Applications Of Artificial Intelligence In Smart Cities

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Introduction

Artificial intelligence for smart cities   mainly focuses on three major categories of applications:

  1. Helping officials learn more about how people use their cities.
  2. Improving infrastructure and optimizing the use of these resources.
  3. Improving public safety in cities.

AI learns the behavior of people in cities

Cities have wealth of possible data sources, such as ticket sales on mass transit, local tax information, police reports, sensors on roads and local weather stations. The main source of raw data for AI pattern recognition technology is video and photos. It is predicted that there will be more than one billion cameras deployed on government property, infrastructure and commercial buildings by 2020.That is far rawer data than could ever be viewed, processed, or analyzed by humans. This is where deep learning comes in. It can count vehicles and pedestrians. It can read license plates and recognize faces. It can track the speed and movements of millions of vehicles to establish patterns. It can process the huge volume of satellite data to count cars in a parking lot or track road use.

The data can be used to identify parking spots for drivers, help first responders, and identify dangerous intersections.

A straightforward application of machine processing video for cities is license plate recognition (LPR), which is used in numerous ways.

AI optimizing infrastructure for cities

A large amount of existing public infrastructure is underutilized, overused, or used inefficiently due to a lack of real time information among individuals, companies, and government agencies. Drivers don’t know where parking is available. Passengers not knowing how long a bus will take have lower ridership than buses they can track. Cities for the most party don’t know what is the right length for a stop light at every given minute. These are issues companies and government are addressing.

Anyone who has spent 10 minutes driving in circles in a city trying to find a parking space should be aware of this problem. It is a waste of your time, and circling around increases downtown traffic, wasting everyone else’s time. It may seem like a minor inconvenience, but multiply that by millions of people each day in hundreds of cities, and it adds up to a significant waste of net resources. This may also result in traffic jams in major cities and towns.

Smart Parking Garages

The amount of available parking is displayed outside the garages on large LED signs and shared with an open platform for use by app developers. It provides the immediate benefit letting individuals know where parking is available, and in the long term, the wealth of data collected will allow the city to make planning and pricing decisions.

Adaptive Signal Control Technologies

Adaptive Signal Control Technology allows traffic lights to change their timing based on real time data.

Connected Public Transit Technology

This technology allows buses and trains to communicate with each other and the general public. Letting individuals know when buses or trains are coming and if they are going to run late makes them more useful to individuals.

The Massachusetts Bay Transportation Authority was the first agency to make bus locations and arrival-time predictions available, allowing developers to create tracking apps. Research on the impact of real time information on bus ridership in New York City found that it increased weekday route-level ridership by 1.7%.

AI Improving Public Safety

Smart cities aren’t just about reducing commute times and saving on fuel. The same networks of sensors and cameras are being used to save lives and fight crime. The same LPR technology used to track parking is used by law enforcement to find stolen cars and track criminals. By 2014 LPR was already being used by an overwhelming majority of local law enforcement. The same intelligent traffic lights normally used to improve traffic flow are utilized by ambulances and fire trucks to get to the scene of an emergency quicker and more safely. ShotSpotter, a company that automatically locates gunfire based on a sensor network, has its technology embedded in GE intelligent street lights. Last year ShotSpotter alerted law enforcement to 74,916 gunfire incidents. In its recent IPO it received aggregate proceeds of approximately $35.4 million. Highly connected cities also provide the ability to give individual hyper localized warnings about possible natural disaster. Do to its unique geography rainfall can vary significantly throughout the city of Seattle. To address this they created Rain Watch which combines radar data with a network of rainfall gauges to monitor rainfall with a high degree of resolution. It allows city maintenance workers to more quickly respond to possible problems and provide more accurate flood warning to residents.

Conclusion

The grand long term vision of smart cities is full inter connectivity: Self-driving cars, trucks, and buses all talking with each other as well as with smart highways, traffic lights, and parking garages. The whole system will be working together to move people around with an incredible degree of efficiency and safety. A highly connected system that will save lives, save time, and save fuel. A reality that will be made more possible as the federal government moves towards requiring vehicle-to-vehicle communication build into new vehicles in the coming years. It is also to provide engineers and city planners with an incredible wealth of data that can be used to promote safety, health, and economic growth. Right now researchers often rely on rough estimates of how people are using most roads and bike paths, but in the future they could have access to a minute by minute breakdown of every block. The important thing is that we don’t need to see any new technology developed to see massive gains from cities becoming smarter. We have existing technology proven to be capable of improving parking utilization, safety, and significantly improving traffic; it just hasn’t been widely deployed yet. For example, less than 1 percent of traffic signals in the United States are smart, but the federal government, local governments and many large companies hope to change that. As a result, the potential for growth for these systems and the companies that make them is significant. It is not surprising, then, that numerous large companies (Siemens, Microsoft, Hitachi, others) have put an increased focus on smart city technology. Beyond the industries directly providing these services to local governments, the spending on smart cities could impact a range of businesses. Reduced traffic would mean cheaper shipping and technicians being able spend more time at job sites and less time moving between them. Fewer accidents could result in lower insurance costs for everyone.