The DOI matches its Crossref record; 105 of the 105 references it cites were found in a registry or an index.
Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends
Abstract
Transportation systems operate in a domain that is anything but simple. Many exhibit both spatial and temporal characteristics, at varying scales, under varying conditions brought on by external sources such as social events, holidays, and the weather. Yet, modeling the interplay of factors, devising generalized representations, and subsequently using them to solve a particular problem can be a challenging task. These situations represent only a fraction of the difficulties faced by modern intelligent transportation systems (ITS). In this paper, we present a survey that highlights the role modeling techniques within the realm of deep learning have played within ITS. We focus on how practitioners have formulated problems to address these various challenges, and outline both architectural and problem-specific considerations used to develop solutions. We hope this survey can help to serve as a bridge between the machine learning and transportation communities, shedding light on new domains and considerations in the future.
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Applications of artificial intelligence and machine learning in smart cities
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Ahmad, K., Maabreh, M., Ghaly, M., Khan, K., Qadir, J., & Al-Fuqaha, A. (2022) · Computer Science Review · doi:10.1016/j.cosrev.2021.100452 · Cited by 291
How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
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Metadata
- Authors
- Veres, M., & Moussa, M.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2020-08
- Volume
- 21
- Issue
- 8
- Pages
- 3152-3168
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2963240573
- Cited by
- 394
- References
- 105
- FWCI
- 30.23
- Type
- journal article
- Language
- en
- Open access
- closed
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