Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

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.
Complete list of metadatas

https://hal.inria.fr/hal-02284820
Contributor : Thierry Turletti <>
Submitted on : Saturday, November 28, 2020 - 5:52:21 PM
Last modification on : Tuesday, December 1, 2020 - 3:45:25 AM

File

AI_ITS_Survey_V6__hal_.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02284820, version 2

Collections

Citation

Tingting Yuan, Wilson Borba da Rocha Neto, Christian Rothenberg, Katia Obraczka, Chadi Barakat, et al.. Machine Learning for Next-Generation Intelligent Transportation Systems: A Survey. 2020. ⟨hal-02284820v2⟩

Share

Metrics

Record views

55

Files downloads

355