by Krissy Lyon 

Think about your daily commute. How many times have you hit every green light while driving or stepped out your front door just in time to catch your bus? If you’re like me, then your answer is probably never. But what if you could catch the bus right on time every day? Hundreds of Silicon Valley companies already collect and analyze data in hopes of streamlining our daily lives. Could recording New Yorkers’ or Seattleites’ daily commutes and crunching the numbers lead to cities where everyone hits every green light and always catches their bus? That’s the hope of smart cities.

What is a smart city?

Smart city is a term for a city that uses technology and data collection to more efficiently provide its citizens with utilities and services.  In a smart city, analysts collect data on what roads are heavily used, where traffic jams occur, and how long drivers wait at traffic lights. Changes can then be made to roads or traffic light timing to reduce time spent in traffic for all. This data could also allow for ways to incentivize traveling outside of high traffic times or to design better public transportation. Applying data analysis to traffic patterns is just one way a city could be ‘smart.’ Other community resources and utilities such as water, electricity, trash collection, libraries, hospitals, and law enforcement could also be monitored and optimized.

How does a smart city work?

Have you ever stopped at an intersection where the green light is only activated when a car is present? How does the traffic light ‘know’ your car is there? Currently, intersections use detection methods, such as a laser, that rely on the presence of your car to signal to the traffic light that you are waiting for it to turn green. Since many drivers already use their smartphones to navigate from point A to point B, a smart stoplight that was able to integrate signals from drivers’ smartphone could predict your arrival and change the light before you even arrive at the light (Figure 1). Interaction between two different technologies to streamline daily life is just one component of a smart city.

Figure 1. Smart traffic How does a traffic light know your car is there? Smart cities could sync GPS navigation systems to traffic lights to anticipate a car’s arrival. On the left, a car without this technology waits for the light to turn green. On the right, GPS navigation alerts the traffic light of the approaching car and then light turns green to allow for a streamlined trip.

Another way to streamline daily life involves data collection and analysis. For example, imagine there is a turning lane you use to get to the stadium of your favorite basketball team. Many people will use this turning lane before the game but probably not during or after. A smart city would collect data on the traffic patterns at this intersection and develop algorithms to predict when that turning lane will be heavily used. Using this data and a schedule of the season’s games, the turning lane light could ‘learn’ to stay on longer right before the start of a game. Similarly, more buses and trains could run to this area before the game and away from it after. Buses taking people home from work right before that game could be re-routed to avoid that congested area.  Amsterdam has already implemented a smart traffic management program and reports a 10% decrease in time spent in traffic with plans to connect in-car navigation equipment in the future.

Another example comes from Santa Cruz, California. In efforts to prevent crime, Santa Cruz turned to the data. A local mathematician found that home burglaries and car theft happened in ‘waves’, that is if a burglary occurred in one city block, it was more likely that other burglaries would occur nearby (Figure 2). Through analysis of 11 years of data, an algorithm predicted where crimes might occur. Indeed, this program resulted in a reduction in property crime. While Santa Cruz did not use control areas, that is areas where they predict crimes to occur but do not patrol, the Los Angeles Police Department used a similar method and included this control. Here, they found that their algorithm correctly predicted crimes 2.6% better than professional crime analysts. Jeffrey Brantingham, a UCLA professor and lead author on a study of this method known as predictive policing, noted that the success rate could continue to be improved as more data is collected, “in much the same way that your video streaming service knows what movie you’re going to watch tomorrow, even if your tastes have changed, our algorithm is constantly evolving and adapting to new crime data.”

Figure 2. Crime prevention in smart cities Home burglaries and car theft happen in ‘waves’. In Santa Cruz, a mathematician developed an algorithm that analyzes where crimes occurred, shown here as triangles with exclamation points, and predicts where crimes might occur, shown here as a red area, for police to patrol.

Beware the smart city

While smart city technologies aim to improve lives, a big concern for many critics of smart cities is privacy. Questions arise about who owns the data, what the data is used for, and how personalized that data is. One company, Renew London, received criticism for technology that aimed to place a sensor on trash bins to detect the WiFi signals from passerby’s smart phones. You could imagine this information being used to track how often that trash bin was used and better manage trash pickup. In reality, the program used information about an individual’s route through the city, detected by passing these sensors, to target advertisements to them.

Linking GPS on smartphones and traffic lights may improve traffic flow, but this would require drivers to consent to providing data on their car’s location. In Singapore, such plans are already in the works in an initiative called “Smart Nation.” The government plans to require cars to have GPS systems monitoring their location, speed, and direction, allowing for automatic charging of any parking fees and tickets. Yet, this will also be a real-time tracking device for every citizen with a car. How this data is protected is a large concern. If this data isn’t properly secured, it could be at risk to hackers.

The smart city has incredible possibilities for improving our daily lives, but this would come with many new questions about data management and privacy. A recent poll in the US found that 84% of respondents were concerned about the privacy of their personal information. This concern was higher among younger respondents. Yet, a 2015 poll in the UK found that more than a third of 18-24 year olds were passionate about their city becoming smarter and would even consider moving to a city smarter than their own. Therefore, while there is some enthusiasm for smart cities, there are certainly reservations about privacy as well. To improve lives, smart cities will need to be smart with privacy, too.

Krissy Lyon is a PhD candidate in Harvard’s Program in Neuroscience.

For more information:

  • To learn more about crime prevention in smart cities, check out this article on algorithmic policing

This article is part of the 2018 Special Edition — Tomorrow’s Technology: Silicon Valley and Beyond

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