Cities can use artificial intelligence and analysis of data in a number of ways to help them become smarter, and provide better services for their citizens.

Controlling parking by variable pricing on parking meters. Boston is planning to raise the prices of its parking meters for the first time in more than five years. But it is not just planning a blanket hike. Instead, it will use the changes to test how parking prices affect occupancy of parking bays, and the way that people use cars. San Francisco also plans to implement demand-driven pricing as a way to control congestion.

Creating smart parking systems to direct drivers to spaces, and reduce congestion. One problem in cities is congestion created by drivers driving round and round a district searching for a parking space. Deutsche Telekom is trying to address this via smart parking systems which will alert drivers to empty spaces, and direct them straight there. Systems are being tested in Dubrovnik in Croatia, Bucharest in Romania, and Pisa in Italy.

Intelligent traffic management helps to reduce congestion and air pollution. Sensors are not just useful for parking meters. Pollution sensors on traffic lights and at bus stops can also help identify areas of congestion, and direct traffic away from those. Providing real-time information about hold-ups and traffic jams to drivers, together with suggestions for alternative routes, helps traffic to avoid those areas. This, in turn, reduces congestion, emissions, and air pollution in busy areas.

Vehicle-to-vehicle communications offer increased potential to avoid congestion. Smart traffic management can also involve direct vehicle-to-vehicle communications. Vehicles including cars and buses can be fitted with RFID tags that can communicate with each other. Information about traffic jams can therefore be passed on ‘from the horse’s mouth’, almost literally, helping drivers to avoid problem areas.

Improving the use of energy by predicting spikes in demand. Artificial intelligence systems can learn from experience, which means that they can analyse past patterns in data, and use them to predict the future, and therefore manage better. This is particularly useful in managing power grids, because the system can learn when spikes in demand are more likely to occur, and enable better use of the power at times of lower demand.

Variable energy pricing could encourage better use of energy by individuals. Managing energy by predicting spikes in demand is one thing. Encouraging people to use energy at particular times is quite another. Artificial intelligence systems can be used to propose variable pricing models to encourage use at low-demand times. These build on, for example, the UK’s Economy 7 tariff, a good example of a tariff that encouraged night-time use of energy for heating.

Improving awareness of patterns in data, to create new theses and models. Bringing together data from different areas and about different issues can create new insights: it is one of the most important benefits of big data and analytics. These insights, in turn, can help smart cities to create new models about how their citizens behave, and therefore improve the way in which they provide services. Insights might be about ‘known’ and ‘unknown’ issues, leading to very different ways of working.

Helping individuals to make better choices. Cities are all about their populations, and populations are made up of individuals. Helping individuals to make better choices—that is, both better for them, and for the community or population—can therefore change the environment, one person at a time. Big data, coupled with good data visualisation technology, can be used to show citizens the consequences of their decisions quickly and simply, and help them to improve their decision-making for everyone’s good.

Encouraging healthy eating among children. Healthy eating may not sound like a smart city initiative. But a healthy population has fewer health problems, resulting in less demand for healthcare and infrastructure. And with the demands on services from aging populations, this is no light matter. The city of Amsterdam, for example, has used data from grocery stores about sales of vegetables to evaluate a city campaign to encourage healthy eating.

Supporting better land use planning around urban areas. Demand for housing is growing exponentially, according to many government models. Data about demographics, income levels, businesses and general land use within an area can help planning officers to ensure that urban brownfield land is used effectively to meet the needs of the population in the area, rather than just to suit developers.


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