Machine learning for next‐generation intelligent transportation systems: A survey
Tingting Yuan, Wilson Borba da Rocha Neto, Christian Esteve Rothenberg, Katia Obraczka, Chadi Barakat, Thierry Turletti
Abstract
Abstract Intelligent transportation systems, or ITS for short, includes a variety of services and applications such as road traffic management, traveler information systems, public transit system management, and autonomous vehicles, to name a few. ITS are expected to be an integral part of urban planning and future smart cities, contributing to improved road and traffic safety, transportation and transit efficiency, as well as to increased energy efficiency and reduced environmental pollution. On the other hand, ITS pose a variety of challenges due to its scalability and diverse quality‐of‐service needs, as well as the massive amounts of data it will generate. In this survey, we explore the use of machine learning (ML), which has recently gained significant traction, to enable ITS. We provide a thorough survey of the current state‐of‐the‐art of how ML technology has been applied to a broad range of ITS applications and services, such as cooperative driving and road hazard warning, and identify future directions for how ITS can further use and benefit from ML technology.