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Autonomous City Management with Urban Analytics – Hype or Reality?

Imagine this scenario – It’s a little after 2 pm and there is a major music concert happening later in the evening with large crowds expected to gather at the venue and surrounding downtown areas of the city. As the event draws closer, the city manager is in the city operations control center viewing the action on a large dashboard screen. He observes traffic density growing on the main roads leading into the city center that could cause congestion and traffic snarls.

Cities like New York, Los Angeles, Chicago, Philadelphia, Washington DC, Houston, Berlin, Amsterdam, London, Paris, Vienna and Stockholm have published their historical data on Open Data portals with many more cities around the world planning to do the same. Examples of such datasets include crime rates, revenue from traffic violations, historical pollution levels, property tax collection, vehicle density on roads, and traffic camera locations. These datasets are being used by companies and universities to develop new applications that could discover actionable insights to help the city manage its urban services delivery in a more efficient and cost-effective manner. One of the highest value adds that an AI based urban analytics platform can deliver is by combining historical data from open datasets with real time sensor data to identify actionable insights for the city in the areas of parking, street lighting, traffic, security surveillance, environmental monitoring, water, sewage, waste management and other urban services. From an evolving technology point of view, we are now entering a phase where the development of AI models having predictive, prescriptive and contextual analytics capability is becoming real and initial deployments are happening in several cities around the world. Some examples are New York City predicting bridge problems on the Brooklyn Bridge before they happen, San Jose using data analytics and GIS analysis to identify priority traffic corridors where most major injury accidents occur, cities like Boston, Philadelphia, and Raleigh have begun taking steps towards route optimization for trash trucks by deploying smart bins that notify the appropriate agency when full and Seattle using predictive policing software to identify where a crime is most likely going to take place on a specific day.

To conclude, the emergence of AI based urban analytics platforms when combined with the growing deployment of wired/wireless IOT networks and ruggedized sensors on the street to capture real time data will fast track the implementation of autonomous city management services with urban analytics capability for cities to leverage and deliver real value from their smart city / IOT solution investments. So, the next time you observe the parking rates go up in your city on a game night or traffic lights turn green faster than normal when you are the only car waiting at a traffic junction or crime rates going down due to better street lighting and more targeted policing, you now know that autonomous city management with urban analytics is more real than a hype.

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