Smart Cities Get Smarter With Technology

Posted by Mark Weitner on Nov 16, 2018 7:00:00 AM

Today, more than half of the world’s population live in cities. The United Nations is forecasting that this will increase to over two thirds by 2050. This population growth and rapid urbanization will not only increasingly challenge how cities are managed but how cities apply technology to address these challenges.  The application of new technologies allows cities to improve how assets and resources are managed to increase efficiencies, improve quality of life and reduce costs to taxpayers.

Two technologies seeing accelerated adoption within the smart city technology solution landscape are the Internet of Things (IoT) and artificial intelligence (AI). In their simplest of forms, IoT provides for the real-time collection of data from Internet connected devices while AI allows for the processing of large amounts of data and types or data from one or many sources to provide actionable intelligent insights. Of even greater value when combined, the application of IoT and AI offers cities the unique capability to optimize their operations and services. 

Three very different examples help show the value these technologies are being used to help smart cities get smarter.

 

A Simple Case of IoT Enablement

Driving in a city environment can be frustrating. Adding further to this frustration are the challenges faced when a driver searches for a place to park, often driving for long periods in circles to find that ever elusive spot. A study from INRIX Research found that, on average, drivers in the United States spend 17 hours per year searching for parking at a cost of $345 per driver in wasted time, fuel and emissions.

The application of IoT technologies helps reduce these frustrations and costs. By using real-time parking sensors, the location and availability of available parking spaces can be tracked thus optimizing available parking. IoT sensors show drivers where the nearest parking is without going around blindly in circles. Not only is time saved by finding parking in less time, but traffic jams and air pollution are reduced as well. And in the long term, the wealth of IoT data collected will allow the cities to make future planning and pricing decisions as well.

 

Improving City Travel Using IoT and AI

SmartCities-FractaAI-2Even before locating that perfect parking spot however, commuters must often face the daunting task of traversing a city’s geography. By combining the massive data collection aspects of IoT and the analytics of artificial intelligence within solutions referred to as Adaptive Signal Control Technology, commute times can now be drastically reduced by allowing traffic lights to change their timing based on real-time data.

According to the Department of Transportation (DOT), “On average [Adaptive Signal Control Technology] improves travel time by more than 10 percent. In areas with particularly outdated signal timing, improvements can be 50 percent or more.” Given that traffic congestion costs the country $87.2 billion in wasted fuel and productivity (according to the Department of Transportation), there is a strong reason why numerous cities and companies are deploying these IoT and AI-based technology solutions around the country.

 

Making More Intelligent Underground Infrastructure Management Decisions

However, smart cities aren’t just about reducing commute times and greater ease of parking. While citizens will certainly enjoy the benefits from these two examples, the application of AI and IoT offer their greatest value in addressing what is becoming the most serious challenge facing cities, and their residents, today – aging underground infrastructures. As these infrastructures fail, they represent not only unique challenges in locating where these failures are (due to their lack of easy visibility below ground), but also significant disruptions to everyday life and the highly intensive capital and financial impacts to city operations borne directly by taxpayers.

The reality is that much of our nation’s underground infrastructure are reaching or functioning beyond expected lifespans. This critical network of pipes and mains serve our most basic drinking water and sanitary sewer needs. Many municipalities are faced with the task of replacing or lining deteriorating facilities while minimizing service disruptions for residents and businesses resulting in service interruptions, soaring costs, increased taxes, pressure to raise water rates and the strain on available municipal resources.

By 2050, the American Water Works Association (AWWA) estimates that within the United States alone, taxpayers will spend an estimated $1 trillion to address the rehabilitation and replacement of the more than one million miles of aging buried pipe. To put this in perspective, by 2050, the US will have an estimated economy of $34.1 trillion. This means that in the US, approximately 3% of the total US economy will be needed to rehabilitate and replace buried water pipes. Quite a cost to bare not even considering the disruptions resulting from pipe failures and inconveniences faced when access to drinking water is limited.

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The fact is that any man-made structure requires ongoing maintenance, repair and even replacement at some point in their life. Buried aging water pipes are no exception. The challenge then for municipalities is to best understand the likelihood of failure of buried water pipes to better anticipate when and where failures will occur so they may plan for failures and better control of budgets, reduce disruptions to commerce, minimize the loss of valuable water resources with fewer (and far more expensive) surprises when unexpected failures do occur. 

 

Using IoT, AI and Machine Learning to Assess Pipeline Likelihood of Failure

With more than one million miles of buried pipe in the US, imagine how difficult is it to determine when this buried pipe requires maintenance, repair or replacement before failure occurs. The traditional methods of making physical condition assessments are indeed accurate, but they tend to be slow, expensive and labor intensive and only effective for those pipes assessed. The use of IoT and AI (particularly that branch of artificial intelligence called machine learning in which systems learn from data, identify patterns and make decisions with minimal human intervention) technologies offer a more robust and far less expensive alternative to accurately predict Likelihood of Failure (LoF).

From a historic data perspective, most utilities have already inventoried and digitized their pipeline infrastructure assets using Geographic Information Systems (GIS) and Computerized Maintenance Management Systems (CMMS) or Enterprise Asset Management (EAM) systems. These systems help utilities more productively capture, store, retrieve, manage, analyze and visualize asset data spatially and help track the maintenance of these assets offering a rich and plentiful source of information in which to apply AI and machine learning to accurately assess asset condition and more efficiently predict likelihood of failure.

And with the availability of newer, and less costly sensor technologies, IoT can be used to collect more real-time information on other factors that influence LoF. These include the continuous collection of microclimate zone data such as temperature and precipitation to geographically specific activity data such as soil shifts and above ground activity flows resulting from traffic and construction. Incorporating these more targeted and constantly changing real-time data points to the already available historic pipeline infrastructure data offers municipalities the opportunity to significantly reduce disruptions to everyday life and make more intelligent decisions on highly intensive capital projects reducing what often are significant financial impacts on city operations.

 

IoT and AI in the Smart City Framework

From driving and parking to core city infrastructures, IoT and AI are proving to be increasingly valuable components in a smart city framework. Nowhere are the long-term benefits seen more than in managing capital-intensive infrastructure programs. As IoT and AI technologies are increasingly applied to such programs, municipalities will better optimize how assets and resources are managed resulting in increased service efficiencies, reduced disruptions to city commerce, improved quality of life and maybe most importantly for citizens, reduced costs to the taxpayer.

 


 

Author: Mark Weitner, Operations Risk Expert  

As an Operations Risk expert, Mark Weitner delivers technology-driven business solutions that consistently achieve short-term objectives while building long-term success for industrial operations. With more than 35 years of executive management experience, Mark is an internationally recognized business leader, trusted strategist, innovator, thought leader, published author and speaker.

 

Topics: Risk Management, Capital Planning

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