Machine learning for next-generation intelligent transportation systems: A survey
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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.
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[50] focused on ML methods for ITS challenges, addressing vision-based perception, infrastructure, resource management, traffic flow prediction, vehicle/user behavior, and road occupancy, but did not address security and trust.
“In [50], the authors focused on the proposed ML method for ITS challenges. Security and trust were not addressed, but they addressed ML-based works from the point of view of vision-based perception, infrastructure, and resource management as well as the prediction of traffic flow, the behavior of vehicles and users, and the road occupancy of ITS.”
From Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions (2024) · cites this paper as [50]
Surveys [50,70,76] addressed ML-based trust evaluation in IoV focusing on the trust scheme.
“For example, the surveys in [50,70,76] addressed the use of ML-based trust evaluation in IoV focusing on the trust scheme.”
From Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions (2024) · cites this paper as [50]
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- Authors
- Yuan, T., Da Rocha Neto, W., Rothenberg, C. E., Obraczka, K., Barakat, C., & Turletti, T.
- Affiliations
- DIANA: Conception, Mise en oeuvre et Analyse d'Architectures Réseau (FR)
- University of Campinas Campinas Brazil
- Universidade Estadual de Campinas (UNICAMP) (BR)
- University of California, Santa Cruz (US)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Corresponding author
- Yuan, Tingting
- Venue
- Transactions on Emerging Telecommunications Technologies
- Publisher
- Wiley
- Published
- 2021-12-14
- Volume
- 33
- Issue
- 4
- ISSN
- 2161-3915 (ISSN-L)
- OpenAlex
- W2972381239
- Cited by
- 179
- References
- 238
- Citations by year
- 2026: 26 · 2025: 65 · 2024: 46 · 2023: 19 · 2022: 11 · 2021: 10 · 2020: 2
- FWCI
- 6.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Transactions on Emerging Telecommunications Technologies (publishedVersion, cc-by-nc-nd)
- HAL (submittedVersion, other-oa)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by-nc-nd)
- Funders
- Agence Nationale de la Recherche
- FAPESP
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Transportation Systems and Logistics
- Keywords
- intelligent transportation systems, machine learning applications, road traffic management, traveler information systems, public transport management, autonomous vehicles, cooperative driving, smart cities, road safety, transportation data
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