The helicopter landed in a rush of air. I’d called emergency services just 10 minutes earlier, and the dispatcher told me that no vehicular ambulances were available in my area. Real-time traffic information helped personnel make a split-second decision to dispatch an air ambulance to my street corner. It airlifted my mom to the nearest hospital, preserving her life and her quality of life.
Examples like these, and so many more, have led to the global rise of digital cities — cities where data-driven decision making positively affects citizen outcomes. As their populations grow, cities are showing signs of stress in the form of congested streets and highways, rising crime rates, rapidly spreading disease, and a host of other challenges. Limited resources place an added burden on city infrastructure and resources.
These are among the challenges that city leaders can address using artificial intelligence and related technologies that are at the heart of the digital city. For example:
- Digitally transformed cities keep their residents better informed with real-time updates and serve their residents with services on tap round the clock. These smart cities use AI in a wide range of use cases to make our cities safer, more livable, and healthier for all residents.
- Data privacy and security capabilities protect citizen data from cyberattacks.
- Open standards and modular, scalable systems allow for low initial investments with the ability to scale as city needs evolve.
- Digital cities integrate disparate systems to enable easier access to municipal services, provide sustainable environments, and foster innovation.
AI use case examples
Let’s take a closer look at a few of the ways that AI-driven applications help municipalities address pressing challenges.
Safe cities
In digital cities, the combination of AI-driven applications and data from connected devices aid public safety agencies in deterring and solving crimes. These applications leverage deep analytics to help public safety personnel keep a close watch on the city. Along with AI-driven applications, these systems often include optical sensors and video management software to enhance incident management, visualization, analytics, and search.
For example, AI-driven systems can help police and other public safety officials detect persons and vehicles of interest, perimeter intrusions, and unclaimed objects that could pose a threat to life and property. Similarly, used with sound sensors connected to street lights and other municipal infrastructure, AI-driven applications can help police detect gunshots, “man down,” and other indicators of violence in real-time.
In another safe-cities application, data scientists employ predictive tools to help identify where crime is likely to occur during specific days of the year. This capability allows law enforcement to target patrols in certain areas to prevent crime. These applications combine and analyze data generated from diverse resources — such as past crime analytics, drones, body-worn cameras, and facial and image recognition systems.
Urban mobility
AI-driven applications help city transportation agencies improve safety, reduce congestion on streets and highways, keep traffic moving at more predictable rates, and improve air quality.
A few examples:
- Systems with sensors detect traffic violations — such as running red lights, exceeding speed limits, and driving the wrong way — and can automatically issue traffic citations.
- Adaptive traffic light systems detect traffic flow patterns and automatically adjust light sequences to keep vehicles moving, alleviating traffic congestion and pollution from gridlocked vehicles.
- Predictive traffic analysis enables more accurate planning for road network expansion or other road infrastructure changes, such as changing dual carriageways to single ones. The impact of such changes can be predicted with significant accuracy using modern tools.
- Several digital cities are now using AI algorithms to enable mobility as a service (MaaS). These applications make it easier for users to select optimum means of getting to their destination using a range of multimodal transportation systems — from rideshares and cars used as a service to subways, buses, and other public transportation modes.
Primary considerations for capitalizing on AI
To better understand the challenges that city IT personnel encounter when implementing these types of use cases, I turned to my colleague Harjeet Singh Rekhi, the worldwide general manager for digital cities at Dell Technologies. Harjeet outlines several significant considerations.
Integrate data sources.
Harjeet notes that many AI applications leverage data from multiple sources, such as air quality and lighting sensors, building management systems and HVAC sensors, optical sensors, citizen mobile and desktop apps, first responder emergency lines, social media sentiment analysis, land records and so on.
“To enable applications such as these, the city’s multidisciplinary departments must agree to share data and build the systems and processes to connect data from their individual siloed systems,” he says. “One way to accomplish this is to consolidate data into a common data lake for shared use by multiple applications, with layers of security and permission-based access built in by design.”
An alternative is to use application programming interfaces (APIs) that provide applications access to relevant data held in different systems. While somewhat limiting, this could be a solution in the short term to access data from disparate systems.
Leverage analytics at the edge.
Many digital city applications require data analysis in near real-time, as the data is generated, Harjeet points out. He cites the examples of “vehicle-to-everything” (V2X) connections for autonomous vehicles and real-time traffic analysis for adaptive traffic light systems.
“The speed of response needed in such systems does not allow for centralized processing,” Harjeet says. “With applications such as these, data must be analyzed at the edge, close to the point where sensors, cameras, and other IoT devices capture it. There often isn’t time to send data to a cloud data center for analysis.”
In many cases, analyzing data at the edge is more cost-effective than using network bandwidth to send data to a cloud or municipal data center. For example, video analytics at the edge saves precious bandwidth by processing video content nearer to where it is acquired and sending only metadata to a data center.
Look for scalable, open solutions.
Where possible, AI solution stacks should be based on open systems that are easy to upgrade and easy to scale over time as city needs and technology evolve, Harjeet advises.
“It’s also a good idea to look for converged solutions that deliver processing, storage, networking, management, and security components in tested and validated configurations,” he says. “These ready-to-deploy solutions can help organizations move AI applications into production quickly, without the complexity of building a solution from the ground up.”
Choose the right technology partners.
Finally, to develop and deploy AI applications, organizations don’t need expertise in the underlying technologies. They need to choose partners who understand the full scope of the challenges, from data capture and integration to analysis at the edge, the data center, and in a public cloud, Harjeet advises. These partners should build in data security and privacy capabilities at each stage of the data life cycle — acquisition, transmission, processing, presentation, and archiving.
Key takeaways
The pace of urbanization is accelerating as people migrate from rural areas to cities in search of economic opportunities and a better quality of life. This trend is projected to continue over the next three decades, according to the United Nations. By 2050, 68 percent of the world’s population will live in urban areas, up from 55 percent in 2018. And by 2030, the world will have 43 megacities with more than 10 million inhabitants.[1]
With this increasing urbanization, cities need to make greater use of AI-driven applications and systems. These applications and systems promise to make our cities safer, easier to navigate, and healthier places to live and work, while streamlining municipal operations and providing a more nurturing environment for businesses.
Ultimately, it is vital to understand citizen needs and to design systems around citizen outcomes — for today and tomorrow. The digital transformation of cities is a journey that requires a long-term vision and constant evolution.
To learn more
For a deeper dive into the topics explored here, visit the Dell Technologies digital cities site, “Building foundations for our urban future.” And for a graphically oriented look at the digital city, see the Dell Technologies infographic “Defining the Digital Future of our Cities.”
Learn from and engage with leading players in the industry who are pushing the boundaries in AI and data analytics. Join us at AI & Data Analytics Re-Imagined.
[1] United Nations news release, “68% of the world population projected to live in urban areas by 2050, says UN,” May 16, 2018.
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