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Applications of Artificial Intelligence in Transport: An Overview
Open accessAbstract
The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.
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“ML algorithms can analyze customer data to personalize their experience. For example, by analyzing past trips, AI can suggest preferred routes or destinations to customers, reducing their travel time and improving their overall experience [134] .”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [134]
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“Most articles narrowly focused on ML (Abduljabbar et al., 2019)”
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Metadata
- Authors
- Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A.
- Affiliations
- Swinburne University of Technology (AU)
- Corresponding author
- Abduljabbar, Rusul
- Dia, Hussein
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2019-01-02
- Volume
- 11
- Issue
- 1
- Pages
- 189
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W2908162093
- Cited by
- 769
- References
- 138
- Citations by year
- 2026: 72 · 2025: 164 · 2024: 165 · 2023: 150 · 2022: 97 · 2021: 67 · 2020: 41 · 2019: 13
- FWCI
- 36.51
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sustainability (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Swinburne figshare (Swinburne University of Technology) (submittedVersion, cc-by)
- Swinburne figshare (Swinburne University of Technology)
- Funders
- Swinburne University of Technology
- Topics
- Social Sciences › Transportation › Traffic Prediction and Management Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Physical Sciences › Engineering › Civil and Structural Engineering › Road Transport and Highway Systems
- Keywords
- artificial intelligence in transport, traffic management, road safety, public transport, urban mobility, traffic congestion, CO2 emissions, artificial neural networks, genetic algorithm, simulated annealing, artificial immune systems, ant colony optimization, bee colony optimization, fuzzy logic, travel time reliability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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