By Judith Hurwitz, President & CEO, Hurwitz and Associates In cities everywhere, traffic congestion leads to frustration, lost productivity, inefficiencies in commerce and delays in emergency responses.
Traffic is a challenge that has frustrated planners, administrators, and citizens for decades; progress, when it has been achieved, has been very limited. Now, however, a confluence of better and more affordable sensor technology, improved networking, and powerful real-time analytics and machine learning are putting major city improvements within reach. Cities can be defined as layers of complex systems that have to work in collaboration with each other to function successfully.
Computer models and day-to-day experience can provide some guidance for helping things to run smoothly, but only some. There has simply been too much complexity and too many variables. Even an individual vehicle – itself a complex system – may be subject to hard-to-predict failures that can upend the best planning and the most sophisticated attempts at traffic management.
In short, one flat tire can cause chaos across a metropolitan region. The key question is how do you move from the normal chaos of traffic to a controlled and managed environment? The answer lies in data and the evolution of machine learning and analytics. In recent years, the cost of sensors that measure everything from speed to temperature, location, and acceleration has decreased dramatically. In addition, sensor battery life has increased, making longer term monitoring of more things far more practical. And the networks that support communication between sensors and between sensors and gateway devices have become ever more robust. We are therefore seeing more and more sensors added to both vehicles and to urban infrastructure. These sensors are making it possible for planners and administrators to collect and analyze data both for the long term and in near real time to support minute-by-minute decision making.
This is completely changing operational approaches.
