Cited by 820Traffic Prediction and Management TechniquesTraffic control and managementRoad Transport and Highway Systems

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Applications of Artificial Intelligence in Transport: An Overview

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Abduljabbar, Rusul, Dia, Hussein, Liyanage, Sohani, Bagloee, Saeed Asadi·2019·Sustainability

DOI 10.3390/su11010189

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Abstract

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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Authors: Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A.
Affiliations: Swinburne University of Technology (AU)
Corresponding author: Abduljabbar, Rusul
Dia, Hussein
ORCID: Abduljabbar, Rusul
Dia, Hussein
Liyanage, Sohani
Bagloee, Saeed Asadi
Venue: Sustainability
Publisher: MDPI AG
Published: 2019-01-02
Volume: 11
Issue: 1
Pages: 189
ISSN: 2071-1050 (ISSN-L)
DOI: 10.3390/su11010189
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: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
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

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Applications of Artificial Intelligence in Transport: An Overview

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