In the “smart cities” of the future, lighting will play an important role. In December 2013, the city of Eindhoven kickstarted a very interesting future project for a “smart city” for the long term.
The project aims to model the ambiance of one of the hotspots of popular entertainment in Europe, Stratumseind. It combines a dynamic evolution of the lighted environment, which is automatically driven by data collection, full-scale, in real time:
- Cellular connections;
- Parking spots; and even
- Tweets and other social networks, interacting with algorithms derived from behavioral research related to light.
The Greatest “Drinking Street” in Europe
The problems the site poses are complex. Stratumseind is the biggest party street in Europe: 50 bars, 10 restaurants, in the space of 400 meters. While there are anywhere from 2,000 to 3,000 people on weeknights, the crowd jumps to more than 10,000 partiers on Saturdays, which is to say that beer and alcohol flow like water, and that accidents requiring security intervention services number 800 annually. Therefore, it was necessary for business owners and authorities to define a quality lighting scheme in order to give value to the street and its commercial context, which generates activity, as well as to ensure the security of its users – with the advantage of being one step ahead of needing it.
A Project of Multiple Convergences
The “OpenRemote” project for Stratumseind is part of a municipal undertaking at a greater scale: the “LivingLab” is a joint venture between the City of Eindhoven, the Einhoven technology universities TU/e, of Tilburg and Amsterdam, the Dutch Institute of Technology, Safety and Security, DITSS, and private partners, as well as large companies, like Philips and Bosch, which find terrific R&D ground in this project. The “LivingLab” collaboratively studies the impact of environmental factors (of lighting, but also sound and scent) and behaviors (crowds, types of public) in very dense public spaces, which would explain the sometimes incongruous nature of the collected data.
“OpenRemote,” An Immense Laboratory
On the one hand, there has been data collected in real time:
- Human densities (counts of entrances and exits from the site) allowing for a refined estimate of attendance;
- 3-D measurements of sound levels;
- Climate indicators, but also the frequency of mobile phone devices on the site;
- Number of available parking places in the surrounding areas; and above all
- An innovative monitoring system of “social activity” by following activity on location-enabled or specifically tagged (#nomdubar) social media sites, such as Facebook, Twitter and Instagram.
A renewed interest in information available on a weekly basis, such as where visitors are coming from, is also part of it:
- Tracking of cell phone identifiers;
- Volume of garbage;
- Type of consumption indexes (provided by the brasseries); and
- Collecting information on more personal preferences through surveys of residents, security services, urban managers and business owners.
Having few interests of a collective or individual nature, these “Big Data” are translated, interconnected and reported in graphic format through “data visualization,” like maps in artificial colors, and other graphs that could thus simplify the identification of needs (security, density concentrations, etc.) and the detection of factors and situations of aggravation (the amplification of problem areas).
On another hand, the system rests on the base of behavioral knowledge related to lighting that we are all familiar with, such as:
- Color gradation and the calming/exciting effect of certain colors; and
- The impact of lighting levels on security.
Thus, in real time, the operator (who is also an experimenter) visualizes and can also adjust, point by point, elements like light density and color, but also react to police presence and sound levels. All of this is to facilitate less aggravation and limit the seriousness of problems.
Finally, there is the experimentation with and improvement of the system’s performance: The intelligence base – the database – as well as previously successful or unsuccessful solutions enable research and an accrued sensitivity to the warning signals that make prevention possible through anticipation.
In Order to Learn More
You can also download the presentation file for Stratumseind (PDF in Dutch), where the graphics originated from.
Certainly, this kind of experimentation is particularly invasive and will not be carried out in local residential streets because it deals with a very particular problem area; yet, we must look at it on the same terms as a “concept-car,” a research process and implementation at full scale that will cause technologies to emerge, which will be integrated in the lighting revolution of tomorrow, and that will allow for a more refined management of the resources and benefits of light (beyond its “illuminating” function).
An Extended Headstart that Is Questionable
But there is the problem of confidentiality, usage and the large-scale intersections of semi-private data by the “Big Brother” contingent. An example: the identification of cell phones on a large scale allows for targeting socio-typical user profiles (example: residents typically detected on antenna relays in the centre-city or coming from other neighborhoods, ie. non-locals) and categories of users according to terminal type. For example, an algorithm has already correlated the density of people who are “not typically localized” (meaning newcomers or non-residents, who are therefore not typically connected to centre-city antenna relays) and good weather having an impact on the type of incidents …
What do you think? Does technology weave a web of trust? Or is this an example of too much “Big Brother?”
Credits: Data and images linked to sources.