AI for Traffic Analytics

Information and communications technology (ICT) is used extensively to better manage the city resources and improve the quality of life of its citizens. ICT spans many departments of the cities, from transportation, water, energy to building management and social-care services. AI techniques are getting more and more attraction from cities to represent and organize information, maintain sustainable networks, predict incidents, optimize distribution, diagnose faults, plan routes and organize their infrastructure. Managing traffic efficiently, among many other domains in cities, is one of the key issues in large cities. In this article we describe the domains of applications which could benefit from AI techniques, along with introducing the necessary background knowledge. Then we focus on traffic applications, which make use of recent AI research in knowledge representation, logic programming, machine learning and reasoning. Specifically we go through the next version of scalable AI driven traffic related application where (1) data from a variety of sources is collected, (2) knowledge about traffic, vehicles, citizens, events is represented and (ii) deductive and inductive reasoning is combined for diagnosing and predicting road traffic congestion. Based on these principles, a real-time, publicly available AI system named STAR-CITY was developed. We discuss the results of deploying STAR-CITY, and its related AI technologies in cities such as Dublin, Bologna, Miami, Rio and the lessons learned. We also discuss the future AI opportunities including scalability issues for large cities.