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Machine Learning for Next-Generation Intelligent Transportation Systems: A Survey

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. It is expected that ITS will be an integral part of urban planning and future cities as it will contribute 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 poses 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 comprehensive 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 use and benefit from ML technology.
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Contributor : Thierry Turletti Connect in order to contact the contributor
Submitted on : Wednesday, November 24, 2021 - 6:30:54 PM
Last modification on : Tuesday, November 29, 2022 - 12:00:08 PM


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Tingting Yuan, Wilson Borba da Rocha Neto, Christian Esteve Rothenberg, Katia Obraczka, Chadi Barakat, et al.. Machine Learning for Next-Generation Intelligent Transportation Systems: A Survey. Transactions on emerging telecommunications technologies, In press, ⟨10.1002/ett.4427⟩. ⟨hal-02284820v3⟩



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