According to the 2016 UN-Habitat World Cities Report, by 2030, at least 60 percent of the world’s population will live in cities, with one in every three people living in cities with at least half a million people. During that same time frame, developing countries’ urban populations will double, and the number of megacities that contain more than 10 million residents will continue to rise. Mass urbanization is on the horizon, so where do big data and smart cities come into play?
It’s not hard to guess how city governments will be impacted by the projected influx of humanity: crime, safety, sanitation, infrastructure maintenance, traffic. The limits of city systems will be tested as urban dwellers increase.
With this level of population growth on the horizon, leveraging big data and smart cities is one answer worth exploring. When it comes to the adoption of connected technologies, cities fall into one of three categories:
Today, the city of Las Vegas is one of the leaders in connecting big data to city systems. It rolled out a connected vehicle pilot project with GENIVI, a nonprofit alliance committed to the adoption of connected car technology. Information from the project vehicles is used to help the city improve pedestrian safety and traffic flow. The project’s goal is to show how in-vehicle data can integrate with the city’s transportation infrastructure and deliver information about road conditions to make drivers more aware of other road users, including pedestrians.
Many big data users realize the presence of data from one project often inspires new use cases—this was true for Las Vegas. The city is now considering the use of vibration data gathered from these cars as a way to target pavement rehabilitation projects.
Las Vegas envisioning new ways to use its data is a common occurrence with projects like this. While big data projects begin with defined goals, other benefits are often realized once the data is available.
The City of Los Angeles LED Streetlight Program is another prime example. The project’s goal was to convert all the city’s sodium vapor lights to smart LEDs. As Ed Ebrahimian, the city’s director of street lighting, notes, Los Angeles is the second-largest municipal street lighting system in America. Therefore, the main motivators for the conversion were money and energy savings.
The return on this investment was quick. The city now saves roughly $9 million per year on its energy costs. There have been other positive effects of the lighting program as well. Ebrahimian says the city has received reports of the community feeling safer and a drop in night crime statistics after the change.
The combination of big data and smart cities is creating amazing results today. It’s also creating visions of what the future may hold.
Michael Ger, the general manager of automotive and manufacturing solutions at Hortonworks, envisions a future of shared cars that could transform any city’s landscape. “When people envision the autonomous vehicle model, they don’t see one person owning a car—they see shared ownership,” he says. “Imagine people having an app on their smart device where they can ask for an autonomous car to show up, and they use it to go about the city for as long as they need. When they’re done with it, someone else can order the car.”
In Ger’s scenario, cars are able to be used to their full potential, as opposed to a one-owner car sitting in a garage or parking space 90 percent of the time. “The number of vehicles in a city will plummet,” he says. “They won’t need sidewalk, lot, or garage parking. Think how the city landscape would change: sidewalk cafes could replace sidewalk parking; parking lots could become parks.” That’s a compelling and hopeful vision for future cities!
As a greater majority of the world’s population becomes city dwellers, the public sector will have to decide if they want to lead, follow, or fall behind when it comes to accommodating residents.
Find out what the Next-Generation Smart Cities conference had to say about how the public sector is capturing, storing, and using big data.