Of the many ways artificial intelligence and machine learning are poised to improve modern life, the promise of impacting mass transit is significant. The world is much different compared with the early days of the pandemic, and people around the world are again leveraging mobility and transit systems for work, leisure and more. Across the U.S., traditional mass transit systems including buses, subways and personal vehicles have returned to struggling through gridlock, rider levels and congestion. However, advanced AI and machine learning solutions built on cloud-based platforms are being deployed to reduce these frustrations. Transportation is one of the most important areas in which modern AI provides a significant advantage over conventional algorithms used in traditional transit system technology.

Governments are already using data and analytics in a number of ways to help them become better informed and provide superior services for their citizens. For both central and local governments, an increasing number of back end processing and citizen engagement opportunities are emerging for smart use of artificial intelligence and its many subfields. The biggest area for potential quick wins will be the vast processing that occurs in various administration tasks. This includes improving awareness of patterns in data, to create new theses and models. Bringing together data from different areas and using algorithms that learn, can create new insights.

Fujitsu Laboratories Ltd. and Fujitsu Research and Development Center Co., Ltd. are developing new technology to analyze traffic video in order to provide real-time information on congestion, accidents and crime violations. Using machine learning and image processing, the technology analyzes the images from surveillance cameras installed along highways and streets, and groups characteristics that can lead to recognition errors, such as changes in lighting or environmental factors like nighttime and fog. The technology also analyzes moving objects, such as vehicles, bicycles and people, to identify accidents. A comparison of the previous traffic camera technology (left) and a sample of the application Fujitsu is testing (right). Source: Fujitsu The goal is to improve the way surveillance cameras can be used to improve traffic safety, reduce pollution and reduce congestion.