Authors :
Pwaviron Kennedy Gambiye; Emmanuel Nicholas Jesman; Aliyu Yazid
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/45U8C1n
DOI :
https://doi.org/10.5281/zenodo.8022504
Abstract :
Effective traffic management is essential for
addressing the critical problem of traffic congestion in
urban areas, and the development of accurate and
reliable traffic models plays an important role in this
process. Microscopic traffic models that simulate
individual vehicle behaviour have gained popularity in
recent years. However, the development of these models
can be challenging due to the complex interactions
between vehicles and the environment. In response,
artificial intelligence (AI) has emerged as a promising
approach to traffic modelling. This paper covers the
review of microscopic traffic models that use artificial
intelligence (AI) techniques, such as modeling based on
intelligent transport system, microscopic car-following
and lane-changing models, and driver behaviour models.
The review is divided into three sections, each section
discusses several papers that propose new models,
methodologies, or algorithms, and provides a brief
overview of their contributions, methodology, and
simulation results. Specifically, the paper discusses the
advantages of AI-based traffic models, such as their
ability to handle large datasets and complex traffic
scenarios, improved accuracy and increased efficiency.
Additionally, the paper highlights the limitations of AIbased traffic models, such as their reliance on the
accuracy of the data that is used in developing the
models, their computing intensity, and their inability to
completely replace human drivers. Overall, this review
highlights the potential of AI to revolutionize the field of
traffic modelling and offers insights into the challenges
and opportunities of AI-based traffic models.
Keywords :
Microscopic Traffic Models, Artificial Intelligence, Traffic Flow, Intelligent Transport Systems, Traffic Management.
Effective traffic management is essential for
addressing the critical problem of traffic congestion in
urban areas, and the development of accurate and
reliable traffic models plays an important role in this
process. Microscopic traffic models that simulate
individual vehicle behaviour have gained popularity in
recent years. However, the development of these models
can be challenging due to the complex interactions
between vehicles and the environment. In response,
artificial intelligence (AI) has emerged as a promising
approach to traffic modelling. This paper covers the
review of microscopic traffic models that use artificial
intelligence (AI) techniques, such as modeling based on
intelligent transport system, microscopic car-following
and lane-changing models, and driver behaviour models.
The review is divided into three sections, each section
discusses several papers that propose new models,
methodologies, or algorithms, and provides a brief
overview of their contributions, methodology, and
simulation results. Specifically, the paper discusses the
advantages of AI-based traffic models, such as their
ability to handle large datasets and complex traffic
scenarios, improved accuracy and increased efficiency.
Additionally, the paper highlights the limitations of AIbased traffic models, such as their reliance on the
accuracy of the data that is used in developing the
models, their computing intensity, and their inability to
completely replace human drivers. Overall, this review
highlights the potential of AI to revolutionize the field of
traffic modelling and offers insights into the challenges
and opportunities of AI-based traffic models.
Keywords :
Microscopic Traffic Models, Artificial Intelligence, Traffic Flow, Intelligent Transport Systems, Traffic Management.
