Artificial intelligence and machine learning have proven to improve the traffic in different cities around the world, both in terms of better traffic management but also in terms of better designed roads and infrastructure. We are looking at less congestion, fewer accidents and lower travel times.
We have now reached a technological paradigm where it has become feasible to use artificial intelligence to solve problems that simply are too difficult with traditional deterministic and statistical models. Today, we can use neural networks to process vast amounts of video data, not just for detection of simple objects but also for analyzing sequences of events. Something that was considered as impossible just a few years ago.
The number of sensors being in use for traffic analysis is growing fast and more and more information has become available to us. We have information from public transits, reporting of traffic crashes and incidents, road camera feeds, onboard diagnostics information from individual vehicles, navigation data, localized weather data, booking data from online booking apps such as taxi bookings, hotels, and other types of accommodation, and parking space occupation. To put it shortly, the monitoring capabilities of traffic have merely become better and better.
The challenge we are facing is how to aggregate these vast amounts of data into powerful models that can lead to a smarter regulation of traffic that is more adaptive to different traffic situations. With good models we can get more efficient traffic reallocation in regard to construction sites, different kinds of events such as concerts or events related to sports, or weather conditions or high traffic due to paycheck week.