Smart cities are set to become smarter through the use of artificial intelligence (AI) – and the technology could have benefits in various applications, from traffic and parking to smart sensors.
A recent report from IDC found that expenditure on smart cities technology would cost around $80 billion, perhaps $135 billion by 2021. These investments will make cities more convenient and sustainable, but will come with new complex challenges. Three core values are promoted by the Smart Cities Council:
- Liveability: These are cities that provide clean, healthy living conditions without pollution and congestion. They have a digital infrastructure that makes city services instantly and conveniently available anytime, anywhere
- Workability: Cities that provide the enabling infrastructure — energy, connectivity, computing, essential services — to compete globally for high-quality jobs have the core value of workability
- Sustainability: This value comes to the cities that provide services without stealing from future generations
Due to its ease of use, local governments can now gather real time data, along with the capabilities of artificial intelligence (AI). Collection of more and better data is the first step in a city becoming a “smart city”. AI is currently being used for helping cities to gather and process data. Cities have various data sources, like ticket sales on mass transit, local tax information, police reports, sensors on roads and local weather stations. One huge source of raw data that AI pattern recognition technology is making significantly more manageable is video and photos. That is far more raw data than could ever be viewed, processed, or analysed by humans.
Driving in city traffic is not so much a matter of distance but of time. The time it takes to get there can be an open-ended question, with so much depending on congestion and how red lights contribute to it. Adaptive signal control, applied in Los Angeles, San Antonio, Pittsburgh and some other cities, uses real-time data to change the timing on traffic lights to adjust to the flow of traffic. The Transportation Department says adaptive signal control can improve travel times by over 10%— and in some areas with seriously outdated signals by 50%.
A clear advantage with AI is in streamlining power and water and usage. Google has said its AI has cut power requirements in its data centres by 40%. Cities are employing smart grids to better manage power use. AI also is being applied to water metering to curb excess water and find leaks.
AI is being applied to help solve — or, at least, ease — one of the banes of city existence: finding a place to park. Redwood City, California, for instance, uses a predictive modelling and AI framework from VIMOC Technologies to identify patterns of use in parking garages and other areas to make parking more efficient.
Yet perhaps the most intriguing project is taking place in Ho Chi Minh City, Vietnam. An ongoing collaboration between the World Bank’s Governance and Land & Geospatial Teams, together with the Development Economics Research Group, is helping connect emerging technology and machine learning to key development stakeholders in the city.
Specific types of machine learning algorithms “teach” computers to automatically detect and classify different types of land cover and land use across space and time, and then generate compelling insights, analytics and visualizations. In this type of machine learning computers are trained what to look for in a satellite image based on reference, or training data. This data consists of examples that can be collected “on the ground” through surveys, or even from existing classifications or from data collected from comparable cities.
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