth of the amount of data combined with the development of computers’ capacity is called “Big Data” and its counterpart: artificial intelligence.

This evolution looks like a paradigm shift as explained by Tony Hey in his book The fourth paradigm : Data-intensive scientific discovery”. Big data has moved science into a new era. The first science paradigm is empiricism: drawing conclusions based on observations of the world. The second one is hard sciences, mathematics and physics theories allowing us to explain observable phenomena. The third one is based on world modelling and computers’ calculating power allowing us to simulate experiments.

In the fourth paradigm, we no longer model the world, we calculate it! We no longer use samples to extrapolate theories, but individuals as a whole. We no longer limit ourselves to the most important explanatory factors, but use all of them. However, above all, the machines themselves will correlate the data and make statistical, “spontaneous” links amongst billions of data. We, humans, will have to judge the accuracy of these links.

What does this mean for the Smart City? Let’s take a look at parking, an important issue in city management which could definitely benefit from ‘smart’ solutions. The first step of intelligent parking is the digitalisation of the payment process for the user (for example, payment through text message) and the checks of the parking attendants, who would receive an alert and only head out to the scene if the violation is known and identified. The latter could be done through floor sensors, camera’s in public spaces or the well-known scan car that patrols the streets and has a camera attached on top that collects and analyses license plates.

 

But how can we bring this Holy Grail, i.e. real-time info about parking availability and the best way to that car park, to users? Cities and start-ups have been numerous in trying to solve this puzzle, because during the time needed to get to “your” parking space, which has just freed up, someone else will have taken it. In this example, Big Data could be useful. Super computers would be able to find correlations and make predictions based on millions or  - let’s not be scrooges - billions of data, mostly historical data (for example all regional parking data of the past 20 years) but other possibly useful data could be included in those analyses. That way, we would be able to say, for example, that there is  a 83% chance of finding a parking space in street x at a particular time of the day whilst it’s only 29% in street y, where, however, in 30 minutes, those odds will rise to 48%. The cherry on the cake is that it would not be necessary to install ground sensors as we could use your transport data (Waze, Google, CCTV) to determine where exactly your vehicle is parked. 

For public services trying to carry out our mobility policies, the possibility to collect, analyse and use these data is priceless. Improving parking based on occupancy data would allow for dynamic, offer-based pricing as well as full occupancy predictions and communication to users just to name a few examples.

The above leads me gently but surely to the conclusion of my opinion piece. My wish for the Brussels-Capital Region are massive investments in the development of our infrastructure and digital capabilities so that we can get the most of these data. I argue for a Smart City or Big Data Department within the BRIC that focuses on collecting, categorising and analysing Brussels’ urban data for use by our public and private partners. Furthermore, I want this endeavour to be accompanied by a proper Open Data phased programme including innovative projects implemented by Europe.

We also need to attain relevant knowledge about this subject in order to really benefit from the described data, so my second wish for the Brussels-Capital Region is an ambitious training programme with a focus on data,  adapted to meet the needs of not only developers and researchers, but also of children, public service workers, small or mid-sized company managers and our citizens.