Singapore wants to influence population flows with artificial intelligence app
Singapore is partnering with Japan’s Fujitsu to start a trial to reduce congestion in the city. This is a field trial with a smartphone application that is mainly intended to reduce congestion around shopping centers, stadiums and major events.
With the application, people get a proposal how and when to go back home from crowded places. The results are then checked for correctness of the prediction. The predictions are generated by various artificial intelligence techniques developed by Fujitsu and recently merged under the heading Human Centric AI Zinrai. The system must prevent congestion by absorbing the peak traffic load and changing the modes of transport if necessary.
It is not surprising that the research is taking place in Singapore. The country cannot expand and the scarce available space must be used sparingly. In addition, the construction of extra roads, metro lines or other large infrastructural projects is no easy task: it costs a lot of time, money and causes long-term disruption of the environment. That is why Singapore launched a sustainable urbanization project more than a year ago, together with Fujitsu, the Singapore Agency for Science, Technology and Research and the Singapore Management University, which is the first project to go from the lab to the street.
Through the app, people can, for example, receive a proposal to adjust their departure time, change their mode of travel by, for example, offering a discount, or entice people to stay longer in or around the same place. In the latter case, efforts will be made to encourage people to visit local shops or restaurants, possibly also by offering discounts or other benefits.
Fujitsu shows in a message on its site how the model of the project works and where the snags are. For example, it may be that some do not feel like adjusting their way of travelling. The hope is that several tens of thousands of people will install and use the app. The test runs until December 31, 2017.