Imagine a city that anticipates your needs. A trash can that knows what to recycle, a traffic system that eases your commute, and a city hall that answers your questions instantly. This isn't a scene from a sci-fi movie; it's the reality being built in cities like Raleigh, North Carolina, and the secret ingredient is Artificial Intelligence.
But before a single line of AI code can be written, there's a more fundamental challenge to solve: data. As experts from a recent TechConnect panel emphasized, the success of any smart city project hinges on one thing above all else.
The Foundation of a Smart City
"You’ve got to have clean data. And you have to have your data in a location that is easy to access,” says Jonathan Minshew, a chief technology and innovation strategist at Dell Technologies. This is the core principle guiding cities as they venture into the world of AI. It's not about migrating massive databases; modern AI tools can often work with data right where it is. The key is that the data must be accurate, organized, and ready for analysis.
Raleigh is putting this principle into practice. John Holden, the city's smart city manager, shared how they're exploring smart trash cans that use visual analytics and AI to learn how to sort waste. "That’s a very contained data set, and we are teaching the model ourselves,” Holden explains. This small-scale, controlled approach allows them to build and refine the AI model effectively.
From Reporting Problems to Finding Solutions
Raleigh has also launched a new AI-powered portal called Ask Raleigh. This tool is designed to streamline how residents report issues or ask questions. Instead of a citizen needing to know which department handles a broken streetlight, the AI figures it out for them.
“It’s designed to identify the right problem, the right solution, the right person — staff — to send a message to, to get the answer, and keep in touch with the person who asked it,” Holden notes. The system intelligently routes requests, saving time for both residents and city employees.
Improving Safety and Flow on City Streets
One of the most powerful applications of AI in urban environments is analyzing video feeds, such as those from existing traffic cameras. This data, which is already being collected, can be used to dramatically improve traffic flow and pedestrian safety.
"AI can tell you at which intersection in the city are people jaywalking the most,” Minshew points out. This insight allows the city to investigate why it's happening and implement targeted solutions, rather than just reacting to accidents. Raleigh is already using video analytics to manage traffic and enhance safety, aligning with its Vision Zero goals.
However, the journey isn't without its learning curves. Holden shared a valuable lesson: when his team added more data related to pedestrian and bicycle traffic to their successful traffic model, its accuracy surprisingly dropped. “You have to be really careful and test these things,” he warns. For a public entity, ensuring the AI's output is based on accurate, reliable data is non-negotiable.
Key Takeaways
As cities continue to embrace AI, the lessons from Raleigh provide a clear roadmap for success:
- Data First: The journey to AI begins with clean, accessible, and well-managed data.
- Start with a Goal: Align AI projects with existing strategic city goals, such as sustainability or public safety.
- Leverage Existing Infrastructure: Use data from current systems, like traffic cameras, to gain new insights without reinventing the wheel.
- Test, Test, Test: AI models must be rigorously tested, especially when new data is introduced, to ensure continued accuracy and reliability.
- Focus on People: The ultimate goal of AI in city services is to make life better, safer, and more efficient for the people who live there.