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Using Big Data to reduce risks and optimize infrastructures

What is Big Data

Most of the world’s population lives in cities. In fact, according to the Brookings Institute, 75% of people will be living in cities by 2050. All over the world, cities are facing the inevitable consequences of rapid growth. 

Citizens are expecting always more from their cities. They want to ensure their quality of life. They also want to be reassured when they feel threatened by natural or man-made disasters. To meet these expectations, many organizations are experimenting with the opportunities that lie in using Big Data. The objective: to create safer and more efficient cities. 

What is Big Data? 

Put simply, Big Data constitutes larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. (Source: Oracle). 

These massive volumes of data can be used to solve problems that were simply unassailable before. They can also be used in a very concretely to reduce the risks related to public infrastructures. 

One opportunity that has emerged from the use of Big Data is the ability to break the isolation between different data sources. As a result, it’s possible to optimize infrastructure life cycles by analyzing their construction, maintenance, safety, and performance. 

Improving transport planning 

As mentioned in a previous article, better project planning reduces financial and scheduling risks. 

During the design phase, data collection may include information gathered from future users. It is also possible to collect, among other things: 

  • environmental analyses; 
  • stakeholder concerns; and 
  • discussions on social media between citizens impacted by the project. 

Designers have the opportunity to analyze all of this information to evaluate different alternatives. 

In the United States, Brown University’s management, in collaboration with the architectural firm Sasaki Associates, developed a data entry method as part of a campus planning project to analyze student and staff movements. 

The resulting data provided a clear picture of how students use the campus and revealed an impressive number of interconnections between the different departments. It led the university to build its new engineering school near the central campus rather than relocating it to another part of town, as originally planned. 

Optimizing road maintenance 

Infrastructure maintenance is a real headache for municipalities, but they may find a solution in Big Data. 

In Kansas City, public works and IT officials are collaborating to analyze data on traffic, pavement age, weather anomalies, and more. This allows them to predict where the next pothole will form. This preventive analysis allows 35 to 45 miles of roads to be repaired or resurfaced per year instead of 20 to 25 miles. 

Citizens can also contribute to the data pool. In Boston, the Street Bump application allows residents to help maintain roads in their neighbourhood. The sensors on their cell phones (accelerometer and GPS) collect data to locate potholes.

 

Ensuring the safety of road infrastructure 

The City of Montreal has been closely monitoring the state of its infrastructure since the collapse of a paralume (grid-like ceiling in highway tunnels) on the Ville-Marie Expressway in July 2011. Although this tunnel is under the jurisdiction of the Ministry of Transport, this event was an electrifying event for the city officials. In 2017, 98% of the city’s bridges and tunnels were inspected. No fewer than 222 were subjected to a general inspection, while 348 were subjected to a quick inspection. 

With the advent of Big Data, it’s now possible to monitor tunnels and bridges constantly, rather than sporadically. Sensor networks can be deployed to monitor the health of various infrastructures. These sensors regularly check the structures and transmit data autonomously when they detect a crack. This type of system is mainly used for bridges. However, it can also be used to monitor other at-risk infrastructure. 

For instance, real-time data analysis opens the door to improvements in road safety. In Detroit, new traffic lights can give priority to emergency vehicles like ambulances and police cars. But they also make crossings safer for cyclists and pedestrians. Lights can extend green signals for cyclists, allowing them to cross the intersection in time. In the future, the City of Detroit plans to use the data collected to improve the design of the intersections themselves. 

Reducing losses 

In Florida, Miami-Dade County and IBM have undertaken a smarter cities initiative that includes a predictive analysis project to remotely monitor consumption and identify water leaks across the Parks, Recreation and Open Spaces Department. This project will reduce water consumption by 20% and generate up to $1 million in savings per year. These surpluses will be reinvested into services for residents. 

Energy savings can also be achieved in other areas, such as road lighting. A new dynamic system is currently in use in Norway that allows rapid control of light levels as vehicles move along the road. Radar units detect traffic along the road and its speed. The lights are then increased to full brightness in the vehicle’s path. 

Conclusion 

Construction projects generate a lot of data, and it’s often unstructured and kept in silos. Collected data, especially on paper, is simply filed away and forgotten at the end of the project. 

It’s now becoming clear that predictive analysis and data sharing can provide important information. 

Big Data is one of the new approaches that must be considered during the development, planning, and optimization of the infrastructure life cycle, as it can create more efficient and resilient infrastructure.