Smart Interlaced Systems: The Next Frontier of Smart Cities

Imagine it’s a hot, still, summer day in a big city. As people drive vehicles, going about their daily lives, emissions from their tailpipes collects along streets in pollution pockets, which have elevated levels of ozone, particulate matter, and other pollutants. Pollution pockets occur routinely in the same locations and are detrimental to human health. Air Quality Monitoring for Smart Cities, a new report on air quality sensors by Guidehouse Insights, examines the market for Internet of Things devices made to monitor poor air areas.

But in some of the pollution pockets in the UK city of Leeds, something interesting happens. As soon as a municipally owned truck hits the edge of a pocket, the truck automatically changes how it operates. Its hybrid engine switches to electric only mode and eliminates its contribution to the pocket. Then, once out the other side, the truck switches back to more efficient hybrid operation. This use of data from one municipal system to automatically influence the behavior of other systems represents a step in the evolution of smart cities: smart interlaced systems (SIS).

SIS Secures and Streamlines Mobility

Some of the first phases into the era of the smart city relied on sensors to control specific and directly related municipal functions, like traffic flow sensors activating an on ramp metering system. These systems deserve to be called smart city projects, but they use data in limited, directly related ways. In an SIS framework, cities develop the potential of smart projects by interlacing them with other smart projects. A system in one part of the SIS can be directed to inform the behavior of another—even if they would not generally be directly connected. For example, air quality sensors were typically used to monitor and report pollutants. Now they are combined with geofencing to automatically change operation parameters of networked vehicles.

Elsewhere in the UK, the city council of Coventry is engaging with SIS by using air quality sensors to divert traffic away from areas with high air pollution levels. When local air quality is deemed too poor, messages are shown on electronic boards advising of conditions and suggest alternative routes to motorists and pedestrians. It’s not too difficult to imagine versions of this system where alternative routes are not merely suggested but enforced. The behavior of traffic infrastructure could be automatically altered to limit the amount of drivers on the polluted route.

SIS can also be seen in the use of geofencing to dictate mobility and micromobility patterns. Lyft launched a trial in 2018 using geofencing to limit pickups along the busiest corridors in San Francisco, California, where loading and unloading passengers presented safety concerns. Riders requesting pickup in these areas may be directed to a side street to a waiting car. The City of Baltimore, Maryland, partnered with micromobility companies to automatically limit the speed of micromobility vehicles in certain areas that are densely packed with pedestrians. Both cases interweave the technology to inform when an object has entered a certain area with digitally accessed mobility services. SIS broadly improves the efficiency and safety of mobility.

Smart Cities Use Data to Solve Problems

Projects like these represent one of the key promises of smart cities—improved function. They help create a city that can sense, think, and automatically react in complex ways. SIS moves beyond simply collecting data or using one data input for a defined function. It harnesses the ability of a smart city to use an array of sensors to implement frictionless solutions to complex urban issues.