Using data to create sustainable cities of tomorrow

Over the past 20 years, the city of Istanbul in Turkey has doubled in size – making it twice as big as New York City, US. This rapid population growth has led to an increase in traffic in the city, and consequently, generated a great deal more data, too. 

To accommodate this, statistical software suite SAS worked with the Istanbul Metropolitan Municipality (IMM), to leverage data analytics to build models of traffic patterns in the city. Jason Mann, Vice President of IoT at SAS, explains how data analytics is being used to improve the sustainability credentials of cities across the globe and why SAS’s model can be applied to other growing cities.

SAS’s cloud-native AI platform

The city of Istanbul used to have rush hours between 8-11 AM every morning and 8-9 PM every evening, according to the Head of the Transportation Center at Istanbul Ticaret University. However, like with many major cities, these periods grew longer and larger. This means that traffic is worsening, causing citizens to drive slower for longer, burning more fuel, releasing a greater volume of emissions, and leading to more drivers breathing polluted air for longer.

As a global leader in advanced analytics, SAS provides organisations in a host of industries with complete choice and control when analysing large sets of data – such as a city’s transportation data – through SAS® Viya, the business’s cloud-native AI platform. 

Additionally, SAS Viya provides flexible and scalable power quickly and intelligently, by providing insights that enable better strategy and operational excellence – improving traffic patterns for citizen safety, reducing emissions, streamlining energy costs and preventing flooding.

By using a network of connected devices and SAS Viya, IMM is able to see when and where traffic is peaking in the city. More importantly, analytics help transportation leaders predict and act on changes in traffic before they occur. With this information at hand, IMM can forecast and manage the city’s traffic challenges using congestion alerts, warning citizens of delays and surges before they find themselves trapped in gridlock.

“We are fortunate to work with the IMM, the local government for the city and surrounding districts,” Mann says. “Using SAS Viya and SAS Event Stream Processing’s advanced analytics, IMM’s team of city planners and engineers have built models of traffic patterns in Istanbul that help them better understand, predict, forecast and manage traffic flow in real-time.

“By predicting the best routes to alleviate traffic congestion, the municipal government is reducing the volume of cars and trucks idling on roadways and shortening travel time for those already on the road, both of which contribute to increased emissions. 

“For bus routes and other public transportation, insights from this newly available traffic and passenger data also improves services, route availability and rider satisfaction, which increases the likelihood that people use these greener modes of transportation. In all cases, locals are better informed on the best routes for their travels, leading to less time on the road and fewer emissions overall.”

The use of advanced analytics enables IMM to ensure that drivers and riders are on the most efficient and green routes to their destinations – despite the consistent “rush” each hour. This poses a significant step towards a smarter and more sustainable city. What’s more, IMM’s data strategy has taken raw, unprocessed data at an immense scale and made it consumable, not just by analysts, but by its own citizens for their benefit. According to Mann, a commuter can “hold the entirety of Istanbul’s traffic patterns in the palm of their hand” as they walk out their front door and around the city, enabling them to feel confident and well-informed about their government’s transportation system.

How data analytics improves cities across the world

Although the city of Istanbul has seemingly found a solution to combating the impact of air pollution, other cities around the world have not – and it’s a pressing matter that plagues many areas of the globe. 

Mann calls for municipal, local and state governments from countries with major metropolitan areas to employ better data analytics to track traffic. By doing so, counties will not only be able to decrease emissions, but enhance citizen safety, too. 

For the IMM strategy to be replicated, governments first need to determine and understand the data they already have. From here, they can assess how it is being analysed, while also asking whether it is being used to forecast traffic, emissions and other key factors.

“An initial step is to work with technology firms and consultants to implement a pilot programme. Start small and scale up” Mann explains. “Using an initial pilot programme to apply IoT analytics is an approach used across industries.

“In manufacturing, for example, Austria-based Wienerberger Group – the world’s largest brickmaker – was searching for ways to reduce energy consumption while lowering greenhouse emissions and improving product quality. Wienerberger turned to AI and IoT analytics from SAS, running SAS Viya on Microsoft Azure Cloud, to help optimise energy costs and achieve its ambitious sustainability goals. By 2023, the company aims to reduce emissions by 15% compared to 2020 and be completely climate-neutral by 2050.”

What’s more, SAS’s analytics has helped other countries around the world, including the city of Jakarta, Indonesia. AI, machine learning and real-time data streaming have led to major improvements in disaster awareness, particularly important as the city has 13 major rivers and 40% of its area is below sea level, leading to a long history of flooding. Climate change has had a major impact on these rivers, causing flooding to worsen in recent years.

“Using SAS Analytics for IoT, Jakarta’s city government has created a data- and AI-powered flood control system similar to Istanbul’s approach to traffic congestion,” Mann explains. “Jakarta has aggregated data from sensors and weather forecasting across the city into intelligent models, which can predict water levels around high-risk areas. City officials can send push notifications to residents’ phones, close floodgates and prepare the city as much as six hours in advance of emergency flooding. Jakarta’s smart models not only prevent potential damage to the city but protect the lives of citizens living in flood-prone zones.”

Cary, North Carolina, is another example of how analytics has helped a government regain control of a city after the population has tripled in size over the last 25 years. Cary, therefore, saw an influx of residents, which led to a boom in new housing, shopping centres and businesses. Mann explains that now, the town has a team dedicated to using “cutting-edge technology” to benefit the community, including IoT analytics to safeguard against flooding events. 

Many more communities can emulate Istanbul, Jakarta and Cary by using data and analytics to drive better and faster decisions that support sustainability and smart-city goals.