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

The DOI matches its Crossref record; 140 of the 140 references it cites were found in a registry or an index.

Applications of Artificial Intelligence in Transport: An Overview

Open access
Abduljabbar, Rusul, Dia, Hussein, Liyanage, Sohani, Bagloee, Saeed Asadi·2019·Sustainability
DOI 10.3390/su11010189
Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/su11010189

Cites

140 of 140 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 138 references

Research works printed but not linked: 4

  1. Minsky, M., and Papert, S. (1969). Perceptron Expanded Edition, MIT Press.
  2. Parker, D. (1985). Learning Logic: Technical Report TR-87, Center for Computational Research in Economics and Management Science, The MIT Press.
  3. Lučić, D.B., and Teodorović, P. (2001, January 13–19). Bee system: Modeling combinatorial optimization transportation engineering problems by swarm intelligence. Proceedings of the TRISTAN IV Triennial Symposium on Transportation Analysis, Azores, Portugal.
  4. Waltz, 1997, Artificial Intelligence: Realizing the Ultimate Promises of Computing in Computing Research, AI Mag., 18, 49

Cited by

Sorted by citations

Items 401–500 of 820

Related

See all

Metadata

Authors
Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A.
Affiliations
Swinburne University of Technology (AU)
Corresponding author
  • Abduljabbar, Rusul
  • Dia, Hussein
ORCID
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
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 ›

Are you an author, or do you see a problem with this page? Request a review