New trends analysis from SmartCitiesWorld explores how locally generated and processed AI is improving smart lighting, urban mobility and public safety.
In the management of city operations, ‘the demand for real-time intelligence at street level has never been greater,’ says the introduction to a new trends report published by SmartCitiesWorld. It then shares a range of good examples, from which we can all learn.
Photo by Denys Nevozhai / Unsplash
It’s no surprise to learn that AI is increasingly used to process this data at speed. But what may be surprising is the approach to AI itself taken by some cities.
Usually, AI processing is based in the cloud. That means the actual processing is done by a mass of computers in a data centre – literally, a centralised site – accessed via high bandwidth connection. But so-called ‘edge AI’ is something different: it means data is processed on the edge of the network, on-site or locally to where it is generated.
Why is that advantage? That’s what this new report lays out, with a host of site-specific examples already in operation in cities around the world. The report covers three areas of city operations: smart lighting, urban mobility and public safety.
The examples range from Abu Dhabi, where thousands of streetlights process their own data to adjust light levels in real time in response to ambient conditions and the presence of pedestrians, to the smart lighting system in Barcelona that assesses its own state of ‘health’ and need for repair.
Other examples of smart streetlighting include the public Wi-Fi infrastructure incorporated into streetlight poles in San Diego, and CCTV incorporated into poles in Amsterdam.
On how edge AI can enhance urban mobility, the report cites a pilot scheme conducted in Vienna, where AI-enabled traffic cameras led to a 25% reduction in wait times at pedestrian crossings, supporting wider efforts to encourage active transport.
In public safety, there are examples from Doha of a system that can recognise smoke and fire, and a pre-emptive system for avoiding the risk of crush in the metro system in Madrid.
There are insights, too, from lessons learned, such as changes made following concerns over public privacy. Indeed, the report argues that edge AI avoids intrusive data collection associated with centralised processing.
‘Edge AI is not a future technology,’ concludes the report. ‘It is already being embedded in city systems, often quietly but powerfully transforming how urban spaces operate.’
- Read the full trend report: Edge AI – transforming city operations and services
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