A look ahead: Data-Driven Cities
This is a repost from the official Pictures of the Future Siemens post. You can find the original article located here.
We live in an age of urbanization. For the past ten years or so, more than half of the world’s population has lived in cities. Moreover, there’s no end in sight for this migration of people to urban areas. On the contrary, the latest UN forecast predicts that 70 percent of the world’s population will be living in cities by 2050. At that point, the world’s total urban population will be almost equal to the earth’s entire population today. Within a mere century, the number of people living in big cities will have grown from one billion to almost six billion. This trend will also lead to the rise of more and more megacities — cities that have more than ten million inhabitants. Whereas there were 28 megacities in 2014, there are expected to be 41 by 2030. Demands on infrastructures are expected to grow accordingly. Smaller cities are also expected to grow considerably. In 2016 there were about 500 cities with more than one million inhabitants; by 2030 there could well be more than 650.
Many cities are already suffering from housing shortages, overstretched infrastructures, and uncertain water and energy supplies. Added to this is the increasing risk of natural disasters resulting from climate change. Emissions from big cities, in particular from the transportation sector, are contributing considerably to this development. According to recent studies, the most effective low-carbon strategy would be to electrify this sector. Some areas are already trending in this direction. However, if the rise in global temperatures is to be kept to less than two degrees Celsius, 90% of all road vehicles would have to be electric by 2060.
Clean Air and Water for All
As more and more cities move toward these goals, they will rely increasingly on digital resources that will, for example, monitor emissions figures and traffic density and coordinate local public transportation and traffic light switching times with monitoring results. Ultimately, they will also use digital technologies to inform individuals about the best ways to reach their destinations, regardless of whether they are driving their own vehicles, sharing cars, using a public transport system, or combining transport modes.
Forecasts for Smart Cities
Answers as to how this can be done are provided by smart software. For example, the City Performance Tool (CyPT) from Siemens. It gives guidance to a city on how to achieve their environmental targets while providing an indication on how each infrastructure-related decision will influence job creation and the infrastructure sector growth.
However, many other software programs consist of more complex systems known as neural networks. Neural networks are computer models that operate in a way that is similar to the human brain. They can be trained to recognize interrelationships and use this knowledge to make forecasts.
Precise forecasts that are based on recorded data are at the heart of almost all smart city solutions. Such forecasts will enable smart grids to offset fluctuations in the electricity supply that are caused by changing weather conditions. A step in this direction is already taking shape as fleets of electric cars are integrated into building management systems so that the vehicles can serve as energy storage devices.
From Smart Data to New Markets
One step in this direction is offered by MindSphere, an open, cloud-based IoT operating system from Siemens that offers both connectivity and a range of industrial applications so that any enterprise, regardless of industry or size, can begin analyzing data to optimize their operations. Similarly cities and infrastructure operators can develop IoT applications to relieve traffic congestion, conserve water and energy and improve infrastructure services.