Cities are finding it becoming more and more necessary to make sense of the massive amounts of information they collect. The smart cities market was valued at $739 billion in 2020, and can reach an estimated over $2 trillion by 2026. As smart cities grow, they will only continue their search to utilize data. With mobility being a large part of a smart city, it will undoubtedly be a significant area of focus for AI and machine learning.
Some of the key areas in which AI can use data in city environments are traffic management, public safety, electrification, and public transit. In Phoenix, Arizona, smart traffic systems have been tested with NoTraffic, an autonomous management technology. These systems use traffic data to update traffic light settings automatically, moving away from a traditional time-based system.
When it comes to public transit, European and Asian countries have led in more autonomous transit systems, which run on newer technology. For example, Singapore has rolled out a first-ever pilot of autonomous buses over a period of three months. However, while the regular use of autonomous public transit without a driver for backup is still a ways away, smart systems have already been controlling more of city transportation in municipalities around the world. These systems can help reroute transit to offset traffic and reduce congestion. They analyze the number of travelers and time delays to provide immediate intervention.
In the future, we might very well see all municipal transportation controlled by an AI-based transportation hub, which will allow each of the transit systems to communicate with the other, ensuring that travel is efficient and organized.